Expect a blend of technical depth, applied reasoning, and communication. Use concrete examples from your experience, quantify impact, and be explicit about assumptions and limitations.

Technical and Modeling

This area probes your statistical grounding and practical ML choices in clinical/research settings.

• How do you choose between logistic regression, gradient boosting, and a neural model for a binary clinical outcome? Discuss calibration and interpretability.
• Walk through your approach to handling extreme class imbalance for detecting rare adverse events.
• Explain how you validate a survival model with time-varying covariates.
• Describe a time when model performance degraded in production. How did you diagnose and fix it?
• Show how you would compute and interpret decision curves and net benefit for a risk model.

Causal Inference and Study Design

Interviewers assess whether your conclusions would stand up to peer review.

• Design an observational study to estimate treatment effect from EHR data; specify confounders and your adjustment strategy.
• When would you use propensity score matching vs. IPTW? What diagnostics do you run?
• Explain instrumental variables and give a plausible instrument in a healthcare context.
• How do you address missing not at random (MNAR) data?
• What sensitivity analyses would you run before making a causal claim?

Coding and Data Manipulation

You will demonstrate fluency in transforming messy data into clean analytic sets.

• Write or describe a query to construct a cohort with inclusion/exclusion based on ICD-10 and lab thresholds across time windows.
• How do you design a reproducible analysis environment for a multi-analyst project?
• Show how you would vectorize a feature engineering step to scale to millions of rows.
• Describe your approach to code sets management and unit testing data transformations.
• How do you prevent PHI from leaking into logs, notebooks, or exports?

System/ML Design in Healthcare

We evaluate your ability to design safe, maintainable, and impactful systems.

• Design a pipeline to generate and deliver a daily readmission risk score to case managers. Address monitoring, alert fatigue, and governance.
• How would you detect dataset shift and bias post-deployment? What metrics and triggers would you put in place?
• Propose an approach to model explainability that aligns with clinician workflow.
• Outline a federated learning approach when data cannot leave partner institutions.
• How would you integrate model outputs into Epic (or another EHR) workflow?

Behavioral and Leadership

We look for scientific maturity, ownership, and collaboration.

• Tell me about a time you identified a methodological flaw late in a project. What did you do?
• Describe a high-stakes decision you influenced with data and how you handled disagreement.
• How have you mentored a junior analyst through a complex analysis?
• Give an example of communicating limitations to non-technical stakeholders without losing trust.
• Share a situation where you balanced speed and rigor under tight timelines.

This timeline illustrates the typical progression from initial screening to technical deep dives and cross-functional conversations, culminating in a final panel. Use it to anticipate when to prepare a brief project talk, when to expect coding or design exercises, and when to frame your broader leadership narrative. Build in time between rounds to refine your examples and gather domain context that surfaced in previous conversations.

Deep Dive into Evaluation Areas

Statistical Learning and Modeling in Healthcare

Modeling in healthcare requires statistical rigor and transparency. Your interviewer will assess how you match methods to questions, validate under data shift, and communicate uncertainty. You should be comfortable balancing performance with interpretability and clinical safety.

Be ready to go over:

• Model selection and diagnostics: Choosing between GLMs, tree-based methods, survival models; checking assumptions; error analysis and calibration.
• Evaluation in imbalanced settings: AUROC vs. AUPRC, sensitivity/specificity, PPV/NPV at clinically relevant thresholds; decision curves and net benefit.
• Interpretability and trust: Coefficients, partial dependence, SHAP, counterfactuals; aligning explanations with clinical reasoning.
• Advanced concepts (less common): Time-dependent ROC for survival, competing risks, conformal prediction, uncertainty quantification, fairness metrics across demographics.

Example questions or scenarios:

• "Walk us through how you calibrated a risk model and selected an operating threshold for a care pathway."
• "You have highly imbalanced outcomes (sepsis). How do you evaluate and communicate model performance to ICU leadership?"
• "What methods would you use to quantify and explain feature importance for a clinician audience?"

Study Design, Causal Inference, and Biostatistics

Clinical and public-health insights often hinge on study validity. Expect questions that test your ability to design robust observational studies and communicate causal claims responsibly.

Be ready to go over:

• Cohort construction and confounding control: Inclusion/exclusion, matching, stratification, regression adjustment, propensity scores/IPTW.
• Longitudinal and hierarchical data: Mixed-effects models, generalized estimating equations, repeated measures, missing data strategies (e.g., multiple imputation).
• Time-to-event analysis: Cox models, time-varying covariates, competing risks.
• Advanced concepts (less common): DAGs, instrumental variables, regression discontinuity, negative controls, target trial emulation.

Example questions or scenarios:

• "Design a study to estimate the effect of an exposure on a pediatric outcome using EHR data—how will you address confounding and missingness?"
• "Explain when you would choose an IV approach and how you would test IV assumptions."
• "You observe an association. What additional analyses increase your confidence in a causal interpretation?"