Whether you are experienced or thinking about getting into Data science, in this guide you will find out:

• Which cities top the chart when it comes to the highest data scientist salary available?
• Do Data Scientists like working with startups?
• Do they want to stick to a job for more than a couple of years?
• What tools should you learn to get the highest data scientist salary?

We reveal all.

The Data Scientist is often a storyteller presenting data insights to decision-makers in a way that is understandable and applicable to problem-solving.  

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The role of Data Scientists has dramatically evolved over the last five years from mere data miners to complex problem solvers. From entertainment companies like Netflix to retail brands like Walmart – all have business models that now heavily rely on data intelligence.

Data Scientists collect, analyze, and interpret large volumes of data, in many cases, to improve a company’s operations. They develop statistical models that analyze data and detect patterns, trends, and relationships in data sets. This information is vital to predict consumer behavior or to identify business and operational risks. 

Data Scientist Salary – Is data scientist a high paying job?

For the year 2020, Glassdoor named Data Scientist as the third most desired job in the United States with more than 6500 openings and a median base data scientist salary of $107,801with a job satisfaction rate of 4.0.

When Glassdoor had named Data Scientist as the best job in the U.S, we published a scenario of Data Scientists’ jobs and salaries in Europe based on a report by Big Cloud.

Amidst this high demand of Data Scientists across the globe, it is not only difficult to hire Data Scientists, but also challenging to retain them.  Undoubtedly, salary is one of the major components when Data Scientists look at jobs and decide what is going to be their next big gig. A lot of experienced data scientists are increasingly freelancing to increase their salary.

Data scientist salary trends we picked up from a recent report by Big Cloud titled European Salary Report. This report delves into insights from over 1300 responses and 33 questions asked to professionals of all backgrounds, ages, and locations within the European region — the largest share of contributions being from Germany, France, UK, Netherlands, and Switzerland. 

Data Scientists prefer learning new skills on the job. Retain them!

Pay is a strong motivator although employees are willing to stay at their current company for longer – more so than before. This could be due to companies starting to understand the serious talent gap in the market and offering more competitive salaries to retain employees with new skills and increasing the overall average salary in the United States and Europe.

Comparing this to just 2% of employees staying with the same company for 10+ years, this data solidifies the ideology that Data Science is a fast-paced industry where Data Scientists and others in similar roles see value in learning new technologies and skills elsewhere. this comes as no surprise, as more companies recruit data scientist teams, salaries rise, and more specialist skills become sought after.

Python: The most popular modelling coding language for Data Scientists

70% of respondents said they use Python as their primary modelling coding language. This is a 10% increase from last year. A key skill to command at least an average data scientist salary. However, to go above an average salary more coding languages may be needed.

Broadly 9% use R, 4% use SQL, and 4% use Java. There were also 3% of respondents that said they don’t code.

In addition, 66% of respondents said their primary production coding language is also Python. These charts only highlight the top five primary modelling and production coding languages from the respondents. There were also 2% of use cases for C++ in modelling and 2% in Matlab. 

Most popular tools and methods used by Data Scientists

As high as 90% of participants chose Python as a tool they use regularly across Europe, which highlights just how universally accepted this data science tool is. Another 65% chose Jupyter notebooks and 60% chose SQL. When it came to Data Science methods, logistic regression, neural networks, and random forests were the top three most popular choices with roughly 56% of respondents claiming to use them.

Compared to a Data Scientists tool preferences, there is a much greater variety amongst their chosen data science methods. Other options in the survey (that didn’t make it to the top seven shown above) were 34% ensemble, 31% Bayesian techniques and 28% SVMs.

Expected Salary Increase by European Data Scientists

The Big Cloud survey asked its scientist respondents, ‘if you were moving jobs, what percentage salary increase do you think is realistic?’ A 23% majority expect to see an 11-15% increase in salary per year, with a further 19% expecting anywhere between 16-20%.

Upon comparing our 2019 results to the 2020 results, free food and equity/shares are more popular benefits now than they were than last year, with car/transport allowance and gym and leisure moving down the majority list instead.

If you are wondering where would a data scientist want to relocate for a new job? The top five places to relocate as a data scientist amongst European citizens were the United States, Germany, Switzerland, the UK, and France – the places with the highest data analytics salary on offer.

How much can you make as a data scientist?

