Data scientist recruiting can be a challenging task, but not an impossible one. Here are eleven tips that can get you going in the right recruiting direction:
1. Focus recruiting at the universities that have top notch computer programming, statistical, and advance sciences. For example, Stanford, MIT, Berkeley, and Harvard are some of the top schools in the world. Also a few other schools with proven strengths in data analytics, such as: North Carolina State, UC Santa Cruz, University of Maryland, University of Washington, and UT Austin.
2. Look for recruits in the membership rolls of user groups devoted to data science tools. Two excellent places to start are The R User Group (for an open-souce statistical tool favored by data scientists) and Python Interest Groups (for PIGies). Revolutions provide a list of known R User Groups, as well as information around the R community.
4. Hang out with data scientists at Strata, Structure:Data, and Hadoop World conferences and similar gatherings or at inform data scientist “meet-ups” in your area. The R User Group Meetup Groups is an excellent source for finding meetings your a particular area.
5. Talk with local venture capitalist (Osage, NewSprings, etc.), who is likely to have gotten a variety of big data proposals over the past year.
6. Host a competition on Kaggle (online data science competitions) and/or TopCoder (online coding competitions), the analytical and coding competition websites. One of my favorite Kaggle competitions was the Heritage Provider Network Health Prize – Identified patients who will be admitted to a hospital within the next year using historical claims data.
7. Candidates need to code. Period. So don’t bother with any candidate that doesn’t understand some formal language (R, Python, Java, etc.). Coding skills don’t have to be at a world-class level, but they should be good enough to get by (hacker).
8. The old saying that “we start dying the day we stop learning” is so true of the data science space. Candidates need to have a demonstrable ability to learn about new technologies and methods, since the field of data science is exponentially changing. Have they gotten certificates from Coursa‘s Data Science or Machine Learning course; contributed to open-source projects; or built an online repository of code or data sets (e.g., Quandl) to share?
9. Make sure a candidate can tell a story in the data sets they are analyzing. It is one thing to do the hard analytical work, but another to provide a coherent narrative about a key insights (AKA they can tell a story). Test their ability to communicate with numbers, visually, and verbally.
10. Candidates need to be able to work in the business world. Take a pass on those candidates that get stuck for answers on how their work might apply to your management challenges.
11. Ask candidates about their favorite analysis or insight. Every data scientist should have something in their insights portfolio, applied or academic. Have them break out the laptop (iPad) to walk through their data sets and analyses. It doesn’t matter what the subject is, just that they can walk through the complete data science value chain.
Categories: Practice & Methodology