See the source image

What is Kickstart?

The Kickstart Scheme makes up part of the Government’s Plan for Jobs kills and employment programmes, which also include Apprenticeships, T Levels and Traineeships.

The Kickstart Scheme offers six-month jobs for young people aged 16 to 24 years old who are currently claiming Universal Credit and are at risk of long-term unemployment. Not only will you have a young person working for you, funded by the Government, you will also be supporting them to develop transferable skills that are aimed at increasing their chances of sustained employment.

Thousands of employers like you, across the private, public and not-for-profit sectors, have already applied for funding, which covers:

  • 100% of the National Minimum Wage (or the National Living Wage depending on the age of the participant) for 25 hours per week for a total of 6 months
  • associated employer National Insurance contributions
  • minimum automatic enrolment pension contributions
  • a grant of £1,500 per job to cover setup costs and employability support.                                                                                                                                                                                                                                                                              Find a kickstart gateway

    A Kickstart gateway can apply for a Kickstart Scheme grant for you. Some gateways also offer employability support and training to the young people you give jobs to.

    The Department for Work and Pensions (DWP) has checked the organizations listed in this service using the Cabinet Office Spotlight Tool. The list will be continually updated.

    Partnering with one of these organizations does not guarantee that you will get Kickstart Scheme funding. Your application will be assessed against the Kickstart Scheme criteria.

    Available To:

    The Kickstart Scheme is open to people that are aged 16-24 that are currently claiming Universal Credit and are at risk of long-term unemployment. If this sounds like you, talk to your Work Coach about Kickstart and see if it could be right for you.


    Six months


    England, Scotland, Wales

    Age Restrictions:



10 Tips on writing a successful CV

When it comes to applying for a new job, your CV could be just the ticket to get you that initial foot in the door and secure an interview – but how do you ensure your CV is added to the interview pile rather than thrown straight in the bin?

Putting together a successful CV is easy once you know how. It’s a case of taking all your skills and experience and tailoring them to the job you’re applying for. But what if you don’t meet the right criteria? Well, I’ve put together the following tips to help you get started in creating a successful CV and securing your first (or next) arts job.

Get the basics right

There is no right or wrong way to write a CV but there are some common sections you should cover. These include: personal and contact information; education and qualifications; work history and/or experience; relevant skills to the job in question; own interests, achievements or hobbies; and some references.

Presentation is key

A successful CV is always carefully and clearly presented, and printed on clean, crisp white paper. The layout should always be clean and well structured and CVs should never be crumpled or folded, so use an A4 envelope to post your applications.

Always remember the CV hotspot – the upper middle area of the first page is where the recruiter’s eye will naturally fall, so make sure you include your most important information there.

Stick to no more than two pages of A4

A good CV is clear, concise and makes every point necessary without waffling. You don’t need pages and pages of paper – you just keep things short and sweet. A CV is a reassurance to a potential employer, it’s a chance to tick the right boxes. And if everything is satisfied, there’s a better chance of a job interview. Also, employers receive dozens of CVs all the time so it’s unlikely they’ll read each one cover to cover. Most will make a judgment about a CV within sections, so stick to a maximum of two pages of A4 paper.

Understand the job description

The clues are in the job application, so read the details from start to finish. Take notes and create bullet points, highlighting everything you can satisfy and all the bits you can’t. With the areas where you’re lacking, fill in the blanks by adapting the skills you do have. For example, if the job in question requires someone with sales experience, there’s nothing stopping you from using any retail work you’ve undertaken – even if it was something to help pay the bills through university. It will demonstrate the skills you do have and show how they’re transferable.

Tailor the CV to the role

When you’ve established what the job entails and how you can match each requirement, create a CV specifically for that role. Remember, there is no such thing as a generic CV. Every CV you send to a potential employee should be tailored to that role so don’t be lazy and hope that a general CV will work because it won’t.

Create a unique CV for every job you apply for. You don’t have to re-write the whole thing, just adapt the details so they’re relevant.

Making the most of skills

Under the skills section of your CV don’t forget to mention key skills that can help you to stand out from the crowd. These could include: communication skills; computer skills; team working; problem solving or even speaking a foreign language. Skills can come out of the most unlikely places, so really think about what you’ve done to grow your own skills, even if you take examples from being in a local sports team or joining a voluntary group – it’s all relevant.

Making the most of interests

Under interests, highlight the things that show off skills you’ve gained and employers look for. Describe any examples of positions of responsibility, working in a team or anything that shows you can use your own initiative. For example, if you ran your university’s newspaper or if you started a weekend league football team that became a success.

Include anything that shows how diverse, interested and skilled you are. Don’t include passive interests like watching TV, solitary hobbies that can be perceived as you lacking in people skills. Make yourself sound really interesting.

Making the most of experience

Use assertive and positive language under the work history and experience sections, such as “developed”, “organised” or “achieved”. Try to relate the skills you have learned to the job role you’re applying for. For example: “The work experience involved working in a team,” or “This position involved planning, organisation and leadership as I was responsible for a team of people”.

Really get to grips with the valuable skills and experience you have gained from past work positions, even if it was just working in a restaurant – every little helps.

Including references

References should be from someone who has employed you in the past and can vouch for your skills and experience. If you’ve never worked before you’re OK to use a teacher or tutor as a referee. Try to include two if you can.

Keep your CV updated

It’s crucial to review your CV on a regular basis and add any new skills or experience that’s missing. For example, if you’ve just done some volunteering or worked on a new project, make sure they’re on there – potential employers are always impressed with candidates who go the extra mile to boost their own skills and experience.


10 ways to Prioritise your workload

Working efficiently is important for any business but getting snowed under is a too-familiar situation. A well-structured workload is key to good time management and will increase your productivity.

Find out how to prioritise tasks.