Switzerland offers the highest salaries for Data Scientists

Here is a look at salaries in different cities in Europe (including the UK for now)- Switzerland leading the way!

Data Science salary in the United States

If you are on either coast this makes a difference, according to indeed the average data science salary is $123,785 and according to glassdoor its $113,436.

Entry-level can range the widest from $50,000 to $90,000. this probably depends on your prior background and education in your career and of course, location.

Seeing that the data analytics industry is young, its not surprising to see professionals more active in moving employers often as anyone with experience becomes far more valuable to companies and can reach manager status quickly.

Data Scientists are more than willing to work with startups

Across all industries in Europe, 68% of people do not currently work in start-ups. Despite this, the data science market remains open-minded, with 83% saying they would consider joining one in the future.

The respondents that currently work in start-up industries are primarily Technology/IT and Consulting (37%). Respondents from these two industries are also respectively the majority that would consider working for a start-up in the future (38%).

Which industry do Data scientists work in?

Consulting was the number one spot in the O’Reilly salary survey, followed by software and banking. No surprises there as these companies have the resources and huge amounts of data to go through. We fully expect data in insurance to be one of the biggest growth areas in 2021.

Final thoughts on a data scientist salary in 2020

Python is the most used although to increase your salary data science skills should continually be enhanced, especially start-ups looking for more rounded skills with SQL and Spark, for example, will only add to boost a data scientists salary.

O’Reilly’s Data Science Survey found that learning D3, visualization library in javascript can boost a salary by $8,000 a year.

SQL, Excel, R and Python are the most commonly used tools although we would suggest learning another to make your resume really stand out.

Lastly, familiarity and experience with cloud computing will also boost salaries, with respondents who use Amazon Elastic Mapreduce getting a boost of about $6,000 in their salaries.

Big data in 2020 is still growing with lots of companies still to recruit we think scientist salaries will continue to rise, and the remote opportunities will also grow as companies compete for experience, skills and need to widen their job search to all geographies to recruit this in-demand skill set.

Disclaimer: The content of this article is from Data Science Salary Report 2020 Europe by Big Cloud & O’Reilly Data Science Salary Survey

As a relatively new role, “data guru” is a challenging job specification to draft for. Organisations are seeking highly-skilled and well-educated individuals to fulfil the position but, the truth is, the data scientist an organisation needs is not a guru, but a colleague.

Most organisations forget that recruiting the right talent is just as much about them as it is about the potential candidates. For example, does the organisation provide an interesting and successful environment for the data scientist to thrive in? Does it create new opportunities and positions for data scientists? Does it support its data scientists and allow them the freedom to work creatively?  

Understanding what data scientists look for is crucial when looking to recruit and retain the right data talent.

So, what makes a data scientist tick?

The fact of the matter is that the attrition rate for data scientists is very high. A recent poll by KDNuggets data scientists revealed that more than one in three expect to stay in their job for three years of less. There are a number of reasons that can lead to a data scientist deciding to hand in their notice, and often these things are in the organisation’s control – the company’s culture and technology available for the data scientists to use.

If the organisation doesn’t provide access to data and the tools necessary for data scientists to do their jobs well, it will lead to frustration. More importantly, these barriers make it difficult for data scientists to achieve their goals and perform to their best level, which understandably results in shorter tenures.  

Moreover, from a cultural perspective, many businesses aren’t quite up to speed with data. This starts with the C-suite: if senior management cannot see the value of a data-driven culture, then it will stifle efforts. A data scientist will soon feel under-appreciated and question the point of their analyses and recommendations if action isn’t being taken by the business.

Even if data is at the heart of the business, the data scientist is often left out of the decision-making process. Not only does this dissociate them from the hard work they have done, but it often leads to their work being misinterpreted, with the full benefits of the analyses being lost on the board.

What will draw a data scientist to work for a business?

1.The right challenge

Data scientists are often drawn to innovation – they want to be a part of it, to evoke it, and to drive it. First and foremost, you will attract data talent by ensuring that your organisation is pushing the boundaries of data analytics and use. Nothing is more engaging than a challenge, and data scientists want to be challenged by your company if they’re going to consider it as a place to work.

