At the heart of a successful Big Data deployment is human intelligence – but a shortage of data scientists is looming.
Gartner estimates Big Data will generate six million U.S. jobs over the next three years (CNNMoney). However, McKinsey & Company predicts there won’t be enough qualified people to fill those jobs. The consulting firm expects a 50-60% shortfall in analytic scientists in the U.S. by 2018.
That’s a big problem because the heart of a successful Big Data analytic deployment is human intelligence. This got FICO thinking about what makes a good data scientist (a topic FICO’s Andrew Jennings recently covered more expansively in an Analytics Magazine article). Here are a few tips taken from his experience which he believes can help banks as they build their analytics teams.
1. Find people who are focused on solving problems, not just boosting model performance. The point of leveraging Big Data analytics is to solve problems in ways that impact the bank’s bottom line. Good data scientists ask questions like: What decision are we looking to improve? How do we make that decision today? What are the deployment constraints? How will we measure improvement?
2. Make sure they can talk with business people. Data scientists must not only be good problem solvers; they must be good at identifying the right problems to solve and framing the questions in a way that yields meaningful answers. These problems are hard to solve and often take a non-mathematical mindset. Many data scientists are purely technical thinkers. But there is a rare breed of data scientist who conceptualizes and communicates in ways that are meaningful to a business audience.
3. Put more emphasis on skills and mindset than degrees. While a strong background in numerical science is a necessity, good data scientists must also be detail-oriented, inquisitive and open-minded. They question everything they find.
When considering job candidates who are transitioning into data analytics from another career, be sure they are able to manipulate data and think logically. Look for a demonstrated ability to learn a programming language and link various concepts via code. There is obviously a need for individuals who know Big Data programming frameworks such as Hadoop and statistical programming languages such as R.
4. Use your current analysts to sniff out the real deal from the pretenders. When interviewing data scientists, having one involved in the process is valuable. Some candidates exaggerate their experience. They may claim to have run a full analytics process but really have been involved in part of it. You don’t want to hire someone who says they’re a modeling superstar but in fact specializes in data cleansing.
5. Embrace diversity of experience and skills. As in any other hiring situation, you should guard against hiring people that “look just like the people you already have.”
We’re entering a new age of analytic competition. It’s a great time to be a data scientist, but a tricky time to hire one. Every candidate will claim mad math skills. It is important to appraise those skills, while also looking for problem solvers and communicators who will make your analytics team a more valuable part of your bank’s success.
