Principal Research Data Scientist

HealthLeap•San Francisco, CA

6d•Hybrid

About The Position

HealthLeap is building an AI operating system to help care teams identify hospitalized patients who need intervention, aiming to improve health outcomes and generate revenue. The company has experienced significant growth in contracted revenue and expanded across health systems, now assisting with patient identification in millions of inpatient encounters. HealthLeap is a ~25-person, SF-based company with over $32M in funding, offering a hybrid-friendly work environment. The role involves asking critical questions about hospital care, such as which patients are missed for which conditions, what interventions truly change outcomes, and where screening is effective. The position will conduct statistical analyses to test the impact of patient screening, analyze results, and identify areas for improvement. This work will support partners and go-to-market efforts, potentially shaping a product used daily by clinicians. The role offers the opportunity to build the research agenda from scratch while leveraging extensive EHR data from over 40 hospitals, hundreds of thousands of patients, and real-world deployments. Support will be provided by the data science and engineering teams. The ideal candidate is curious, impact-driven, and enjoys applied data science, particularly turning messy observational hospital data into citable results in a fast-paced startup environment.

Requirements

PhD in statistics, biostatistics, epidemiology, or a related field.
At least 2 years of (non-PhD) experience conducting observational health research using large healthcare databases.
Background in epidemiology or outcomes research.
Deep expertise in causal inference on observational data: difference-in-differences, regression discontinuity, interrupted time series, propensity methods.
Fluency in Python, including the ability to wrangle large, observational clinical datasets.
A track record of owning analyses or full research projects independently.

Nice To Haves

Hands-on experience with EHR, claims, and billing data.
Familiarity with healthcare quality metrics and health system benchmarking.
Experience presenting research at conferences or to external audiences.
Exposure to claims or billing data.
Industry experience, though strong academic candidates are welcome.

Responsibilities

Own research projects end-to-end, from study design through analysis, interpretation, and publication.
Design and run observational and quasi-experimental studies on real-world hospital data.
Analyze complex clinical and operational datasets and stand behind the methods.
Collaborate with frontline clinicians, health system execs, our customer success team, our go-to-market teams, and our data science team to come up with new research questions, weigh in on product decisions, and lead the outcomes and impact studies tied to our health system partnerships.