2. The right tools

This almost goes without saying. A good comparison is surgeons. You wouldn’t expect a heart surgeon to be able to carry out their job properly or effectively if they didn’t have the right tools or equipment available to them in the operating room. It’s the same for data scientists. Without the right tools in place, data professionals may only be working with partial, fragmented datasets or they may not have access to all the data they need, in order to gain the insights that will help to transform the business.

3. The right level of empowerment

With the right tools in place, people need to be given the space, time and trust to think and work creatively. Taking on-board their insights and actioning their suggestions will go a long way in making a data scientist feel appreciated and included in the company’s success.

4. The right training and development

Innovation is a constant within data analytics – from new tools and developments to learning from others’ methods and implementations. It is important your data scientists are continuously challenged and are learning new skills to keep up with this ever-developing market. Your organisation should open up a dialogue with your data professionals, so that you know what they want, what they are good at, and what they need from you. Only then can you help them develop themselves and grow into an integral role for the business.

Conclusion – It takes two to data science

The hiring process is not a one-way affair – while the organisations must make the decision to hire a data scientist based on their skills and experience, the data scientist must also decide whether the organisation is the right place for them to grow and develop their career.

As soon as organisations start realising this, then they can work on becoming a more attractive and exciting business to work for – providing the right challenges, tools, culture and environment for data scientists to thrive. In doing so, the pool of prospective data professionals that are applying to work for the business will inevitably increase, enabling them to hire the best people and to help the business grow and maintain data science success moving forwards.

Here is what a recent report says about job opportunities for Data Scientists across Europe including salaries and benefits, job motivations, programming languages used, tech skills and what people want most from their work.

Glassdoor names “Data Scientist” as the best job in the United States for 2019 and LinkedIn ranks it number one among the top 10. Topping the list for four years in a row,  Data Scientist has a job score of 4.7, job satisfaction rating of 4.3 with 6,510 open positions paying a median base salary of $108,000 in the U.S. But what is the scenario for Data Scientists in Europe? What is the demand and supply? Which countries in EU are the best destinations for Data Scientists and what salaries can they expect? A recent report titled Data Science Salary Report 2019 Europe by Big Cloud  answers some of these critical questions.  

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First, a little flashback: According to a report by the European Commission in 2017, the number of data workers in Europe will increase up to 10.43 million, with a compound average growth rate of 14.1% by 2020. The EU forecasted to face a data skills gap corresponding to 769,000 unfilled positions by 2020 in the baseline scenario and being concentrated in particular in the large Member States (especially Germany and France). The European Commission suggests that 100,000 new data-related jobs will be created in Europe by 2020. Hence, there are huge opportunities to be deployed from the digitalisation of European industries.

Now, let’s dive into some of the key findings of the report by Big Cloud. The most popular countries to take part in the survey have been Germany, United Kingdom, France, Netherlands, Spain, Italy, and Switzerland. “The majority of respondents have been Data Scientists, however, we^’ve seen a great number of Machine Learning Engineers, Data Architects, Researchers and C-Level professionals taking part too which has offered a broad scope of the European Data Science market at all levels,” says Matt Reaney, Founder and CEO, Big Cloud, a company based in Manchester’s Northern Quarter which deals with Data Science and AI recruitment.

The report looks at the overarching trends of the market including salaries and benefits, job motivations, programming languages used, tech skills and what people want most from their work. Here is a snapshot:

Data Scientists : Lack of Supply , More in Demand

Interestingly, 58% of the respondents in the report have been at their present company for one year or less. Similar to 2017, this highlights the lack of supply of Data Science talent, compared to the heightened demand. It’s also worth noting that this shows how much people move about within Data Science roles. It’s not an industry where people stay in the same job for very long. Data Science roles are often very project oriented, and we find that Data Scientists like to move to other companies where they can learn new technologies and skills.

Data Scientists like working in Startups

Out of all industries included in the survey, 71% are not startups, which shows an established Data Science market in European countries. There is, however, still an exciting start-up scene, especially in cities like London, Paris and Berlin. A further 71% of whom are not already working in a startup, said they would consider joining one in the future, with only 8% saying they wouldn’t be interested in doing so, and 21% being undecided.

