# Data Scientist, Actuarial

**Company:** [Sprinter Health](https://hotfix.jobs/companies/sprinter-health)
**Location:** San Francisco, CA
**Role:** Data Science
**Salary:** $160k – $200k/yr
**Experience:** 5+ years
**Skills:** SQL, Python, R, actuarial modeling, claims data analysis, total cost of care, pmpm, mlr, risk adjustment, Causal Inference, medicare advantage
**Posted:** 2026-07-27

> Build actuarial models (total-cost-of-care, PMPM, MLR) from claims data to quantify Sprinter Health's long-term economic impact on payers. Partner with health plan actuaries, support commercial pricing, and ensure rigorous causal measurement of in-home care interventions.

## Job Description

## What you will do

### Actuarial & Economic Modeling
- 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.

### Payer Credibility & Commercial Support
- 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.

### Measurement & Rigor
- 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.

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