  1. The to-do list. Don’t keep it on different post-it notes or in your head — at the beginning of each day or week, write on a sheet of paper what you want to get done and by when. Rank tasks according to importance or urgency to plan your day and focus your mind
  2. Review your workload regularly. Is there one task that always ends up at the bottom of the pile? If you find you’re avoiding it, can somebody else do it? Consider delegating whole projects that you don’t need to be involved in or allocate a specific time when you only do your admin, for example.
  3. Remember the 80:20 rule of workloads. It’s very simple — 80 per cent of our work contributes to less than 20 per cent of its value. Concentrate on the most crucial 20 per cent of your workload, because performance would still be strong.
  4. Set realistic deadlines for your tasks. Look at your to-do list and estimate the time each task needs to be completed but don’t be overoptimistic. Be honest of what you can achieve in a working day or week so that you don’t feel overwhelmed from the start.
  5. Allow time for interruptions. If you need to finish a certain task at a certain time, only deal with urgent queries during this time. You can then quickly pick up again where you left off.
  6. Structure your workload. Avoid picking up a job, doing a bit and then putting it back on the pile. Deal with them one at a time and finish each one before starting another. Your mind will be clear and ready for the next one.
  7. Don’t let your inbox drive your workload. If you get 50 mails per day, this means 50 interruptions to your day. Don’t check your inbox every time a message arrives. Switch off instant alerts if necessary and allocate a time when you will check your inbox.
  8. Fun, fun, fun. Ticking items off your to-do list is great, but are you concentrating on the quick-and-easy ones? Tackling more challenging projects first might mean more time, but also that a major task is completed and a weight off your shoulders.
  9. Keep multitasking to a minimum. Starting a number of jobs simultaneously means most of them won’t get your undivided attention. Think of multitasking as dealing with more than one task during a day, not at the same time. That way you focus on the project in hand.
  10. Keep a log of your workload. If you’re unsure how long things take, how often your focus shifts or how many times you get interrupted, keep a log of your working week. This will help you plan your week in future.

How to dress up for an Interview

Smart attire at interview is the first step towards making a lasting impression.

It’s well known that interviewers have pretty much made up their minds on whether you’re right for the job within a few minutes of meeting you. A large part of that first impression is based on what they actually see.

“At interview, I want to see clothes that show a candidate is professional, organised and focused,” says Laura Wynne, primary school headteacher. It’s also important to visit the school or look at the website to get a sense of the school’s ethos and dress accordingly: “Different schools have different styles. It’s important that your body language and appearance say: ‘I’ll fit in here’,” she adds.

John Howson, TES careers expert, agrees and adds that personality should come across in your attire, too: “Try to match professionalism with personality: neither overpowering nor boring. Recruiters can detect the person who understands teaching through his or her choice of appropriate, smart clothing, he says,” he says.

Jayne Halliwell, primary school headteacher feels that getting the right message across through well-chosen clothes is crucial. “If candidates arrive for interview looking casual, they leave me wondering if they would be just as sloppy in the classroom,” she says. “They must have a sense of occasion and this should be reflected by smart clothes such as suits, elegant blouses and shirts.”

Follow these guidelines to ensure that your outfit is working for and not against you:

  • Think conservative and wear a well-fitted suit of a solid, dark colour. It may sound boring, but interviewers want to see you looking professional. If the price of a suit is out of your budget range, look out for sales and use the internet to find the best bargains. Remember though, that the suit must fit you well, so try it on and make sure it complements your body shape.
  • If you have decided to splash out on a classic interview suit, remember to remove all the labels. The interviewers’ last memory of you shouldn’t be marred by a white tag hanging out from your jacket collar.
  • Comfort is crucial, so make sure that you can move around with ease. Check that you can sit down and stand up comfortably at all times.
  • Add individuality to an otherwise plain appearance by choosing a blouse/shirt that expresses your personality. An important rule to remember is not to go over the top. You might describe yourself as outgoing and bubbly, and a glittery top might just reflect that, but it won’t look right at interview.
  • Interviewers want to focus on what you are saying, not on the unusual earrings dangling from your ears, so keep accessories to the minimum.
  • By all means wear a light scent, but don’t douse yourself in perfume as this will be overpowering in a small interview room.
  • Choose a hair cut that flatters your face, or if you want to keep your hair long, wear it up or back for a professional clean look.
  • Men should definitely save gimmicky socks and wacky ties for another occasion. Aim for elegance and choose neutral/complementary colours for socks and ties.
  • Women, use make-up in moderation. Avoid hot red lips and heavy eye-liner; visible tattoos are a no-no.

Having listed all of these rules, there are exceptions and no accounting for taste. “I went to an interview recently and another candidate came in behind me. She was wearing jeans, flip flops, green nail varnish and around 100 bangles up her arm,” says one TES forum user. “She got the job.”th

How to write a successful CV

Employers receive an average of 60 applicants for every advertisement for a low-skilled job, and 20 for every skilled job.

Significantly, almost half of these candidates are perfectly suitable for the role, according to research by the Chartered Institute of Personnel and Development (CIPD)

So that makes their CV – or curriculum vitae – all the more important when attempting to stand out from the crowd.

Experts say there are some golden rules for getting a CV correct, not least accuracy, spelling and grammar.

Don’t repeat the mistakes, they say, of a lawyer who stressed his “dew diligence”, or the applicant who ignored commas when describing his interests as “cooking dogs and interesting people”.

Key points

If sending a CV as a hard copy, along with a job application, then it needs to be neat and typed if possible. Most libraries have public computers which can be used by those who do not have their own.

Increasingly, applicants are asked to send a digital copy of a CV. If this is the case then the first set of “eyes” to see it might be an automated search for key words, so experts suggest applicants ensure mandatory requirements in the job advert are included in a CV.

Corinne Mills, managing director of Personal Career Management, which provides career coaching, says that digital CVs should be in a simple format and font so readability is not affected on different screens.

Other tips from Mrs Mills, the CIPD, and the National Careers Service include:

  • Tailor a CV to a specific job – it is vital to ensure the script is relevant to each job application, rather than sending the same generic CV
  • Keep it simple – it should be easy to read and use active language. Two pages of A4 is enough with a mini profile included in the first half page
  • Include key information – personal details, including name, address, phone number, email address and any professional social media presence should be clear. A date of birth is no longer needed, owing to age discrimination rules. A photo is only essential for jobs such as acting and modelling, otherwise it is a matter of choice
  • Showcase achievements – offer evidence of how targets were exceeded and ideas created, but always be honest
  • Check and double check – avoid sloppy errors, take a fresh look the next day and ask for a second opinion from a trusted friend or colleague



I’m unsure about which career direction to take

ea9c0e33-8f89-46d4-852a-74177fd8af24-620x372Twice a week we publish problems that will feature in a forthcoming Dear Jeremy advice column in the Saturday Guardian so that readers can offer their own advice and suggestions. We then print the best of your comments alongside Jeremy’s own insights. Here is the latest dilemma – what are your thoughts?

I’ve worked in the voluntary/public sector since graduating in 2007. I’m currently a social worker in mental health, and over the years have learned what’s important to me about where I work – work/life balance (flexitime and not bringing work home with me); competent, friendly colleagues; good management and structure; clear processes; good team dynamics; a professional, pleasant and aesthetically pleasing environment. Not too much to ask for surely!

I’m fed up with the ongoing challenges – we have to pay for parking permits although we have to go out on home visits, and yet aren’t guaranteed a parking space; no therapeutic resources are provided, so I buy them myself; IT and equipment regularly break; loo paper runs out in the toilets for visitors; the building needs demolishing. I could go on …

I’ve been keeping my eye out for jobs over the past year or so and have applied for a couple unsuccessfully.

