Sprinter Health

1 day ago

Data Scientist, Actuarial

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Build actuarial and economic models to quantify the long-term impact of home-based care on total cost of care, PMPM, and medical-loss-ratio effects using administrative claims. Develop multi-year projections with uncertainty, and package model outputs so payer actuaries can plug them into pricing, reserving, and bid work. Represent the company in medical-economics and MLR conversations, partnering with data science on causal and experimental measurement design.

Location: San Francisco

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Build total-cost-of-care, PMPM, and MLR models from claims data to quantify program impact.
•Project how interventions change cost, utilization, and risk over multi-year horizons and quantify uncertainty.
•Produce model outputs and tables that payer actuaries can plug into pricing, reserving, and bid work.
•Represent Sprinter in MLR and medical-economics conversations with health plans and support commercial pricing/sales with actuarial value narratives.
•Define measurement yardsticks, partner with data science on causal/experimental design, and clarify what can be proven vs asserted.

Pay and Benefits

Salary: USD 160,000 - 200,000 annually

Perks:Health InsuranceDentalVision401kLearning Budget

Key Requirements

•Deep experience building actuarial or health-economic models from administrative claims (total cost of care, PMPM, utilization, trend, and risk).
•Strong command of payer pricing methods including MLR, risk adjustment, and multi-year projection (and knowing their limits).
•Strong SQL and Python or R skills, able to build and own models end to end.
•Ability to hold your own with actuaries and medical-economics teams and explain analysis to non-technical stakeholders.
•Honesty about causal inference—what a given design can and cannot claim.

Experience:HealthcareActuarialPayersMedical-economicsRisk adjustmentValue-based care

Skills:Stakeholder communicationCausal inferenceAnalytical rigorQuantitative thinkingDefensibility

Certifications:ASAFSAMAAA

Tech Stack:SQLPythonRClaude CodeCursorAI coding assistants

Company Brief

Sprinter Health

Operates mobile and home-based primary care services focused on delivering in-home medical visits, chronic care management, and coordinated care for patients who face barriers accessing clinic-based care.

Industry: Healthcare Providers

Website

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Sprinter Health

1 day ago

Data Scientist, Actuarial

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Location: San Francisco

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Build total-cost-of-care, PMPM, and MLR models from claims data to quantify program impact.
•Project how interventions change cost, utilization, and risk over multi-year horizons and quantify uncertainty.
•Produce model outputs and tables that payer actuaries can plug into pricing, reserving, and bid work.
•Represent Sprinter in MLR and medical-economics conversations with health plans and support commercial pricing/sales with actuarial value narratives.
•Define measurement yardsticks, partner with data science on causal/experimental design, and clarify what can be proven vs asserted.

Pay and Benefits

Salary: USD 160,000 - 200,000 annually

Perks:Health InsuranceDentalVision401kLearning Budget

Key Requirements

•Deep experience building actuarial or health-economic models from administrative claims (total cost of care, PMPM, utilization, trend, and risk).
•Strong command of payer pricing methods including MLR, risk adjustment, and multi-year projection (and knowing their limits).
•Strong SQL and Python or R skills, able to build and own models end to end.
•Ability to hold your own with actuaries and medical-economics teams and explain analysis to non-technical stakeholders.
•Honesty about causal inference—what a given design can and cannot claim.

Experience:HealthcareActuarialPayersMedical-economicsRisk adjustmentValue-based care

Skills:Stakeholder communicationCausal inferenceAnalytical rigorQuantitative thinkingDefensibility

Certifications:ASAFSAMAAA

Tech Stack:SQLPythonRClaude CodeCursorAI coding assistants

Company Brief

Sprinter Health

Industry: Healthcare Providers

Website