Senior Data Scientist
Posted Aug 19, 2026 · We last checked this listing on Oct 5, 2026
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
Tell me about a predictive model you built on clinical or claims data that made it into production.
What they are checking: Whether they have carried healthcare models past the notebook into something people actually use.
Tell me about a time you partnered with engineering to make a data science solution scalable.
What they are checking: Whether they can work across product and engineering to turn prototypes into production-ready capabilities.
What would you do
A clinical stakeholder doesn't trust your model's output. How do you build their confidence?
What they are checking: Whether they can explain models in clinical terms and invite scrutiny rather than defend a black box.
A business team asks for a model but can't say what decision it will drive. What do you do?
What they are checking: Whether they push to define the decision and success measure before investing weeks in modeling.
About the work
How would you predict risk of readmission using EMR data, and what pitfalls would you watch for?
What they are checking: Whether they understand leakage, coding changes and missingness patterns that make healthcare models look better than they are.
How have you used NLP or large language models on clinical notes or other unstructured healthcare text?
What they are checking: Whether they can apply modern language models to messy clinical text and evaluate them honestly.
How do you check a model for bias across patient groups like race, payer type or age?
What they are checking: Whether they treat fairness as a measured property of the model, which matters when outputs affect patient care.
You're combining claims data with EMR data from different hospitals. How do you handle the differences in coding and completeness?
What they are checking: Whether they can reconcile disparate healthcare sources without quietly introducing errors into the analysis.
Walk me through how you'd decide between a simple statistical model and a complex machine learning model for a client question.
What they are checking: Whether they choose methods by what the problem and audience need rather than what is most impressive.
How would you monitor a deployed model for drift in a healthcare setting?
What they are checking: Whether they plan for models degrading as coding, practice patterns or patient mix change over time.
How do you handle PHI responsibly when exploring large healthcare datasets?
What they are checking: Whether they work within HIPAA and data use limits as a habit rather than as an afterthought.
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As published by Vizient.