Actuarial Data Science Lead

Shepherd
San Francisco
Workplace: OnsiteFull timeUSD 200,000 - 240,000 annuallyFunction: Data Science & Machine LearningExperience: 7+ yearsSkills: ["Communication","Stakeholder collaboration","Ownership","Analytical thinking"]

Own end-to-end predictive pricing models for commercial auto, starting with feature development through deployment and ongoing iteration as new data sources arrive. Build and deploy loss cost models and data-ready feature pipelines, then validate and monitor model drift, performance, and calibration over time. Run experiments and back-tests to quantify impact on loss ratios and pricing accuracy, working closely with actuaries, underwriters, and engineers to deliver reliable decisions.

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FursaFursa
Shepherd
Shepherd
1 month ago

Actuarial Data Science Lead

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

Source: Company careers pageValidated by: Fursa AI
Last checked: 17 hours agoStatus: Live

Job Summary

Own end-to-end predictive pricing models for commercial auto, starting with feature development through deployment and ongoing iteration as new data sources arrive. Build and deploy loss cost models and data-ready feature pipelines, then validate and monitor model drift, performance, and calibration over time. Run experiments and back-tests to quantify impact on loss ratios and pricing accuracy, working closely with actuaries, underwriters, and engineers to deliver reliable decisions.
Location: San Francisco
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning

Key Responsibilities

  • •Own commercial auto pricing models end-to-end from feature development through deployment and iteration as the book grows.
  • •Build and deploy predictive loss cost models that set pricing for Shepherd’s commercial auto book.
  • •Design and maintain feature pipelines that transform submission, claims, and third-party data into model-ready inputs.
  • •Collaborate with actuaries and underwriters to translate domain expertise into model features and validate outputs.
  • •Develop model monitoring frameworks and run experiments/back-tests to measure drift, calibration, and impact on loss ratios and pricing accuracy.

Pay and Benefits

Salary: USD 200,000 - 240,000 annually
Equity and Bonus:Equity
Perks:Health InsuranceDentalVisionPaid Leave401kLearning Budget

Key Requirements

  • •7+ years building and deploying personal auto or commercial lines predictive pricing models in production.
  • •Strong foundation in statistics including GLMs, GBDTs, time series analysis, heavy tail distributions, and Bayesian methods.
  • •Proficiency in Python and SQL.
  • •Familiarity with actuarial concepts such as loss development, exposure rating, and credibility.
  • •ACAS/FCAS actuarial designation and experience with feature engineering on messy, real-world small data.
Experience:7+ yearsInsuranceInsurtechFintechRegulated industriesTelemetics
Skills:CommunicationStakeholder collaborationOwnershipAnalytical thinking
Certifications:ACASFCAS
Tech Stack:PythonSQLLLMsAI toolsAWS

Company Brief

Shepherd
Provides a digital platform to design, run, and scale decentralized clinical studies, helping researchers recruit participants, collect remote data, and streamline study operations for academic and commercial research teams.
Industry: Clinical Research
Company Size: Small (11 to 50 employees)
Growth: Early Stage Startup
Website