Switzerland offers highest Data Scientist salaries

The highest data science salaries can be found in Switzerland, with an average annual data science salary of  € 115,475, followed by the Netherlands, at €68,880 . Below is a segregation:

Python is the top production coding language for Data Scientists

Below are the five most popular primary modelling coding languages selected by respondents, with 59% using Python. Similarly, Python has come out on top as the most popular production coding language, with C++ 2nd with just under 20% of respondents using this for production coding, but there is quite a large gap between them. There is an even spread of hours spent coding, which could be indicative of the broad range of seniority and experience levels of respondents. The higher paying positions usually consist of less coding hours.

Data Scientists seek meaningful jobs

Between 2017 and 2018, there has not been a vast change in respondents happiness in their current job, in 2017,the report recorded that 65% of people were happy in their current job, which has this year reduced to 62%. Moreover, 68% said they would find it easy to find a new job, which is telling of the demand vs supply Data Science market. It’s also an indicator of the confidence Data Science professionals have in the hiring market. Much like 2017, the top reason to find a new job is higher earning potential. The desire to work on more meaningful projects has overtaken the desire for better work-life balance, most likely indicating the shift in companies beginning to be more accommodating with flexible and remote working.

“In 2017, we saw a reflection of the current hot topics of the day that people wanted to work on – such as fake news. This year, however, we’ve seen an increase in people wanting to use Machine Learning to combat things like climate change – which is a pressing issue of today. Similarly, we’ve seen an increase in people wanting to use Machine Learning in police investigations and counter-terrorism, which could be reflective of the increase of such events in Europe in recent years,” mentions the report.

One overarching trend is for sure, and that is that Data Science professionals want to work on projects that have a positive impact on society.

Final Thoughts

One of the reports by Dataiku rightly mentions that Data scientists are in demand, which necessarily means they are difficult to keep around – after all, they can easily find a position elsewhere. But it’s also a question of happiness: if data scientists were satisfied with the work they do, even if they could find a job elsewhere, they might be less inclined. And since the position is relatively new, many companies don’t really know what to do to retain people in these important, cutting-edge roles. Clearly the industry is populated by winners and losers, companies that know how to best use their employees (including their data scientists) and those that don’t. But it’s not just up to the company – there’s work to be done on the side of data scientists as well, making sure that they market themselves to make their job and the work they do indispensable and visible to others to continue to grow and be able to take on more exciting projects for ever-increasing job satisfaction.

Disclaimer: The content of this article is from Data Science Salary Report 2019 Europe by Big Cloud

When I first started working in data, I used to think it was all about the algorithms and tools. Now banging my head against the wall has helped temper some of that. I now see the Data Science role as including at least some management consultancy.

Recently Max Shron in his “Thinking with Data” book, and Hadley Wickham in his Data Science: how is it different to Statistics article, have been raising the importance of training data scientists – and if you are a data scientist, the importance of training yourself in asking the right questions.

These skills are rarely cultivated by a good statistics or Mathematics education. So I have a few methods to help you develop these “soft” skills:

• Business Skills Training: Some universities have these in their programs, and my experience has been that they are a mixed bag.These also help you ask the right questions and practice the stakeholder management and customer facing skills that you will need whether you are a data scientist at Sony, PwC or even a small startup.

• Consultancy clubs: Some universities have these too – generally where you go through some training on leadership or you discuss business cases. These events include the elevator pitch training, networking, communication skills training, training for case interviews. A good example is McGill Consulting club

• A business case book: A few years ago I would have laughed at this as good advice for Data Scientists but now I think reading one of these books. Ideally through discussion with others – is extremely useful for developing the ‘consulting’ part of the data science skillset. I think that it is really important to be able to talk to senior management in their own language – which is often not statistics. And this means understanding a range of things including their own ‘MBA-speak’ , their strategic objectives, their hopes and fears and how they build a mental model of their companies. It also means ‘here is the R^2 values’ is probably not the best answer. Communicating your results is hard and I have found practicing business cases is really beneficial for this.One famous book is the following: “Case in Point: Marc P. Cosentino”. I personally found this book to be a good compendium of examples giving you some of the techniques and frameworks that are taught in business schools or in consulting use. Having a few of these in your arsenal as a Data Scientist is useful for scoping the data science project.

• Finance for non-Finance types: I recently picked up the excellent book “How to read a balance sheet” and there are other examples out there and any good accountancy introduction or online course would be a good training. The reason I mention this is that there is an unfortunate stereotype in business against technical people – and accountancy is the language of business. To succeed in business you need to understand the language. There are other blogs and online courses that you can follow on Coursera also.
• Marketing: Depending on which team you are working with you’ll have to learn some of their specialist vocabulary such as understanding what metrics the marketing team use and understanding how these are calculated.

