Data Scientist
Posted Sep 24, 2026 · We last checked this listing on Oct 6, 2026
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Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Have you helped write grant proposals? What did you contribute?
What they are checking: Their ability to support funding efforts, which the posting lists as a duty.
Tell me about a time you explained a complex result to clinicians who don't code.
What they are checking: Their ability to make technical findings useful to physicians and researchers.
How would you manage several projects in the program at once?
What they are checking: Whether they can prioritize and track work, since the role also manages the program's projects.
What would you do
You'd supervise trainees. How would you help a new student who writes code that works but nobody else can follow?
What they are checking: Their mentoring style and commitment to code quality across a team.
A collaborator asks you to add a new external computational tool to the pipeline. How do you evaluate it first?
What they are checking: Whether they test tools for validity and fit before adopting them.
You find that pathology data and the clinical database disagree on several patients. What do you do?
What they are checking: Whether they investigate data quality issues carefully and involve the right people to resolve them.
About the work
You'd build frameworks to house genomic, pathology and radiology imaging data for a breast cancer program. How would you design that so the data can be linked by patient?
What they are checking: Their ability to design multi-modal data structures with clean identifiers and sensible linking.
Walk me through a genomic analysis you've done from raw data to results. How did you make it reproducible?
What they are checking: Their hands-on genomics experience and use of version control, pipelines and documented environments.
How do you write a data dictionary that clinicians and programmers will both actually use?
What they are checking: Whether they see documentation as a tool for shared understanding across disciplines.
Tell me about a machine learning model you built on clinical data. How did you guard against overfitting or leakage?
What they are checking: Their rigor with real-world clinical data, where leakage and small samples easily mislead.
How do you handle protected health information in your analysis environment?
What they are checking: Their awareness of privacy rules and secure computing for patient data.
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