Technical and Statistical Analysis
This area tests your ability to select and execute the right analytic methods, implement them cleanly, and interpret outputs with appropriate caveats. Interviewers will explore how you move from research questions to models, ensure data quality, and generate publication-ready figures and tables.
Be ready to go over:
- Statistical programming and tooling: R (tidyverse, lme4), Python (pandas, scikit-learn), Stata/SAS; version control with Git
- Modeling approaches: generalized linear models, mixed effects, survival analysis, time series, causal inference basics
- Results communication: effect size interpretation, uncertainty intervals, sensitivity analyses, assumptions
- Advanced concepts (less common): compartmental transmission models, Bayesian inference, simulation frameworks, machine learning for clinical/public health data
Example questions or scenarios:
- “Walk us through how you handled missing data and confounding in a multi-site study.”
- “How would you design and validate a transmission model for a pulmonary pathogen using limited time-series data?”
- “Show us how you’d critique a model that has high AUC but poor calibration.”
Data Engineering, Pipelines, and Reproducibility
Expect scrutiny on how you acquire, clean, and structure data for scalable, auditable analysis. Teams value analysts who pair speed with traceability and security.
Be ready to go over:
- Data ingestion and QC: building SOPs, automated validation checks, reproducible ETL/ELT
- Data management: database schemas, tidy data principles, code/data versioning, metadata
- Reproducible research: notebooks vs. scripts, project scaffolding, environment management
- Advanced concepts (less common): workflow orchestration, containerization, CI for analytics, secure computing environments
Example questions or scenarios:
- “Describe the SOPs you established for data entry and updates across multiple studies.”
- “How do you ensure a pipeline remains reproducible over a multi-year grant with evolving variables?”
- “Given a CSV dump with schema drift, outline your QC and harmonization steps.”
Research Design and Methods (Quantitative and Qualitative)
Interviewers will assess your understanding of study design, bias, measurement, and appropriate analytical alignment. Some teams will also probe qualitative rigor and mixed-methods integration.
Be ready to go over:
- Study design: cohort, case-control, RCTs, quasi-experimental designs; power and sample size considerations
- Validity and bias: selection bias, measurement error, confounding, effect modification
- Qualitative methods: coding frameworks, codebooks, thematic analysis, reflexivity, triangulation
- Advanced concepts (less common): pragmatic trials, implementation science, mixed-methods joint displays, causal diagrams
Example questions or scenarios:
- “How would you evaluate a school-based mental health intervention using a mixed-methods design?”
- “Explain how you’d structure a difference-in-differences analysis with staggered adoption.”
- “Walk us through your approach to building and validating a qualitative codebook for youth advisory data.”
Domain Expertise and Applied Modeling
Your domain depth helps you make sound methodological choices under real constraints. Teams will explore how you’ve applied methods to specific clinical, public health, or digital health contexts.
Be ready to go over:
- Public/Global health analytics: surveillance data, cohort follow-up, health systems metrics
- Clinical/biomedical: outcomes definitions, censoring, EHR data idiosyncrasies, data privacy
- Digital health/AI: feature engineering, model drift, human-in-the-loop validation, explainability
- Advanced concepts (less common): specialized infectious disease models, sensor/telemetry data, external validation across sites/countries
Example questions or scenarios:
- “Design an approach to estimate vehicle speed from video data and validate it for a public health application.”
- “Describe how you’d assess generalizability of an AI model trained on one hospital’s EHR to another site.”
- “How do you calibrate and communicate uncertainty in an infectious disease forecast?”
Communication, Collaboration, and Leadership
Strong analysts translate complexity for different audiences and elevate team practices. Expect to demonstrate influence without authority, mentorship, and stakeholder alignment.
Be ready to go over:
- Stakeholder communication: tailoring to PI, clinicians, program managers, funders
- Artifacts: one-page briefs, figures, QC dashboards, methods appendices
- Mentorship and standards: training junior staff, code reviews, lab SOPs, authorship norms
- Advanced concepts (less common): grant strategy alignment, cross-lab data sharing agreements, data governance councils
Example questions or scenarios:
- “Tell us about a time you persuaded a PI to change an analysis plan—and what changed as a result.”
- “How do you mentor trainees to use reproducible workflows?”
- “Describe a challenging authorship/credit discussion and how you resolved it.”
