Designing rigorous observational research frameworks that move beyond correlation toward defensible causal inference
Addressing confounding, selection bias, missingness, and other structural challenges in real-world healthcare data
Extracting outcome intelligence from newly acquired longitudinal practitioner datasets
Connecting prescribing patterns, lab data, and behavioral signals into coherent analytical narratives
Establishing methodological standards that protect the integrity of our claims
Translating complex statistical findings into clear implications for Product, Marketing, and executive stakeholders
Leading and mentoring a small data team while remaining deeply hands-on in research and modeling
Building repeatable research processes that can scale as new data flows in

Advanced training in statistics, biostatistics, econometrics, or a related field with deep expertise in causal inference
Demonstrated experience designing studies in observational healthcare data where randomization is not available
Practical understanding of the pitfalls in real-world clinical datasets, including confounding variables, selection effects, and incomplete outcome signals
Comfort working with messy inputs such as PDF labs, narrative notes, inconsistent schemas, and evolving data structures
Strong Python proficiency and hands-on experience with statistical modeling libraries
A track record of translating analytical insight into tangible business or product decisions
Professional maturity and autonomy to run a high-visibility research function independently
Experience leading or mentoring other data professionals while maintaining technical depth

Lead Data Scientist

Key skills