P&C Actuary & Portfolio Risk Manager
New
J
JobgetherInsurance, AI
CanadaFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 2+ years
- Required Skills
- SQLData AnalysisR
Requirements
- 2+ years of professional experience in P&C actuarial work, insurance pricing, reserving, catastrophe modeling, portfolio risk management, or a closely related discipline.
- Demonstrated experience performing quantitative analysis using premium, exposure, claims, loss, reserve, or other insurance datasets.
- Strong understanding of actuarial methods and the ability to evaluate assumptions, methodologies, limitations, and business implications.
- Ability to clearly explain complex quantitative concepts to both technical and nontechnical audiences through strong written and verbal communication.
- Excellent quantitative reasoning, analytical judgment, attention to detail, and ability to identify inconsistencies or unsupported conclusions.
- Proficiency with spreadsheets and at least one analytical, statistical, or actuarial modeling tool.
- Ability to work independently while maintaining rigorous quality standards and meeting defined deliverables.
- ACAS, FCAS, or active progress toward CAS credentials (preferred).
- Experience with personal, commercial, specialty, or reinsurance portfolios (preferred).
- Background in catastrophe modeling, capital modeling, predictive modeling, or rate filings (preferred).
- Experience with SQL, R, Python, SAS, or actuarial modeling platforms (preferred).
- Experience presenting analytical results to underwriting, finance, claims, or executive stakeholders (preferred).
Responsibilities
- Design realistic P&C insurance scenarios covering loss costs, rate indications, trends, development, credibility, segmentation, reserving, profitability, capital, catastrophe exposure, and portfolio concentration.
- Develop high-quality work products such as pricing analyses, reserve reviews, portfolio diagnostics, assumption critiques, sensitivity analyses, and management recommendations.
- Produce “golden” reference responses that reflect experienced actuarial and portfolio-risk standards.
- Evaluate AI-generated responses using structured criteria for mathematical accuracy, assumptions, methodology, interpretation, and communication quality.
- Identify calculation errors, unsupported assumptions, inappropriate actuarial methodologies, incorrect interpretations of correlation and causation, and recommendations that are not supported by available data.
- Provide clear, detailed written feedback to help research teams improve the quality and reliability of AI-generated insurance reasoning.
- Participate in onboarding office hours, calibration sessions, and related activities to maintain consistency in evaluation standards.
- Apply practical insurance judgment to distinguish account-level underwriting considerations from broader portfolio-level actuarial and risk-management analysis.
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