I’d appreciate some ideas of how to gain a better view of what I want to do, and what would be feasible. I like the idea of retraining but can’t really afford it and have invested a lot of time and my own finances in my current career.



World Cancer Day 2015: One in two British people will be diagnosed with the disease at some point in their lives

One in two people will develop a cancer at some point in their lives, experts now estimate.

Previous calculations that indicated cancer will affect just over one in three people were underestimating the scale of the disease, according to a new analysis by Cancer Research UK.

However, because of advances in treatment and early detection, more people are now surviving cancer.

Two-thirds of the increase in risk can be attributed to the fact we are now living longer, and cancer is a disease that becomes more likely the older we get. The additional third is down to changes in lifestyle, CRUK said. The study calculates the lifetime risk of cancer for men born in the UK in 1960 is 53.5 per cent and for women 47.55 per cent, averaging at 50.5 per cent. The risk is likely to increase for people born after 1960, and CRUK said it was confident in predicting that this meant at least half the population can now expect to get cancer.

“Cancer is primarily a disease of old age, with more than 60 per cent of all cases diagnosed in people aged over 65,” said cancer specialist Professor Peter Sasieni, who led the new study. “If people live long enough then most will get cancer at some point.

“But there’s a lot we can do to make it less likely – like giving up smoking, being more active, drinking less alcohol and maintaining a healthy weight.” The new calculation does not mean that each individual in the UK has 50/50 chance of getting cancer, as risk varies significantly according to age, weight, diet, as well as a range of lifestyle, genetic and environmental factors.

Recent estimates suggest that half of people who get cancer now survive the disease for 10 years or more – so it is projected that despite more cases, the number of deaths attributable to cancer will remain stable at around one in four.

Changes in lifestyle that have contributed to the increase in cancer risk include an increase in obesity, which is linked to a number of cancers and is projected to continue rising. Higher consumption of red and processed meats is also linked to a rise in bowel cancer. Other factors include an increase in the culture of using sunbeds and sunbathing, which has increased incidence of skin cancer, while women having babies later and breastfeeding less is also raising their chance of developing breast cancer. As well as this, more cancers are being detected by screening programmes.

Survival rates are improving as more cancers are detected earlySurvival rates are improving as more cancers are detected early (PA)

CRUK’s chief executive Harpal Kumar, said that the NHS faced a challenge to ensure it was “fit to cope” with the increase in cases. “If the NHS doesn’t act and invest now, we will face a crisis in the future – with outcomes from cancer going backwards,” he said. Cancer services are already coming under increased strain, with the NHS in England having missed key waiting time targets for cancer patients for the first time last year.

Responding to CRUK’s study, which is published in the British Journal of Cancer, Macmillan Cancer Support said that the rise in cases would present “a Herculean challenge for the NHS and for society”.

“With cracks already beginning to show, the NHS will soon be unable to cope with the huge increase in demand for services,” said the charity’s spokeswoman Ellie Rose.

A Department of Health spokeswoman said: “Cancer survival rates are now at their highest level, and we are on track to save an extra 12,000 lives this year. But cases of cancer are likely to rise with an ageing population – so we are focused on earlier diagnosis, improving care and tackling preventable cancer.

“We have spent an extra £750m on cancer services and have given local authorities £8.2bn over three years to tackle public health issues, such as smoking, obesity and alcohol abuse.


Ref: –

Don’t do THIS in a job interview! Recruitment expert reveals the funniest fails

HALF-NAKED men to applicants bringing their mums: as record numbers of Brits apply for new jobs on Massive Monday, recruitment expert James Reed reveals the biggest job interview disasters

a naked man at an interview

Disappointingly, undressing for success rarely works in a formal interview.

We’ve had Black Friday, now get ready for Massive Monday.The first Monday of the New Year (for this year it’s Monday, January 5) traditionally sees an enormous surge in enquiries and applications for new jobs.We all think we know the basic dos and don’ts of interview preparation and etiquette and yet some people seem to have missed that memo.

James Reed is the chairman of the Reed group of companies which includes Reed Specialist Recruitment and the recruitment website

On Massive Monday last year the website received over half a million enquiries and 180,000 job applications and James expects applications to top 200,000 this year.

It’s fair to say that he’s seen and heard it all.

Yet even he was surprised by some of the shocking anecdotes he heard when he was researching his latest book, Why You? 101 Interview Questions You’ll Never Fear Again.

From inappropriate states of dress (and undress) to overzealous spousal and family support, James reveals some of the biggest blunders people have made when applying for jobs.

And yes, somebody really did turn up with saucepan lids on their person…



Simon Cowell wearing a saucepan hat on Britain's Got Talent

Recipe for success: Simon Cowell wearing a saucepan hat on Britain’s Got Talent

1. Undressed for success? Everyone knows the old adage ‘dress for success’ and job interviews must rank up there with weddings and court appearances (often soon after weddings) as the time to put your best foot forward.As in so many crucial social interactions, in an interview you will be judged instantly on your appearance before a word has been spoken.

One candidate (thankfully a male) turned up for an important interview wearing simply jeans and no top.

He might have lost his shirt in this difficult economy but there is no excuse for shoddy appearances.

Unless, of course, he was applying for a postion as a go-go dancer.


2. Recipe for disaster:

By all mean throw everything but the kitchen sink into your interview preparation and execution.

However, maybe you should leave any actual kitchen implements at home.

One candidate arrived at a job interview with saucepan lids stitched into the lining of their coat.

When asked the reason for this, they explained that it was “for protection” (yes, we’re confused too!).

Quite possibly it was some form of extreme preventative measure to combat mobile phone radiation.

However, the only thing it protected them against on that particular day was getting a job.

woman trying on lots of shoes

Footwear should remain firmly in place at all times during an interview

3. Keep your shoes on!Don’t just keep your hat on in stressful interview situations – please also try and keep footwear firmly in place.Perhaps concerned that her shoes weren’t sealing the deal, one female candidate actually offered to change them half-way through questioning.

She may well have thought that she was demonstrating initiative or resourcefulness as she halted proceedings and took out the entire contents of her bag to show the extra shoes she had brought.

In return, the only thing she was shown was the door.

man skyping in bed

Dress appropriately for a skype interview!

3. The perils of skype:One of the pleasures of working from home is that you can wear whatever you like.

Some people find it still helps to dress as if they were going to an office, but others find wearing casual clothes or even staying in their sleepwear helps release their creativity.

This is perfectly fine in general and poses no problems whenever you have to conduct business over the phone.

But please remember that the point of skype is that both parties can see each other.

