• A bias to deliver. You would rather have a clear answer to a real question in front of a clinician next week than a beautiful methodology that ships next quarter.
• A scrappy streak. You can pick up an unfamiliar dataset, statistical method, or clinical concept on a Wednesday and have a credible first cut by Friday.
• A serious drive to keep getting better. You read other people’s analyses, papers, and code. You treat being wrong as cheap information rather than an event requiring counseling.
• Experience in data science, analytics, or applied statistics: 5+ years, with at least 2 in healthcare, biomedical, or another regulated longitudinal-data domain.
• Strong foundation in statistics and probability: you can defend a confidence interval, a regression specification, and a sensible forecasting baseline honestly.
• Solid working knowledge of forecasting and time-series methods (classical and modern), regression modeling, and applied ML for tabular data (gradient boosting and related).
• Familiarity with the design and analysis of experiments and quasi-experiments: enough to know when an A/B test is the right tool, when it isn’t, and what to do about it.
• Strong Python (pandas, scikit-learn, statsmodels, and at least one forecasting library) and SQL that you write without apologizing for.
• Comfort with a cloud warehouse (Snowflake preferred) and modern data tooling such as dbt and a workflow orchestrator (Dagster, Airflow).
• Track record of partnering with domain experts and turning their questions into defensible analytical work: clinicians, ops leaders, or comparable stakeholders.
• Clear written and visual communication: tight executive summaries, defensible methods sections, and charts that have titles, axes, and a point.
• Interest in healthcare and the responsibility that comes with working on data that affects patient care.