Actuary / Insurance Risk Analytics Specialist
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- Model loss uncertainty and connect pricing with reserving
- Price portfolios, review rates and forecast profitability
- Validate modern pricing models and influence high-stakes risk decisions
Benchmark: Swiss Re Pricing Actuary + AXA XL Middle Market Actuarial + Liberty Mutual Actuarial (current Sep 2026) Market refresh: pricing + reserving, frequency-severity and GLM modeling, rate reviews, portfolio profitability, forecasting/scenarios, predictive modeling, model validation/fairness, R/Python/SQL and decision-ready partnership with underwriting, finance and leadership.
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Model loss uncertainty and connect pricing with reserving
Loss distributions, conditional probability + simulation
Coverage: Statistics & Probability / Actuarial Risk Modeling
Frequency-severity pricing + reserving foundations
Coverage: Actuarial Science & Risk Modeling / Insurance Analytics
Price portfolios, review rates and forecast profitability
GLM pricing, rate adequacy + predictive modeling
Coverage: Statistical Modeling / Actuarial Pricing / Python-R-SQL
Forecasting, trend + portfolio profitability
Coverage: Forecasting / Portfolio Analytics / Scenario Analysis
Validate modern pricing models and influence high-stakes risk decisions
ML pricing, calibration + fairness / model validation
Coverage: Predictive Modeling / Model Risk / Responsible AI
Emerging risk, governance + executive decisions
Coverage: Enterprise Risk / Model Governance / Executive Communication
Role-Readiness Proof / Capstone
Own an end-to-end P&C pricing and reserving case. Clean exposure/claims data in R or Python/SQL; fit frequency and severity assumptions plus a GLM or comparable model; estimate an indicated rate and reserve; validate calibration, segmentation, stability and fairness; forecast trends and stress catastrophe/emerging-risk scenarios; analyze portfolio profitability and rate adequacy; document model governance and regulatory assumptions; present pricing actions, uncertainty and tradeoffs to underwriting and finance.
Proof Rubric
- Show actuarial evidence, not a spreadsheet answer: auditable data checks
- defensible frequency/severity and reserving assumptions
- correct exposure and trend treatment
- GLM/model diagnostics and out-of-sample validation
- calibrated rate indication
- reserve/scenario uncertainty
- portfolio profitability insight
- fairness and model-risk controls
- reproducible R/Python/SQL work
- clear documentation
- translate technical findings into an underwriting or leadership decision with quantified impact and limitations