Perhaps this did not occur to the unfortunate gentleman who conducted his entire interview by skype – dressed in his pyjamas.

From the dreadful first impression that this inevitably created, it’s safe to say that he probably shouldn’t have bothered to get out of bed.

main with crazy hair

Sorry, sensible haircuts are usually best for a job interview.

4. Avoid comedy hair (everywhere):Amusing, extravagant or experimental hair styles should probably be avoided when you go for an interview.

The same applies for novelty facial topiary.

And yet, one colleague remembers a candidate turning up with only one eyebrow.


6. Fishing for compliments…It is possible (and possibly quite productive) to draw comparisons between job hunting and a fishing expedition.You need to bait your hook, wait for the right opportunity and reel it in.

See, we would have landed that job interview for a metaphor expert hook, line and sinker.

Unfortunately, this was not the position on offer – nor was it a job at a tackle shop – when one gentleman turned up wearing a fishing hat full of bait.

He may have been angling for success, but the whole thing was slightly fishy.

a family interview

It’s probably best not to bring spouses and children to your interview!

7. Leave mums, spouses, BFFs and cheerleaders at home!Yes, you gotta have friends – just not at an interview.A shocking number of recruitment agents reported stories of candidates turning up with support teams in tow.

The majority brought along a best friend, but others came with extended members of the family (including, but not limited to, aunts and uncles).

One applicant brought their mum but left her in the lobby.

James Reed Why You 101 interview questions you’ll never fear again
8. Confident, articulate and capable:It really should be no surprise that these are three of the main things a potential employer would like you to be.None of these criteria were met by the unassertive man who took his wife into an interview with him – where, unsurprisingly, she proceeded to answer most of the questions on his behalf!

Hopefully he learned from that absolutely disastrous experience – while we learned who wears the trousers in that relationship.



Job hunting is a matter of Big Data, not how you perform at an interview

How do we end up in the jobs we end up in? And why did we miss those opportunities we had set our hearts on? If most of us look back, the reality is likely to be as fraught with chance as any other aspect of our biography. Our working lives are essentially fictive constructs, born out of the fantasy and chemistry of CV and interview, the lucky break or wrong call, the age-old laws of square pegs and round holes, or, just occasionally, of “perfect fit”.

Where such randomness exists now, of course, “big data” – that amalgam of all that we and our fellow digital citizens do online, the gigabyte human traces we bequeath second by second to machines – is certain to follow.

None of us would like to think of our essential self – our talents and skills, traits and quirks, education and experience, those all-important extracurricular passions and hobbies – as being reducible to a series of data points, a set of numbers and correlations. But what if such information could help us find our perfect workplace, our ideal match?

One man trying to bring data to bear on our careers is Alistair Shepherd, an engineering graduate of Southampton University. In 2009 he won a place at Harvard Business School where he planned to develop an idea for a company based on a wave-power innovation he had created. But he was diverted by two things that his professor, Noam Wasserman, said to him. Wasserman is author of The Founder’s Dilemmas and a guru of the reasons businesses go wrong.

Big data
Entrepreneur using mobile phone in creative office space. Photograph: Hero Images/Getty Images/Hero Images
He told Shepherd that 83% of all new companies failed and that the evidence showed the primary reason for failure in two-thirds of those cases was not the quality of the business idea or lack of funding. It was a failure in the personal business relationship of the company’s founders. Shepherd decided he would abandon his engineering project and attempt some social engineering instead.


He is explaining some of this in his shared office on a floor of the Google incubator campus just off “Silicon Roundabout” at the junction of City Road and Old Street in London. The business he is trying to incubate is called Saberr – tagline: “Optimise your workforce” – and it is one of the more interesting entrants into the increasingly crowded field of “people analytics”, the attempt to apply big data to human performance, in an effort to optimise productivity, success (and happiness) in the workplace. Shepherd speaks with something of the infectious excitement of the early adopter.

The social science of human interaction at work that Shepherd discovered at Harvard, he suggests, was a minefield of competing psychological models, mostly from the middle of the last century. As a would-be MBA, he was already familiar, for example, with Myers-Briggs (the model, based on the work of Carl Jung, that was initially developed by Katharine Briggs and Isabel Myers to assign appropriate jobs to women entering the workforce during the second world war) as well as the more evidence-based theory of the components of an ideal team researched from observation by Meredith Belbin at Henley College in the 1970s. Neither they, nor their later variations, however, had ever proved particularly useful in predicting real-world business success.

Shepherd looked for a significant source of data on what might make successful teams. “Online dating,” he says, “seemed to me a great place to start. It provides a digital record on a very large scale that answers a very simple question: which two people will have a successful relationship?”

He spent months digging through what research he could access of the characteristics of the most successful online matches (which he defined as when both parties committed to cancelling their online dating accounts, settling for what they had). Using the kind of questionnaire that helped to make those matches in the first place – “Do you like horror movies?”, “Would you have sex on a first date?” – stripping out as much romance as possible, and combining it with the latest academic thinking in behavioural science, he worked out a rudimentary algorithm for what might spark a successful business relationship.

All he then needed was a forum in which to test his theory, culled from the data, that a measurable “resonance” of shared core values, rather than the grouping of any particular personality types, was the key driver of the most creative partnerships and teams – and that such a resonance could be predicted using analysis based on his blind online questionnaire.

The first place he tried was at a business competition called the Bristol Spark, a week-long competition for young entrepreneurs in which there were eight teams of eight people. The people in the teams had never met before. The idea was to come together to produce business ideas by the end of the week and present them before a panel of investors. Several thousand pounds were at stake.

Before the competition started, Shepherd got permission to have everyone answer the 25 online questions he had formulated from the dating research. Then he was told who was in each team, so worked out their “resonance” and, based on his “pretty scrappy algorithm”, ranked the teams in order one to eight, presented the results to the judges in a sealed envelope and asked them to open it after they announced their decision.

Shepherd never met any of the people involved. He had no knowledge of their skills, experience, education, demographic or, crucially, what ideas they were proposing. He had nothing but their answers to questions such as, “Do spelling mistakes annoy you a great deal or just somewhat?” It turned out he was correct not only in predicting which team won, but also the exact ranking of all the other teams.

He has refined his formula andrepeated the sealed envelope exercise many times at innovation competitions. “The longest we did was the Microsoft Imagine Cup,” he says, “which is an eight-month student development competition for the Windows platform. We have a greater than 95% accuracy in predicting the ranking of teams.” Last September, he did his trick at the London Seedcamp competition, which won Saberr its office space at this Google campus, among other things, and the chance to see if it could make money from data-driven clairvoyance.

Shepherd is at the very beginning of this commercial experiment, in the process of persuading companies to let him identify their high performers and engineer their most successful teams. He pleads innocence to the idea that he might just have created a monster – another way for companies to justify with data their “up or out” philosophies – and is some way from making it pay, but his ambition is to feed the growing appetite to apply quantitative rigour to some of the more intangible aspects of human potential.

The advance of algorithms into recruitment and “talent management” – workplace Moneyball – is the latest encroachment of the power of big data in areas traditionally policed, for better and worse, with a large element of human instinct and subjective decision-making. If you trace its history, you get back to Daniel Kahneman, the Nobel prize-winning psychologist, who, as he documented in his game-changing book, Thinking Fast and Slow, was tasked at 21 with the job of organising recruitment for the Israeli defence force.

He proved that numerical analysis of a simple psychological questionnaire on its own was far more efficient in predicting successful candidates than the interview-based judgment of expert senior officers (for which, wonderfully, the correlation between actual and predicted performance of recruits was precisely zero).

Digital applications of that insight have the advantage of huge data resources and programmable machine intelligence. They also have all of the dangers – to privacy, notion of the individual, to long fought-for rights in the workplace and beyond – that blind number-faith in arenas of complex human activity always involves. Any employee knows there are few more chilling sights than time-and-motion operatives with clipboards. As our work and our career path moves online, those anonymous monitors of our progress and productivity (or its perceived lack) are more and more likely to be embedded and alert to our every interaction.

Proponents of big data invite us to think of the information gained from such insight as value neutral. They argue, with the persuasive clarity of the digitally born-again, that it will offer certainty in spheres of doubt. And what more dubiously scientific process is there than that of job recruitment?

Lauren Rivera, a sociologist at Northwestern University in America, spent three years from 2006 studying the recruitment practices of global investment banks, management consultancies and law firms, which spent millions of dollars on apparently objective processes to secure “top talent”, and concluded that among the most crucial factors in decision-making were “shared leisure interests”. Squash players hired squash players. Golfers chose golfers. “Assessors purposefully used their own experiences as models of merit,” she concluded.

Listening to Shepherd talk about “cultural resonance”, I wonder how his algorithm would counter such biases?

“To achieve that shared spark,” he says, “you use the data to maximise behavioural diversity, but to minimise value diversity.” His interest is in the alignment of values rather than the values themselves. “When you see companies with the words ‘integrity’ or ‘trust’ written on the wall in big type you know straight away that’s a load of nonsense, because how do you measure it? We all say we value trust and freedom but do we, say, value trust more than freedom? It is at those points that the data begins to let you see those fundamental values that allow very different kinds of people to work very successfully together.” Shepherd is evangelical about the possibilities. “If you think of your workforce as a machine that delivers all your goals, then for you not to pay mathematical attention to how best it fits together is madness.”

That perceived attention deficit is being filled at a rapid rate. The science and the pseudo-science of business performance originated in the post-Henry Ford world of US commerce and it is there that most of the analytics pioneers are pimping their algorithms. Ever since there have been corporations and companies, leaders and managers have been invited to think of their employees – arguably always to the detriment of the latter – as numbers in a system.

The complexity and sheer volume of those numbers is expanding exponentially. Max Simkoff is the co-founder of a fast-growing Silicon Valley company called Evolv whose website keeps a ticker of the number of data points its computers are assessing (489,562,868 as I write and rising by the second).

Evolv – tagline: “The first predictive analytics app to proactively manage your employee workforce” – was created in response to a particular problem. Simkoff and his business partner worked for a small-scale private health company employing the majority of its workers on an hourly rate. The biggest and most expensive challenge the company faced was the fact that on average those entry-level staff stayed less than a year. Simkoff assumed there must be software packages available that analysed employee data and helped employers discover which attributes made the people they hired more likely to stay in the job.

In 2006, he discovered there wasn’t, really. “People were relying on intuition; looking at how many jobs someone had had, for example, and trying to decipher from that whether a person would be a productive and long-term employee,” he says, with an analyst’s disdain for gut instinct.

Little was being measured, so Evolv set about measuring whatever it could. Its customers mostly supply internal company data daily: how many interactions each employee has had, whether they have sold anything, and so on. This is added to information about how long each individual has been at the company, how they have performed, who their managers have been, any pay increases, subjective appraisal and engagement scores, along with “ongoing macro-economic data on everything from regional labour markets to median home prices”.

And then, Simkoff explains, with the satisfaction of the statistician: “Every single night our machine learning engine runs hundreds of millions of reports to look at individual differences even within the same company – some explainable by cultural differences, some by regional differences.”

By morning, he says: “If a customer has thousands of people in similar job types, our system can predict accurately on a given day which individuals are most likely to quit.” In response, Evolv then offers employers “what-if types of analysis” by which if they change certain incentives – a bonus, training scheme, change in environment – they can see exactly what effect it is likely to have on a particular person’s behaviour. In this way Evolv advertises average reduced employee attrition rates among its clients, who include one fifth of Fortune 100 companies, of up to 15%.

As well as apparently inveigling its way into all sorts of (anonymised) aspects of individual employee behaviour, Evolv data undermines certain truisms, among them the idea of the serial job-hopper. “The number of jobs you have previously had,” Simkoff says, “and even whether you’re employed or not at the time of application, has zero statistical correlation with how successful you will be or how long you will stay.” The statistics also show, he suggests, counter-intuitive correlations, including evidence that former prison inmates are among the most reliable employees.

Evolv has not attempted to use its model in professional salaried jobs because, Simkoff says, the data is less reliable as there are rarely hard and fast productivity measures. “Things get squishier in the professional end,” he suggests. “But two-thirds of all jobs are entry level, so there is no shortage of opportunity…”

One of the great challenges facing recruiters and job seekers at that squishier end of the market is the vast increase in the number of applications for positions in the few years since the process became digital. Steve Weston, group technology director at the global recruitment firm Hays, suggests that the number of applications per job has risen tenfold in the past five years. Hays sees 30,000 new CVs uploaded every day. Its algorithmic search engine can filter its 250m CV database in less than a second. The machine can learn precise keyword matches for education and experience; it can give precise location matches for individuals; but it struggles to find that Holy Grail of job qualification: culture fit. Weston suggests that is the “10 terabyte” question facing recruiters around the world; one solution, particularly for the new generation of job seekers might lie in “gamification”.

Among many companies attempting that particular quest (including Saberr) is another American startup, Knack, which this month established an office in London. Knack – tagline: “We’re reimagining how people discover their potential, from education to work to innovation” – uses immersive online video games to collect data to quantify how creative, cautious, flexible and curious, etc, potential job applicants are, and offers thereby to funnel tens of thousands of applicants for clients. Its Wasabi Waiter game, for example, casts the applicant on service at a sushi restaurant with multiple demands on his time and many orders to fill.

