About Sprinter Health:
At Sprinter Health, our mission is reimagining how people access care by bringing it directly to their homes. Nearly 30% of patients in the U.S. skip preventive or chronic care simply because they can’t get to a doctor’s office. For many, the ER becomes their first touchpoint with the healthcare system—driving over $300B in avoidable costs every year.
By using the same technologies that power leading marketplace and last-mile platforms, we deliver care where people are, especially those who need it most. So far, we’ve supported more than 2 million patients across 22 states, completed 130,000+ in-home visits, and maintained a 92 NPS. Our team of clinicians, technologists, and operators have raised over $125M to date investors like a16z, General Catalyst, GV, and Accel and enjoy multi-year runway.
About the Role
We’re looking for a Data Scientist with an actuarial focus to quantify the long-term economic value of what Sprinter does. When we tell a health plan that finding and treating disease in the home lowers their cost of care over time, someone has to turn that claim into numbers the plan’s actuaries will trust. That is this role.
You will build the models yourself: total-cost-of-care and per-member-per-month (PMPM) projections from claims, medical-loss-ratio (MLR) impact, and a defensible account of how Sprinter’s interventions change cost and risk over a multi-year horizon. You will package those outputs so a customer’s actuarial team can plug them straight into their pricing, reserving, and bid work.
This is a hands-on, applied role, not an advisory one — we would rather you build the table than write a memo about it. The ideal candidate is a quantitative scientist who thinks like an actuary and is comfortable talking actuary-to-actuary with a payer’s medical-economics team. Formal actuarial credentials are welcome but not required; we care that you can do the work and defend it. This is a first-of-function role; you will define what good looks like.
Hybrid & Office Experience
We operate on a hybrid schedule, working from the office Monday through Thursday, with Fridays designated as work-from-anywhere days.
We care deeply about work-life balance and are happy to provide flexibility when life happens. We ask that employees be in the office Monday through Thursday to collaborate with their teams while maintaining flexibility where it matters most.
Lunch is provided every day, and the entire team takes an hour to eat together. It's one of the ways we stay connected outside of meetings. You'll usually find us playing a board game before getting back to work.
What you will do:
Build total-cost-of-care, PMPM, and MLR models from claims data to quantify the long-term impact of Sprinter’s programs.
Project how interventions change cost, utilization, and risk over multi-year horizons, and quantify the uncertainty around those projections.
Produce model outputs and tables that a payer’s actuaries can plug directly into their pricing, reserving, and bid work.
Represent Sprinter in MLR and medical-economics conversations with health plans; go toe-to-toe with their actuaries.
Turn analysis into value narratives that quality, risk, and finance teams can act on.
Help the commercial team price and sell Sprinter’s impact on an actuarial basis.
Define the yardstick for whether an intervention actually changed cost and outcomes, not just whether it correlated with them.
Partner with Data Science on the causal and experimental design behind those measurements.
Bring an honest view of the line between value we can prove and value we can only assert.
What you have done:
Deep experience building actuarial or health-economic models from administrative claims: total cost of care, PMPM, utilization, trend, and risk.
Command of the methods payers price on — MLR, risk adjustment, and multi-year projection — and the judgment to know their limits.
Strong SQL and Python or R, with the ability to build and own your models end to end.
Ability to hold your own with actuaries and medical-economics teams, and to explain the analysis to non-technical stakeholders.
Honesty about causal inference — what a given design can and cannot claim.
What gives you an edge:
Actuarial credentials (ASA, FSA, MAAA, or actuarial exam progress) — welcome but not required.
Payer-side, value-based-care, or risk-bearing experience; familiarity with Medicare Advantage, Stars, and risk adjustment.
Experience producing analysis that a customer or partner built into their own pricing or reserving.
Fluency with AI coding assistants (e.g., Claude Code, Cursor) in your day-to-day development workflow.
Interview Process:
We aim to complete the interview process between 2–3 weeks. It will usually consist of:
Recruiter Screen (30 minutes)
Hiring Manager Introduction (30 minutes)
Hands-on-Keys Technical Assessment (1 hour)
Onsite Interview: Systems Design / Technical Case Study + Research Presentation + Behavioral Interview + Lunch with the Team (4 hours)
References
What we offer:
Meaningful pre-IPO equity
Medical, dental, and vision plans 100% paid for you and your dependents
Flexible PTO + 10 paid holidays per year
401(k) with match
16-week parental leave policy for birthing parent, 8 weeks for all other parents
HSA + FSA contributions
Life insurance, plus short and long-term disability coverage
Free daily lunch in-office
Annual learning stipend
United States