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Open nowPosted 17 days ago

Insurance Domain Expert

Workable (global search)108,016 open roles

Where
California City, CA, United States
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Your applicationOpen nowInsurance Domain ExpertWorkable (global search) · California City, CA, United States

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Early applications get read.

7.9% of postings close within 7 days. Measured by our own scanner across the market. Workable (global search) postings stay open a median of 7 days.

Share of postings closed within
  1. 1.6%1 day
  2. 3.6%3 days
  3. 7.9%7 days
  4. 14.9%14 days
  5. 34.0%30 days
This job: posted 17 days ago

Workable (global search) median: 7 days open

The posting

This role is for one of our clients

Compensation: $60-$100 per hour

We are hiring a senior insurance and actuarial domain expert to work directly with a leading AI lab's research and program management teams, improving how frontier AI models reason about real insurance work.

Your insurance expertise is the substance of this role. You will review the quality of insurance knowledge work tasks, write the instruction specs and golden solutions that define what "correct" looks like, and build the benchmarks that show whether the model is genuinely improving. We are looking for a practicing specialist rather than a generalist.

Location: This is a hybrid role based in the Bay Area, California. You must live in the Bay Area and work on-site with the client's team multiple days each week, when required. This is not a remote role. If you do not currently live in the Bay Area, you must be willing to relocate there at your own cost before the engagement starts. Relocation assistance is not provided.

Requirements

Key Responsibilities

  • Data QA and reviews: Vet the quality of insurance knowledge work tasks and model outputs, spotting missing behaviors, thin reasoning, flawed actuarial assumptions, misapplied policy language, and answers that read well but would not survive professional scrutiny.
  • Instruction specs and golden datasets: Write high-quality instruction specs, produce golden solutions to insurance and actuarial problems, and define new tasks that reflect how the work is actually done in practice.
  • Benchmarks and domain depth: Design challenging insurance tasks and evaluation sets, and help build domain-specific skills and tools together with the research team.
  • Calibration: Work with client researchers and specialists in adjacent fields to keep standards consistent, translating tacit insurance judgment into explicit, teachable criteria.

Core Qualifications

  • Credentials: Either an actuarial credential (FSA, ASA, FCAS, or ACAS) or an active state insurance license in adjusting, underwriting, or broking, for example an all-lines adjuster license. Actuarial candidates are expected to hold a degree in actuarial science, mathematics, statistics, or a related quantitative field.
  • Experience: 4+ years of substantive insurance experience at a carrier, reinsurer, broker, adjusting firm, or insurance regulator.
  • Domain depth: Genuine specialization in at least one area, for example life and annuity, group life and disability, property and casualty, health, reinsurance, pricing and reserving, enterprise risk management, claims adjudication, or underwriting.
  • Seniority: Clear progression to a senior level, for example Actuary, Senior Actuary, AVP, Director, Chief Actuary, Claims Manager, or Head of Underwriting, with real ownership of a book, a reserve, or a claims portfolio.
  • AI fluency: Hands-on working use of large language models in your professional work, and the judgment to tell a well-reasoned answer from a plausible-sounding wrong one.
  • Availability: Able to commit reliably to 40 hours per week for an initial engagement of 6 months.
  • Location: Living in the Bay Area, California, and able to work on-site with the client's team multiple days each week, when required. Candidates not currently based in the Bay Area must be willing to relocate there at their own cost. Relocation assistance is not provided.
  • Excellent written communication, and the ability to give precise, well-structured written feedback.
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