• Business development: Another aspect that data scientists at fast growing companies need to know is how to integrate data-driven with the growth and development of the company. For these topics I wholeheartedly recommend the “Lean Startup” books. You could start with the “Lean Startup” book itself!

The importance of developing these ‘soft’ skills cannot be underestimated. It takes some time but I hope that my suggestions are a good start for you on your journey to becoming a better data scientist.

Luckily an academic background develops some of these skills – it is impossible to survive in graduate school without collaborating, giving presentations, and grasping other peoples domain-specific vocabulary.

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Paedar Coyle is a data analyst based in Luxembourg. His trajectory has evolved across physics, mathematics, and computer science in order to extract value from data, innovate and drive change. His work involves statistical modelling, building Data Analytics Proof of Concepts and working with the software teams to deliver them. He is also involved in the business strategy around data analytics and data auditing.

(Image credit: Hillman54, via Flickr)

From apple grower to fine arts student, from software developer to machine learning PhD- Jose Quesada has done it all. Now, he’s established Data Science Retreat, a course to help people with his passion for growth and development to delve into the world of data science. We recently spoke to Jose about his remarkable story, the Data Science Retreat experience, and why so-called “soft skills” are often the making of future data scientists.

Give us a brief introduction to you & your work at Data Science Retreat.

I love developing people. When I see the ‘delta’ on a person after hard work, it makes me feel good. Data Science is ‘the’ place for this ‘huge delta’ development: because the state of the art is changing rapidly, you are forced to teach yourself new things every week just to stay current. Fields like this tend to attract people who like pushing themselves.

I came from a rural background. My father grew apples, and would expect me to do the same. Instead, I studied psychology and fine arts. Then I did a PhD with lots of machine learning. In it I developed a software system to teach pilots how to land commercial aircrafts without the need of a senior instructor sitting next to them (which I didn’t patent; silly me).

What you can see is that I changed direction many times; I taught myself mostly all I know that is really useful. I think we live in self-taught paradise. But after a certain level of excellence, it’s hard to make progress. This is something most aspiring data scientists find. No matter how many MOOCs you do, there’s a barrier that very few people ever break.

This is why Data Science Retreat started. I think I know how to create an environment where you can go “faster than average self-taught speed” and break the barrier of excellence that most people encounter. I asked myself: “What does it need to exist for this to happen?”. My answers was: you need to have access to ‘chief data scientist’-level people, contributors to leading open source packages, etc, and they need to be invested in your progress. You need to be surrounded by other people seeking excellence, too. DSR is the kind of setup that I wish I had when I started. Two batches later, all I can say is that I’m very proud of the result, as is everyone involved.

Talk us through the course structure.

You can check the instructional part online in our curriculum. What you don’t see there is how we approach the ‘portfolio project’, where you do original work under mentors.

We start with finding a good question. This is a creative process, and a skill you will use often once you graduate. Not all questions are answerable with data and machine learning. Of those which are answerable, not all of them produce business value. Once you know you have a good question, finding the data that can be challenging. Or cleaning it. Or making sure it’s correct.

Next step, you find a good evaluation metric (‘How do you know when you’ve won?”), and start iterating with your predictive models. When to stop fine tuning parameters is also a key skill; you will hit diminishing returns eventually.

Once you have demonstrated you answered the question you started with, it’s time to present your results and make a convincing case in front of stakeholders. Here, your communication skills determine everything: your beautiful product may never get put in production if you don’t do this well. You’ve exercised your communication skills quite a bit already by settling on the question; ideally the company is receptive, and was sold on the value. Do they believe you have generated that value, now that you’re finished?

At all times, you could have asked different mentors. You got around 270hrs of instruction on state-of-the-art methods. But let’s be honest: anything can happen here. It’s stressful. You are at the helm managing your project. Often you find your data has nothing going on for it, your predictive models are not doing anything interesting, or you cannot answer the question. You are back at square one. And there’s a hard deadline where companies will sit and look at you with their undivided attention.

What differentiates Data Science Retreat from other courses for aspiring data scientists?

1. Our mentors are at the ‘chief data scientist’-level or contributors to leading open source packages. All our mentors teach, and they are invested in your success. There’s nowhere else in the world you can get this today.

