This Lead Data Scientist interview profile brings together a snapshot of what to look for in candidates with a balanced sample of suitable interview questions.
Lead Data Scientist Interview Questions
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 lead, the ideal candidate will also have great communication skills, be well organized and 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:
- Discuss the common pitfalls and risks in planning a data science project such as building a model that predicts whether a bank customer will default on their loan.
- What is the biggest team that you have ever managed and what challenges had you faced?
- Do you have experience in managing agile teams?
- A model your team has built performs 90% accuracy. What do you need to know in order to interpret whether this is good or not?
- Discuss a data-driven product that has really impressed you in recent years
- How do you think one becomes a data scientist? What do you look for when you want someone to join your team?
- What is big data, really? Are you familiar with big data architectures?
- Off the top of your head describe a product that uses data from twitter to build something that people could conceivably pay money for.
- How do you stay current in your job and what are the challenges to doing this when you are a data scientist
- How would you evaluate a feature such as Spotify’s Discover Weekly playlist?
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