Risk & Actuarial AI Expert
New
W
Weekday AIInsurance, Risk Management
US, UK, Canada, France, Portugal (remote)ContractMiddle
Salary not disclosed
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Job Details
- Experience
- 2–8 years
- Required Skills
- PythonArtificial IntelligenceMachine LearningData visualizationR
Requirements
- Bachelor’s or Master’s degree in Actuarial Science, Mathematics, Statistics, Data Science, or a related field
- Progress toward actuarial certification (e.g., IFoA, SOA, or equivalent) preferred
- 2–8 years of experience in actuarial analysis, risk management, or insurance analytics
- Strong expertise in loss ratio and combined ratio analysis
- Proven experience in portfolio risk assessment and risk modeling
- Hands-on experience with catastrophe modeling tools and exposure management frameworks
- Proficiency in programming (Python/R)
- Proficiency in data visualization tools
- Familiarity with machine learning techniques and their application in insurance
- Strong problem-solving skills
- Ability to communicate complex insights to non-technical stakeholders
Responsibilities
- Evaluate and optimize portfolio performance through detailed loss ratio and combined ratio analysis
- Monitor trends, identify deviations, and provide recommendations to improve underwriting profitability
- Conduct comprehensive portfolio risk assessments using statistical models and AI-driven techniques
- Identify risk concentrations, assess diversification, and support strategic decisions related to risk selection and capital allocation
- Collaborate closely with underwriting, finance, and product teams to ensure alignment between risk appetite and business objectives
- Work with catastrophe models and geospatial data to assess potential losses from natural disasters and extreme events
- Enhance traditional modeling approaches using machine learning techniques to improve prediction accuracy and scenario analysis
- Contribute to stress testing, scenario planning, and regulatory reporting requirements
- Leverage modern AI/ML techniques to automate actuarial workflows, improve predictive modeling, and uncover hidden patterns in large datasets
- Design and implement models that enhance pricing, reserving, and risk selection processes
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