2. We focus on the question as much as on the technical details of the solution. We provide training on technical communication; you will present often, and get one-on-one feedback from a communication expert.

3. We prepare our participants for leadership positions. That is, either being the lead data scientist, or the only one in the company. This is far harder than preparing someone to join an existing group of data scientists and solve problems picked by someone else.

Why did you choose Berlin as the HQ?

There are two hotspots in the EU: Berlin and London.

Berlin has been doing really well in the last five to ten years with regards to Internet tech startups. When you look at figures in terms of size, how many VC-backed companies there are, how much venture funding flows into those companies etc. As a result the tech scene is huge in Berlin, there’s an interesting meetup almost every day.

London is also very interesting. There’s definitely money floating around because of so many banks. But tech-wise, choices are more conservative. If you are a bank, losing information, even if it’s a single transaction, is a big no-no. You have to stick to tried-and-true technologies. Berlin companies can afford to pick riskier, newer technologies, because they often deal with consumer-level information, which is usually not as crucial. If Twitter loses a tweet, it is unlikely they will get sued, unlike a bank. I suspect Berlin is already ahead of London tech-wise, and with time this difference will only grow. This is a good thing for data science, because companies who can take risks will use data scientists sooner than conservative companies.

What do you consider to be the main differences between the data science scenes in the US & Europe?

There’s of course a lot more VC money in the US, and this makes it easier for companies who use data science to exist. There are more web-scale, B2C companies in the US. 40% of the data science jobs are in the valley according to LinkedIn. And the pay is higher over there. So what’s to like about the EU?

• Since there’s less VC money floating around, companies doing well in EU (and hiring) are more likely to have solid business models (and be resilient to big changes in the economy).
• EU companies are less prone to follow fads.
• EU companies tend to offer better working conditions, even if salaries may be lower. Retirement, health insurance etc are all well covered by law. You get a full month of vacation. If you are on a high tax bracket, at least you know your money is not used for say fueling the military industry.
• There’s something to be said about being early days for data science in EU. There are better opportunities for truly outstanding people. From what I hear, there are in the order of 100 people able to do a good job at lead/chief data scientist in the entire EU. If you are one of them, or can imagine to be one shortly, you are clearly in a privileged position.

Still, I worry about EU competitiveness mid-term. Some companies are too traditional, and have trouble integrating data scientists in their structures. But this is a topic for another day 🙂

What are the essential skills and traits a data scientist must possess?

There are three must have skills to just enter the data science space. You need to know some programming of some kind preferably R or Python, but really any programming language will do. The second thing is that you absolutely must know some statistics and machine learning. This shouldn’t be a superficial understanding of these data analysis techniques – any programmer can blindly implement a technique as a black box. You need to actually understand why a particular technique is suitable and what its limitations are. Finally, you need to know how to query databases.

Different data scientists will have different strong suits. Some will be very strong with data visualizations, some with databases and others with statistics but all data scientists need to have these basic skills to work in this space.

We do not run coding tests, because nowadays with sites like stack overflow, it’s easy to write almost anything without really understanding the details. We consider coding tests non-discriminative. We do like to see code samples on github for existing projects.

We invite the most promising applicants to an interview. There, we make sure we are a good match for each other. There are questions about creativity with data, communication, and raw machine learning knowledge. We want to see people who have put the effort to learn this stuff on their own. Many interviews end early.

You’re currently accepting applicants for your third class; what level of prior knowledge do your candidates typically have?

You only need to know at least one programming language well. Other than that, there are no real prerequisites. We have applications from people who are already data scientists, but feel they are stagnating at work. Initial skillsets are all over the place, which makes is challenging (and fun!) to prepare the teaching. As you can see, the curriculum is very varied, and no participant has had experience in more than one or two topic groups.

A big chunk of people applying have been in the industry for years, and/or have PhDs. But I’ve seen many people with no experience, who weren’t so strong on paper, but ended up doing incredibly well during DSR. The sheer willpower and raw intelligence of some participants has been inspiring. I’m happy my interview process detected these people and let them in! I wish more and more people applied even though they felt intimidated by DSR’s reputation; if you know you have it in you, and have a burning passion for data topics, by all means apply! We tell you whether you are accepted the same day of the interview. If you are on the fence, I’ll encourage you to go for it; this batch we are hosted by Zalando, which is a great place because they have around 40 data scientists working already (two DSR alumni!).

