Principal Research Data Scientist

HealthLeap•San Francisco, CA

8d•$170,000 - $215,000•Hybrid

About The Position

HealthLeap builds AI that helps clinicians prioritize patients, surfaces the right data, and gets patients the care they need earlier, so they can leave the hospital sooner. We integrate with hospital electronic health record systems, screen 100% of patients daily, and risk-rank them in real time. Clinicians at Cedars-Sinai and Penn Medicine start every morning with HealthLeap — with Houston Methodist, Emory, and Intermountain Health deploying now. Real results: 39% more diagnoses. 4 days earlier detection. $11M/year ROI for our first site at Cedars Sinai. 7× revenue growth in 7 months. We started with malnutrition. We're expanding to every major condition to ensure no patient falls through the cracks. Sequoia and First Round are backing us to build the platform that screens every patient for everything and drives tangible outcomes. At HealthLeap, you'll ask the hard questions about hospital care. Who gets missed, and for which conditions? What actually changes outcomes? Where does screening help, and where doesn't it? You'll run the statistical analyses that test whether screening every patient really changes their trajectory, look hard at the results, and figure out where we can do better. That work supports our partners and our go-to-market efforts, and it can shape a product that clinicians use every day. You'll be early enough to build the research agenda from scratch, but late enough to know the product already works. You'll also have a lot to work with: EHR data from 40+ hospitals, hundreds of thousands of patients, real deployments, and your pick of health system partners. You’ll get support from, and work closely with, our data science and engineering teams, who know the data inside and out. You might be a good fit if you're curious, care about impact, and want to do applied data science. It helps if you like turning messy observational hospital data into results people actually cite, and if you're excited by the speed of startups!

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.