About Healthleap

Every day, millions of hospitalized patients who need intervention are missed because clinicians simply can't see everything. HealthLeap is building the AI operating system that helps care teams identify these missed patients, enabling them to improve health outcomes and generate millions of dollars. HealthLeap is changing what is possible: closing gaps that traditional workflows and clinician capacity could never.

Over the past year, we've grown contracted revenue more than 13x, expanded rapidly across leading health systems, and now help care teams identify patients across millions of inpatient encounters.

We're ~25 people. >$32M raised. SF-based, hybrid-friendly. And, we're delivering results that are changing lives.

About the role

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!

Where this goes

You'll be our first dedicated research hire, which means you get to help set research priorities for HealthLeap and own your research portfolio. Year one will focus on running outcomes studies and driving two studies to publication, but you will have the opportunity to shape the research team and grow with the function.

What you'll do

• 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.

What you'll need

• 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.

Bonus

• 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.

Compensation and benefits

• Salary: $170,000 to $215,000.

• Equity: meaningful ownership in an early-stage company.

• Healthcare: 100% of premiums covered.

• PTO: unlimited, with a recommended minimum of 20 days.

• 401(k): 4% match.

• Equipment: laptop plus a home office budget.

Interview process

• Intro call: get to know each other.

• Technical: one or two interviews on your methods and past work.

• Onsite: technical assessment, case study presentation, behavioral interview, meet the team.

• Decision: same week as onsite. We respect your time. If there's a fit, you'll know fast!

If you're passionate about applying frontier AI to real-world impact, join us in building healthcare's future.