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Chris Pearson is a Co-Founder of Big Cloud specialist recruiters in the fields of Big Data & Data Science.”. In his debut article with us, Chris talks about the science behind finding the right Data Scientist.

Read any Big Data Gartner report or salary survey and they all tell you the same thing, Data Scientists are the MVP’s in the team. So how have these self-dubbed “super geeks” managed to go from the receiving end of scrunched up paper snowballs in the classroom to becoming the High School Jock or the Prom Queen?

Well that’s pretty simple, it’s a case of supply and demand.

Most CIO’s who are on their journey into the world of Big Data all want to hire the best Data Scientists for their business.  And from speaking to a fair few of these Data Scientists, it’s pretty clear to see why.  Some are making or saving businesses $multi-millions.  Some are curing diseases and ridding areas of famine.  Some are even predicting the winners at the Oscars.

If Gotham City was a real place, Batman would probably be in line at the Welfare Office, as some Data Scientists are now predicting where crime is about to happen!

So, it’s pretty clear to see why these people are held in the highest regard and clear to see why most businesses are clambering over themselves to hire the best in show.  In some instances this has become a bun-fight at the soup kitchen.  According to EMC, 65% of Data Scientists believe that demand for their skills will outweigh availability over the next 5 years.  Frightening to think that the pendulum could swing that far.

It’s easy to see why businesses want to hire a good Data Scientist.  Attracting the right one is definitely the hard part.

Some traditional methods of recruitment can be ruled out straight away.  Job boards can prove to be futile, such is the shortage of talent available.  Even if the best Data Scientist was to make themselves known on a job board, the rush of approaches from your competitors and other recruiters to hire them, could make the challenge of getting your hands on a ticket for the World Cup final, seem like a walk in the park.

If you know where Data Scientists tend to ‘hang out’ online on social media and other forums, advertising can be successful.  However, in my experience, unless your company has a following of people in the Data Science industry or you have a brand like Twitter, Google or Facebook and have queues of suitable applicants at the door, advertising on your own website alone can be a waste of time.  Advertising can also be quite costly, with no guarantee of a return on investment and it can leave you with an inbox full of applicants who just aren’t relevant.

Being a recruiter in this field, it would be really easy of me to say that the best method is to use a recruitment company, however, the typical feedback we get from new clients or candidates when asked about their last recruiter is that they “didn’t come back with any relevant candidates/jobs”.  Big Data is the biggest band wagon rolling through ‘Recruitmentville’ right now, so it might be worth testing their credentials to see how successful they’ve been, before you start agreeing to pay fees.

Good old fashioned networking can be a trusted route to finding that prized Data Scientist.  According to the most recent Adler Study, around 83% of LinkedIn users (out of 225m) are ‘passive jobseekers’, meaning that they may not be actively looking, but would be interested to hear about new opportunities.  These are the people who aren’t updating their CV’s to upload onto a job board.  These aren’t the people reading job adverts.  These definitely aren’t the people picking up the phone and introducing themselves to businesses!  Typically, the best way of getting to them is through understanding your market and mapping out the talent within it.  Mapping out who works for your competitiors and even going as far as mapping out your ‘competitor’s competitors’.  The only down side to this is that it can be time consuming and may not actually lead to a hire.

It’s worth pointing out that if you already have a team of Data Scientists, don’t let the good ones go!  We speak with Data Scientists all the time and within the first minute of conversation, it’s easy to understand why they’re looking for something new.  They want me to find them a challenging role, so why aren’t you pushing and developing them enough in your business?  They want me to find them more money, so why don’t you know that they’re unhappy with their current deal?  They want me to find them a job with more seniority, so why aren’t you providing them with a clear enough career path?  Sometimes the easiest and cheapest recruitment, is recruiting the team you already have.

Hopefully this will give you some options when it comes to hiring that next Data Scientist.  However, I couldn’t wrap this article up without highlighting the biggest reason that prevents businesses from hiring the right people in this area……”why do I want to hire a Data Scientist, again?”

Big Cloud is a big thinking search firm focusing on all things Big Data. We enjoy speaking to great people, working with great companies and having fun whilst we do it.

Image credit : JD Hancock