I find the hardest part about being a Recruitment Consultant who specialises in Big Data & Data Science is keeping on top of this accelerating, contemporary market. I truly have to consult and educate companies about what Big Data and Data Science is, how it will improve their business and what this type of person (or team) will look like.
Recently, NewVantage published a survey which revealed 70% of organizations surveyed plan to hire Data Scientists, and 100% of them said it’s “somewhat challenging” to hire a competent one (don’t worry, you can find my contact details at the bottom of this article).
We can hit the challenge at its source, with first defining what we’re looking for. If you type into Google, ‘data scientist definition’ you will get the IBM explanation at the top.
“What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization”
However, I feel that this description is problematic (sorry IBM). In this instance, what does the Data Scientist bring to the table that an Insight Analyst can’t? Insight Analysts are those who find insight within data sets; insight which will bring value to the organisation. They are usually highly educated and use various data analysis methods.
(Another confusing definition is one I posted on Twitter last night…)
Jeanne Harris (MD of Information Technology Research at Accenture), writing for The Guardian, is getting it right.
“The first thing to understand is that data scientists are more than just re-branded business analysts…They are the highly educated experts who operate at the frontier of analytics, where data sets are so large and the data so messy that less-skilled analysts using traditional tools cannot make sense of them…Like any other scientist, they test theories by exploring and running experiments with data. They also design the intricate models, algorithms and visualizations that can help companies distill insights from huge volumes of chaotic data. And they educate and guide general managers, helping them understand and tap into the potential of big data-era analytics”
What do I look for in a Data Scientist? I look for these key aspects:
- Education: highly educated (PhD/Masters)
- Sophisticated Tools & Techniques: Machine Learning, Statistical Modelling, Predictive Analysis, Data Mining, Hadoop, R, Python, SQL, Java, C++, MatLab, SAS, SPSS etc…
- The size of their Data Projects: this is why Data Scientists are closely linked with Big Data, because making sense of data that has multiple sources and is unfathomably huge, is a Science
- Attitude: a passion for data with real business acumen. A firm belief/understanding of data driven business strategy and how Data Scientists are integral for this
If you want to get in touch and talk data, feel free to drop me a line on +44 (0) 2079 282 525 or an email on email@example.com
One thought on “What is a Data Scientist?”
Joe, This is probably one of those questions that is going to have as many answers as there are people asking the question. As someone who is in the software business (actually with IBM) and also speaks with university staff who are asking industry what skills they need to be providing it is a question that interests me. It is also of interest to the UK government whose recent big data strategy paper calls for many more data scientists as a way of improving the economy of UK plc. Some people would argue we’ve always had data scientists dealing with “big data sets”. They have tended to be locked in academia or research however analysing data from particle accelerators, radio telescopes whole body scanners etc. What’s different now is that suddenly business has recognised this is a skill they need as the data they now ‘own’, or have access to, grows exponentially. Surely therefore an intimate knowledge of the business domain is key, just like the knowledge of the ‘business’ domain of particle physics is a great help in understanding what is being analysed. This poses a few interesting questions; 1) How do universities instill this business knowledge as it tends to come from experience? 2) How transferable will the role of the data scientist be between industries? 3) Is there an ethical angle to the role (I believe there is)? Just because you can analyse something and draw conclusions does not mean you should?