Knack began when its Israeli founder, Guy Halfteck, failed to land a job with a large New York hedge fund after an interview process lasting six months of interviews and tests based on the common fallacy that the more information human decision-makers have, the better judgments they can make. Beyond a certain point, the information just becomes noise, Halfteck argues, with a data-driven weight of evidence on his side. He looked for a different model, one that could “transcend resumés, transcend interviews, transcend what people say about themselves and cut to some data that is actually credible …”

Wasabi Waiter is derived from economic game theory and because it aims to reveal how people behave and make decisions in real time “is not about what you say you do, but what you do”. The data views the game as a stream of micro-decisions and micro-behaviours and micro problem-solving. “We collect thousands of data points in milliseconds,” Halfteck claims, in the way that Big Data believers tend to. Wasabi Waiter was developed by his (handpicked) Ivy League team of neuroscientists, data experts, researchers in artificial intelligence and gaming engineers.

Wasabi waiter
The game Wasabi Waiter was developed by an Ivy Leave team of data experts and neuroscientists.

“When you compare our data to, say, an interaction between a hiring manager and a candidate, it is orders of magnitude greater,” Halfteck says. He has had much success in persuading companies such as Shell that his culture-fit games, and the algorithm that matches individuals to particular roles and particular organisations, are predictors of the future.

Along with all the obvious caveats that such an innovation might require – not least the fact that the long-term efficacy of its model has yet to be tested – one potential advantage of using games is that, unlike in written or IQ tests, language and culture barriers are largely removed. Of the argument that the games favour game-playing “millennials”, Halfteck can point to his inhouse research that suggests age has no impact at all on outcomes.

“To explain that, if you look at Flappy Birds or whatever, it is a very broad demographic that successfully plays those games from five-year-olds to people in their 80s.” What about that significant element of the population that has never held a games console? What about those who are more stressed by the thought of computers than they are by written tests? Halfteck is predictably adamant that it does not replace one barrier to entry with another.

The premise of Knack games is that “the way you do something is the way you do everything”. It claims to be a method of extending and smartly filtering the pool of talent from which companies can draw, by measuring intangibles not covered by paper qualifications.

“At the moment, at the top end of the market companies are competing for people from this very small competitive pool of people from Oxford or Harvard or whatever,” Halfteck says. “We are suggesting a way that they can find others who have great or greater potential from way beyond that pool.” Steve Jobs is often presented as the case in point. As a college dropout, the Apple founder would never have received an interview with most blue-chip firms.

If the counter arguments are also quickly clear – why trust the evidence of a game over the centuries-old grind of academic pass and fail – Halfteck is not alone in envisaging a future in which “gamification” becomes the norm in applications of all kinds. “This year we will start living side by side with standard tests in many schools and colleges and universities in the States,” he says. “Traditional scores only look at written test abilities. They do not begin to measure the factors likely to have most bearing on your success: social skills or personality traits – how you deal with stress, how you collaborate with other people, how much you listen…”

The next logical step in such a philosophy is the extension of such “gamification” to all aspects of work and life. If this prospect sounds alarming – as alarming perhaps as the knowledge that governments and corporations have long been collecting the data of all our private moves on the internet and applying their algorithms accordingly – then that is already upon us. Closely monitoring and publicly sharing one’s health information is part of a growing trend of “the quantified self” movement; motto: “Self-knowledge through numbers.”

It stems from the belief that the examined life is now made from data points: blood pressure, heart rate, food consumption, hours of sleep, quality of exercise, as well as the nature and range of our real and social media interactions, add up the precise data-set of who we are. Or so the belief goes. That philosophy also threatens to invade the workplace (and brings to mind a variation of that Neil Kinnock formulation: “I warn you not to be stressed, I warn you not to be unfit, I warn you not to be disabled or ageing or tired or having an off day…”).

Alex Pentland, professor of media arts and sciences at MIT, has gone a stage further than virtual world “gamification” in trying to collect data on real-time human performance, and what makes successful teams. He has gone into banks, call centres and research institutions and persuaded workers to wear an “electronic badge” that monitors the tone and range of their interactions and certain elements of body language and self-quantification over a period of months.

“What I decided to do is to try to study this behaviour in the way you would study ants or apes,” Pentland tells me. The results of his work were published in Nature and Science as well as the Harvard Business Review.

“We found that you could pretty accurately predict how well the group or individual would do without knowing any of the group or the content of their work.”

The data suggested that the success of teams had much less to do with experience, education, gender balance, or even personality types; it was closely correlated with a single factor: “Does everybody talk to each other?”

Ideally this talk was in animated short bursts indicating listening, involvement and trust – long speeches generally correlated with unsuccessful outcomes. For creative groups such as drug discovery teams or for traders at financial institutions, say, the other overwhelming factor determining success was: do they also talk to a lot of people outside their group? “What we call ‘engagement’ and ‘exploration’ appeared to be about 40% of the explanation of the difference between a low-performing group and a high-performing group across all the studies,” Pentland says.

It was important that a good deal of engagement happened outside formal meetings. From this data, Pentland extrapolates a series of observations on everything from patterns of home-working (not generally a good idea) to office design (open and collegiate) to leadership. “If you create a highly energetic environment where people want to talk to each other right across the organisation then you have pretty much done your job right there.”

Doesn’t the wearing of a badge monitoring your every move alter the way people behave, I wonder. Don’t people become deliberately more gregarious and promiscuous in their conversations because they know Pentland’s algorithm is “listening”?

“Probably,” he says. “But that’s the outcome you’re trying to achieve anyway.”

If that application and outcome sounds relatively benign, potential extrapolations are not hard to imagine. A German startup called Soma Analytics – tagline: “Evidence-based mobile programmes to increase employee emotional resilience” – has identified some of them in pioneering a system to measure the early-warning signs of anxiety and sleep deprivation in individuals (and potentially employees) which they aim to sell as a pre-emptive strike against the number one enemy of global productivity – “stress-related illness”.

There is no data yet to support the idea that monitoring stress levels might itself be stress-inducing, still less to the privacy invasion that such “routine” data collection might violate. Perhaps it is sufficient to remember that the company shares its name with Soma, the drug that maintains the World State’s command economy in Aldous Huxley’s Brave New World.

The enemy of big data is always privacy. In the ideal world of people, analytics algorithms would have access to all possible “data points” of the lives of employees and potential employees. The argument is that this will ultimately be in the best interests of all parties; employers will recruit ideal candidates and form the best teams, employees will find their most productive role and be most fulfilled. You don’t have to believe it for a moment.

