The ad says“Deep experience building actuarial or health-economic models from administrative claims: total cost of care, PMPM, utilization, trend, and risk”
The ad says“Fluency with AI coding assistants (e.g., Claude Code, Cursor) in your day-to-day development workflow”
The ad says“Command of the methods payers price on — MLR, risk adjustment, and multi-year projection — and the judgment to know their limits”
The ad says“Command of the methods payers price on — MLR, risk adjustment, and multi-year projection — and the judgment to know their limits”
The ad says“Strong SQL and Python or R, with the ability to build and own your models end to end”
The ad says“Strong SQL and Python or R, with the ability to build and own your models end to end”
The ad says“Command of the methods payers price on — MLR, risk adjustment, and multi-year projection — and the judgment to know their limits”
The ad says“Strong SQL and Python or R, with the ability to build and own your models end to end”
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
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
Benefits
Free lunch everyday and fully-stocked microkitchens and coffee/tea bar
Team offsites, game hours, tech talks, and design sprints
Health insurance benefits (PPO / HMO / HSA, 100% of family premiums paid)
Dental / vision / wellness benefits
401k matching
Unlimited PTO
Flexible work-from-home policy for work/life balance (4 days in-office, accommodation for occasional WFH days)
Relocation assistance
Generous parental leave (4 months for the birthing parent, 3 months for a partner, 100% paid)
Preferred experience
Command of the methods payers price on — MLR, risk adjustment, and multi-year projection — and the judgment to know their limits
Ability to hold your own with actuaries and medical-economics teams, and to explain the analysis to non-technical stakeholders
Deep experience building actuarial or health-economic models from administrative claims: total cost of care, PMPM, utilization, trend, and risk
Honesty about causal inference — what a given design can and cannot claim
Strong SQL and Python or R, with the ability to build and own your models end to end
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
Recruitment 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)
