P&C Actuary - Risk Management - AI Trainer

Mercor; Dallas (TX); On-site; USD 90,000 - 140,000; Part time

Job summary

Mercor partners with a leading AI lab to train frontier models on high-quality insurance reasoning data. We seek P&C actuaries and portfolio risk managers to design realistic pricing, reserving, forecasting, and portfolio-management scenarios, and to evaluate model outputs against actuarial standards.

We welcome actuaries from carriers, reinsurers, MGAs, and consulting firms. Flexible hours, rolling applications, and collaboration with a research team await the right candidates.

Qualifications

• 2+ years of professional experience in P&C actuarial work, insurance pricing, reserving, catastrophe modeling, or portfolio risk management.
• Able to perform quantitative analysis using insurance premium, exposure, claim, loss, or reserve data.
• Understand the difference between account-level underwriting judgment and portfolio-level actuarial analysis.
• Able to explain methods, assumptions, limitations, and business implications clearly to technical and nontechnical audiences.
• Demonstrate strong quantitative reasoning, excellent written communication, and high attention to detail.

Responsibilities

• Design realistic scenarios involving loss costs, rate indications, trend, development, credibility, segmentation, reserving, profitability, capital, catastrophe exposure, and portfolio concentration.
• Create work products such as pricing analyses, reserve reviews, portfolio diagnostics, assumption critiques, sensitivity analyses, and management recommendations.
• Write gold standard reference responses at experienced actuarial and portfolio-risk quality.
• Grade AI-generated responses against structured rubrics for mathematical accuracy, assumption quality, methodology, interpretation, and communication.
• Identify calculation errors, unsupported assumptions, misuse of actuarial methods, confusing correlation with causation, and unsupported recommendations.
• Provide written feedback the research team uses to improve model behavior.
• Participate in onboarding office hours and calibration sessions.

Skills

Quantitative reasoning

Written communication

Attention to detail

Explain methods to audiences

Tools

SQL

R

Python

SAS

Job description

About the Role

Mercor is partnering with a leading AI lab to train frontier models on high-quality insurance reasoning data. We're hiring P&C Actuaries and Portfolio Risk Managers to design realistic pricing, reserving, forecasting, and portfolio-management scenarios, evaluate model outputs against established actuarial standards, and help shape how the next generation of AI reasons quantitatively about insurance risk.

We welcome pricing actuaries, reserving actuaries, portfolio analysts, catastrophe-risk professionals, and actuarial managers from carriers, reinsurers, MGAs, and consulting firms.

What You'll Do

• Design realistic scenarios involving loss costs, rate indications, trend, development, credibility, segmentation, reserving, profitability, capital, catastrophe exposure, and portfolio concentration
• Create work products such as pricing analyses, reserve reviews, portfolio diagnostics, assumption critiques, sensitivity analyses, and management recommendations
• Write "golden" reference responses at experienced actuarial and portfolio-risk quality
• Grade AI-generated responses against structured rubrics for mathematical accuracy, assumption quality, methodology, interpretation, and communication
• Identify calculation errors, unsupported assumptions, misuse of actuarial methods, confusing correlation with causation, and recommendations not supported by the data
• Provide written feedback the research team uses to improve model behavior
• Participate in onboarding office hours and calibration sessions

You're a Good Fit If You

• Have 2+ years of professional experience in P&C actuarial work, insurance pricing, reserving, catastrophe modeling, or portfolio risk management
• Have performed quantitative analysis using insurance premium, exposure, claim, loss, or reserve data
• Understand the difference between account-level underwriting judgment and portfolio-level actuarial analysis
• Can explain methods, assumptions, limitations, and business implications clearly to technical and nontechnical audiences
• Demonstrate strong quantitative reasoning, excellent written communication, and high attention to detail
• Are proficient with spreadsheets and at least one analytical or statistical tool

Bonus Qualifications

• ACAS, FCAS, or active progress toward CAS credentials
• Experience with personal, commercial, specialty, or reinsurance portfolios
• Catastrophe modeling, capital modeling, predictive modeling, or rate-filing experience
• Proficiency with SQL, R, Python, SAS, or actuarial modeling platforms
• Experience presenting results to underwriting, finance, claims, or executive stakeholders

Role Highlights

• Minimum 20 hours per week (ideally 40+)
• Role starts immediately, applications reviewed on a rolling basis