Shepherd is not alone in his faith that the best real-time data set by which to examine culture fit and resonance and to optimise teams would be “a semantic examination of email, which the company anyway owns” or uses of social media, which it doesn’t, but to which it often has access. “To me,” he says, “the ultimate system is not a questionnaire or a game or a badge but an analysis of data that we are already producing all of the time. We like to think of ourselves as special and unique, that a computer cannot tell me who I am, which is wrong because a computer mostly can.”

If any computer can do this, it might exist on the Google campus at Mountain View, California. Talking on the phone to Sunil Chandra, Google’s vice-president of global staffing and operations, I wonder if search central has any tools that can monitor the semantic code of personal emails? “Not that I know of,” he says, a little guardedly.

Google, regularly voted the world’s best place to work (and not just for its share options), famously employs a team of industrial-organisational psychologists, behavioural economists and statisticians who use tools including the annual “Googlegeist” survey of every employee to experiment with each detail of campus life – the size of dinner plates, the space between screens. It begins with the data-rich process of recruitment (“Hiring is the most important thing we do,” Chandra says. “Everyone is involved”). Google receives around two million job applications a year, and each is analysed systematically. “We certainly try to look at all of them,” Chandra says. “We think of recruiting as an art and a science. We are known for the analytics side of it, but we really do have people also look at all the applications we get.” The data tells them optimum outcomes are the result of four or five interviews. They used to do 10 or 12.

The data also tells them that exam grades are not predictive of performance at Google at all, so they are disregarded. The urban legend used to be that a Google interview was laced with “brain teasers”. “How much should you charge to wash all the windows in Seattle?” did for one candidate. “A man pushed his car to a hotel and lost his fortune. What happened?” for another. Chandra says that approach has also been retired. “It was not predictable performance, and therefore not predictable hiring”

So how do they do it? “There is no secret algorithm,” Chandra says. “We use structured behavioural interview techniques, rather than any kind of tests, to look for humble leaders, learners who can work in teams.” They guard against bias by having all appointments tested in committee. “We look for cognitive ability, learning capacity and leadership capacity, particularly latent leadership. And then of course for what we call Googleyness …”

As Chandra explains this, I find myself running through the relatively few job applications of my own career (even fewer successful ones). In particular, I’m reminded of my interview for a role at the literary magazine Granta, which involved the then editor Bill Buford pouring me a tumbler full of single malt whisky and employing his own behavioural interview technique which began with: “Do you like women?” My mumbled “yes” seemed to go a long way to convincing him I had the precise culture fit he required in his deputy.

How would you define Googleyness, I ask Chandra.

“We think of it as a characteristic where folks can bring their whole self to work,” he says.

Can he imagine a situation where the machines can identify Googleyness without intervention from Googlers?

“No,” says the Googlegeister in chief, reassuringly. “We still believe there are a lot of things the data will not tell you.”

• This article was amended on 13 May 2014. An earlier version said that Google receives around three million, rather than two million, job applications a year.



Data Scientist: The Sexiest Job of the 21st Century

When Jonathan Goldman arrived for work in June 2006 at LinkedIn, the business networking site, the place still felt like a start-up. The company had just under 8 million accounts, and the number was growing quickly as existing members invited their friends and colleagues to join. But users weren’t seeking out connections with the people who were already on the site at the rate executives had expected. Something was apparently missing in the social experience. As one LinkedIn manager put it, “It was like arriving at a conference reception and realizing you don’t know anyone. So you just stand in the corner sipping your drink—and you probably leave early.”
Goldman, a PhD in physics from Stanford, was intrigued by the linking he did see going on and by the richness of the user profiles. It all made for messy data and unwieldy analysis, but as he began exploring people’s connections, he started to see possibilities. He began forming theories, testing hunches, and finding patterns that allowed him to predict whose networks a given profile would land in. He could imagine that new features capitalizing on the heuristics he was developing might provide value to users. But LinkedIn’s engineering team, caught up in the challenges of scaling up the site, seemed uninterested. Some colleagues were openly dismissive of Goldman’s ideas. Why would users need LinkedIn to figure out their networks for them? The site already had an address book importer that could pull in all a member’s connections.

Luckily, Reid Hoffman, LinkedIn’s cofounder and CEO at the time (now its executive chairman), had faith in the power of analytics because of his experiences at PayPal, and he had granted Goldman a high degree of autonomy. For one thing, he had given Goldman a way to circumvent the traditional product release cycle by publishing small modules in the form of ads on the site’s most popular pages.

Through one such module, Goldman started to test what would happen if you presented users with names of people they hadn’t yet connected with but seemed likely to know—for example, people who had shared their tenures at schools and workplaces. He did this by ginning up a custom ad that displayed the three best new matches for each user based on the background entered in his or her LinkedIn profile. Within days it was obvious that something remarkable was taking place. The click-through rate on those ads was the highest ever seen. Goldman continued to refine how the suggestions were generated, incorporating networking ideas such as “triangle closing”—the notion that if you know Larry and Sue, there’s a good chance that Larry and Sue know each other. Goldman and his team also got the action required to respond to a suggestion down to one click.

The shortage of data scientists is becoming a serious constraint in some sectors.

It didn’t take long for LinkedIn’s top managers to recognize a good idea and make it a standard feature. That’s when things really took off. “People You May Know” ads achieved a click-through rate 30% higher than the rate obtained by other prompts to visit more pages on the site. They generated millions of new page views. Thanks to this one feature, LinkedIn’s growth trajectory shifted significantly upward.

A New Breed

Goldman is a good example of a new key player in organizations: the “data scientist.” It’s a high-ranking professional with the training and curiosity to make discoveries in the world of big data. The title has been around for only a few years. (It was coined in 2008 by one of us, D.J. Patil, and Jeff Hammerbacher, then the respective leads of data and analytics efforts at LinkedIn and Facebook.) But thousands of data scientists are already working at both start-ups and well-established companies. Their sudden appearance on the business scene reflects the fact that companies are now wrestling with information that comes in varieties and volumes never encountered before. If your organization stores multiple petabytes of data, if the information most critical to your business resides in forms other than rows and columns of numbers, or if answering your biggest question would involve a “mashup” of several analytical efforts, you’ve got a big data opportunity.

Much of the current enthusiasm for big data focuses on technologies that make taming it possible, including Hadoop (the most widely used framework for distributed file system processing) and related open-source tools, cloud computing, and data visualization. While those are important breakthroughs, at least as important are the people with the skill set (and the mind-set) to put them to good use. On this front, demand has raced ahead of supply. Indeed, the shortage of data scientists is becoming a serious constraint in some sectors. Greylock Partners, an early-stage venture firm that has backed companies such as Facebook, LinkedIn, Palo Alto Networks, and Workday, is worried enough about the tight labor pool that it has built its own specialized recruiting team to channel talent to businesses in its portfolio. “Once they have data,” says Dan Portillo, who leads that team, “they really need people who can manage it and find insights in it.”

