Managing a Data Science Team: Priorities, Communication and Engineering Partnership

This data science role includes managing a team, and several interview conversations probe how you lead: how you manage the team, how you set priorities, how you communicate, and how you work with engineering teams. Answer each with your approach and a concrete example from your own experience. ### Clarifying Questions - How large is the team, and does it support one product area or several? - Are the data scientists embedded in product teams or organized as a central team? - Who sets the team's roadmap: the data science manager, product leadership, or both together? ### Part 1 — Managing the team How do you manage the data scientists on your team: setting expectations, developing people, and handling someone who is struggling? ```hint Specific people, specific actions Pick one person whose trajectory changed while you managed them, and be ready to say what you did differently for them. ``` #### What This Part Should Cover - How expectations and quality standards are set for analytical work - Development and feedback practices, shown through an example - How underperformance is diagnosed and handled ### Part 2 — Setting priorities Requests from product partners exceed what the team can deliver. How do you decide what the team works on, and how do you say no? ```hint Put requests on one scale Think about how you would compare a dashboard request, an experiment analysis and a longer-term modeling project against each other. ``` #### What This Part Should Cover - Criteria for comparing requests - The process: intake, roadmap and alignment with stakeholders - An example of declining or deferring a request, and what happened ### Part 3 — Communicating How do you communicate analysis results, especially uncertain or unwelcome ones, to product managers and leadership? ```hint Start with the decision Think about what the audience must decide after hearing you, and what they need to hear first. ``` #### What This Part Should Cover - Structuring a message around the decision, at the right depth for the audience - Communicating uncertainty and limitations honestly - An example of delivering a finding the audience did not want ### Part 4 — Working with engineering How do you work with engineering teams, for example to get product data logged correctly or to put a model or metric into production? ```hint Data starts with engineers Consider what goes wrong when the people who log the data and the people who analyze it never agree on what an event means. ``` #### What This Part Should Cover - Shared definitions and ownership of logging and data quality - Getting analytical work onto engineering roadmaps - An example of resolving a disagreement with engineering ### What a Strong Answer Covers - Concrete examples in situation, task, action and result form, with the candidate's own actions and measurable outcomes - Management practices specific to analytical work, such as review of analyses and reproducibility - Prioritization tied to the decisions and business value the work enables - Honest communication of uncertainty, and the courage to deliver unwelcome results - Partnership with engineering built on shared definitions and mutual understanding of constraints - Reflection on what the candidate would do differently ### Follow-up Questions - Tell me about a time your team's analysis turned out to be wrong after it had been shared. What did you do? - A senior product leader insists on a success metric you believe is misleading. How do you handle it? - How do you balance doing hands-on analysis yourself with managing the team?