Managing a team of data scientists is a highly technical and demanding role that requires a candidate to be a jack of all trades when it comes to developing data-driven products and architectures. A typical team working on data science projects will encompass data scientists with a highly analytical capability as well as those whose role emphasizes a software engineering component dealing with production-quality code. Finally, the team can include big data engineers, database specialists and roles with a strong research component such as machine learning engineers and natural language processing engineers. Thus at its core, the data scientist lead requires the efficient orchestration of a highly technical team and an in-depth understanding of the challenges of the different roles that comprise the team.
The ideal background for this candidate is an experienced data manager who has worked in a team and has both a strong theoretical background in fields such as machine learning and predictive modelling but also very strong software engineering skills. To be an effective leader, the ideal candidate will also have great communication skills, be well organized, and be able to prioritize and plan in a way that mitigates many of the risks that come with doing research and analyzing massive quantities of data. Finally, top candidates will also demonstrate a good understanding of data-driven services at the product level and how individual features impact the way customers interact and engage with a company’s product line.
A data science lead interview should include questions that could be asked for a general data scientist role. For examples of these, check out our interview questions for the data scientist (analysis) and data scientist (coding) roles. In addition to these, questions for the data scientist lead should focus on leadership and management skills: