I'm in academia, what do I need to know about data science in industry?

Early this summer, Laney’s budding Data Science for Scientists organization held a meeting to give Ph.D. students tips on getting hired for a Data Science position. In a presentation led by Jeff Kolve, senior technical recruiter at eHire, we learned about: converting academic CVs to resumes for industry, questions to ask during interviews, and different kinds of positions available within the nebulous field of Data Science. Jeff mentioned that his company eHire works to find data scientists and match them with companies, mostly centered in Atlanta.

responsibilities of a data scientist

As with many talks on Data Science, we began with the responsibilities of a data scientist as well as the types of skills needed to succeed. Jeff mentioned the four main responsibilities include:

  • Pure statistics and analytics
  • Data Architecture and Data Engineering
  • Data Visualization
  • Software Development

He emphasized that for aspiring Data Scientists, it’s important to be able to master many of these skills--with the more of these skills we have in our toolbelt the more employable we will be and the more independently we will be able to work within their company.
More and more companies and other types of employers are recognizing the importance of using data to detect and respond to consumer demands, project change, develop new products, and more. "Companies want to be able to say they’re doing data science and advanced analytics,” Jeff asserted. For some companies, the role of Data Scientists will be very cleanly defined with a specific role of the four necessary skill mentioned earlier; whereas, for a smaller company, a Data Scientists may have to be comfortable in all of these roles.


Jeff recommended when interviewing to directly ask a company how do THEY define data science (meaning how much of the four skills above do they expect you to know?). He also recommended being specific when answering what it is you want to do and what you’re good at. He strongly recommended that, while flexibility is important in the interview process, be sure to state your goals and make it clear what type of role you want to have within the company and what type of work you want to do. Among some advice that Jeff gave as part of the talk, he suggested asking why the position is open (to understand rates of turnover in the company and potential reasons for it), researching the interviewers beforehand (to know the level of technical detail that you may go into), and keeping track of the types of interview questions you were asked and how you answered them (so that you can continue to prepare for interviews). A particularly intriguing suggestion made was to ask the interviewer at the end of the interview what specific concerns they have about you as a candidate. Notice that you do not phrase this as a "yes or no" answer! This allows you to address their concerns in the interview. As Jeff explained, if these concerns about you as a candidate are not addressed by you in-person in the interview, they may end up being a factor in not getting a position.

more info straight from Jeff

Overall, the outlook for an aspiring Data Scientist is bright, with the more experiences acquired and tools in your toolkit increasing your likelihood of securing a position! For more detail, including details on converting your C.V. to a resume, and quotes from people in the Atlanta data science community, check out the presentation that Jeff kindly shared with us: https://github.com/Data-Science-for-Scientists-ATL/meeting-2018-06-04