Who Are These People?

If capitalizing on big data depends on hiring scarce data scientists, then the challenge for managers is to learn how to identify that talent, attract it to an enterprise, and make it productive. None of those tasks is as straightforward as it is with other, established organizational roles. Start with the fact that there are no university programs offering degrees in data science. There is also little consensus on where the role fits in an organization, how data scientists can add the most value, and how their performance should be measured.

The first step in filling the need for data scientists, therefore, is to understand what they do in businesses. Then ask, What skills do they need? And what fields are those skills most readily found in?

More than anything, what data scientists do is make discoveries while swimming in data. It’s their preferred method of navigating the world around them. At ease in the digital realm, they are able to bring structure to large quantities of formless data and make analysis possible. They identify rich data sources, join them with other, potentially incomplete data sources, and clean the resulting set. In a competitive landscape where challenges keep changing and data never stop flowing, data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data.

Data scientists realize that they face technical limitations, but they don’t allow that to bog down their search for novel solutions. As they make discoveries, they communicate what they’ve learned and suggest its implications for new business directions. Often they are creative in displaying information visually and making the patterns they find clear and compelling. They advise executives and product managers on the implications of the data for products, processes, and decisions.

Given the nascent state of their trade, it often falls to data scientists to fashion their own tools and even conduct academic-style research. Yahoo, one of the firms that employed a group of data scientists early on, was instrumental in developing Hadoop. Facebook’s data team created the language Hive for programming Hadoop projects. Many other data scientists, especially at data-driven companies such as Google, Amazon, Microsoft, Walmart, eBay, LinkedIn, and Twitter, have added to and refined the tool kit.

What kind of person does all this? What abilities make a data scientist successful? Think of him or her as a hybrid of data hacker, analyst, communicator, and trusted adviser. The combination is extremely powerful—and rare.

Data scientists’ most basic, universal skill is the ability to write code. This may be less true in five years’ time, when many more people will have the title “data scientist” on their business cards. More enduring will be the need for data scientists to communicate in language that all their stakeholders understand—and to demonstrate the special skills involved in storytelling with data, whether verbally, visually, or—ideally—both.

But we would say the dominant trait among data scientists is an intense curiosity—a desire to go beneath the surface of a problem, find the questions at its heart, and distill them into a very clear set of hypotheses that can be tested. This often entails the associative thinking that characterizes the most creative scientists in any field. For example, we know of a data scientist studying a fraud problem who realized that it was analogous to a type of DNA sequencing problem. By bringing together those disparate worlds, he and his team were able to craft a solution that dramatically reduced fraud losses.

Perhaps it’s becoming clear why the word “scientist” fits this emerging role. Experimental physicists, for example, also have to design equipment, gather data, conduct multiple experiments, and communicate their results. Thus, companies looking for people who can work with complex data have had good luck recruiting among those with educational and work backgrounds in the physical or social sciences. Some of the best and brightest data scientists are PhDs in esoteric fields like ecology and systems biology. George Roumeliotis, the head of a data science team at Intuit in Silicon Valley, holds a doctorate in astrophysics. A little less surprisingly, many of the data scientists working in business today were formally trained in computer science, math, or economics. They can emerge from any field that has a strong data and computational focus.

It’s important to keep that image of the scientist in mind—because the word “data” might easily send a search for talent down the wrong path. As Portillo told us, “The traditional backgrounds of people you saw 10 to 15 years ago just don’t cut it these days.” A quantitative analyst can be great at analyzing data but not at subduing a mass of unstructured data and getting it into a form in which it can be analyzed. A data management expert might be great at generating and organizing data in structured form but not at turning unstructured data into structured data—and also not at actually analyzing the data. And while people without strong social skills might thrive in traditional data professions, data scientists must have such skills to be effective.

Several universities are planning to launch data science programs, and existing programs in analytics, such as the Master of Science in Analytics program at North Carolina State, are busy adding big data exercises and coursework. Some companies are also trying to develop their own data scientists. After acquiring the big data firm Greenplum, EMC decided that the availability of data scientists would be a gating factor in its own—and customers’—exploitation of big data. So its Education Services division launched a data science and big data analytics training and certification program. EMC makes the program available to both employees and customers, and some of its graduates are already working on internal big data initiatives.

Data scientists want to build things, not just give advice. One describes being a consultant as “the dead zone.”

As educational offerings proliferate, the pipeline of talent should expand. Vendors of big data technologies are also working to make them easier to use. In the meantime one data scientist has come up with a creative approach to closing the gap. The Insight Data Science Fellows Program, a postdoctoral fellowship designed by Jake Klamka (a high-energy physicist by training), takes scientists from academia and in six weeks prepares them to succeed as data scientists. The program combines mentoring by data experts from local companies (such as Facebook, Twitter, Google, and LinkedIn) with exposure to actual big data challenges. Originally aiming for 10 fellows, Klamka wound up accepting 30, from an applicant pool numbering more than 200. More organizations are now lining up to participate. “The demand from companies has been phenomenal,” Klamka told us. “They just can’t get this kind of high-quality talent.”

Why Would a Data Scientist Want to Work Here?

Even as the ranks of data scientists swell, competition for top talent will remain fierce. Expect candidates to size up employment opportunities on the basis of how interesting the big data challenges are. As one of them commented, “If we wanted to work with structured data, we’d be on Wall Street.” Given that today’s most qualified prospects come from nonbusiness backgrounds, hiring managers may need to figure out how to paint an exciting picture of the potential for breakthroughs that their problems offer.

Pay will of course be a factor. A good data scientist will have many doors open to him or her, and salaries will be bid upward. Several data scientists working at start-ups commented that they’d demanded and got large stock option packages. Even for someone accepting a position for other reasons, compensation signals a level of respect and the value the role is expected to add to the business. But our informal survey of the priorities of data scientists revealed something more fundamentally important. They want to be “on the bridge.” The reference is to the 1960s television show Star Trek, in which the starship captain James Kirk relies heavily on data supplied by Mr. Spock. Data scientists want to be in the thick of a developing situation, with real-time awareness of the evolving set of choices it presents.

Considering the difficulty of finding and keeping data scientists, one would think that a good strategy would involve hiring them as consultants. Most consulting firms have yet to assemble many of them. Even the largest firms, such as Accenture, Deloitte, and IBM Global Services, are in the early stages of leading big data projects for their clients. The skills of the data scientists they do have on staff are mainly being applied to more-conventional quantitative analysis problems. Offshore analytics services firms, such as Mu Sigma, might be the ones to make the first major inroads with data scientists.