Weekday AI
insurance
Risk&ActuarialAIExpert
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“Risk & Actuarial AI Expert at Weekday AI. Skills: actuarial science, risk management, AI/ML techniques, loss ratio analysis, combined ratio analysis, portfolio risk assessment, catastrophe modeling. evaluating and optimizing portfolio performance. monitoring trends”
What You'll Achieve.
transforming complex risk data into actionable insights; improve prediction accuracy and scenario analysis; enhance decision-making across insurance and risk portfolios
Industry & Context.
problem-solving skills; ability to translate analytical findings into strategic recommendations
What They're Looking For.
Must Have
Bachelor’s or Master’s degree in Actuarial Science, Mathematics, Statistics, Data Science, or a related field, 2–8 years of experience in actuarial analysis, risk management, or insurance analytics, 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) and data visualization tools, Familiarity with machine learning techniques and their application in insurance
Nice to Have
Progress toward actuarial certification (e. g. , IFoA, SOA, or equivalent)
What You'll Do.
evaluating and optimizing portfolio performance
identifying deviations
providing recommendations to improve underwriting profitability
conducting comprehensive portfolio risk assessments
supporting strategic decisions related to risk selection and capital allocation
catastrophe modeling and exposure management
assessing potential losses from natural disasters and extreme events
enhancing traditional modeling approaches using machine learning techniques
contributing to stress testing
and regulatory reporting requirements
leveraging modern AI/ML techniques to automate actuarial workflows
improving predictive modeling
uncovering hidden patterns in large datasets
designing and implementing models that enhance pricing
and risk selection processes
How You'll Work.
Team & Collaboration
collaborate closely with underwriting, finance, and product teams to ensure alignment between risk appetite and business objectives
Communication Scope
ability to communicate complex insights to non-technical stakeholders
Full Job Description
**This role is for one of our clients** **Compensation:** $100-$120 per hour (20 hours per week commitment)** Job Type:** Part-time / Contract **Location:** US, UK, Canada, France, Portugal (remote) We are seeking a highly analytical and forward-thinking Risk & Actuarial AI Expert to join our growing team. This role sits at the intersection of actuarial science, risk management, and advanced analytics, leveraging artificial intelligence to enhance decision-making across insurance and risk portfolios. The ideal candidate will bring a strong foundation in actuarial principles combined with hands-on experience in data science, enabling the transformation of complex risk data into actionable insights. **Requirements** **Key Responsibilities:** You will play a central role in evaluating and optimizing portfolio performance through detailed **loss ratio and combined ratio analysis**. This includes monitoring trends, identifying deviations, and providing recommendations to improve underwriting profitability. A deep understanding of claims behavior, pricing adequacy, and expense structures will be critical to success in this area. In addition, you will conduct comprehensive **portfolio risk assessments** , using statistical models and AI-driven techniques to evaluate exposure across various lines of business. This involves identifying risk concentrations, assessing diversification, and supporting strategic decisions related to risk selection and capital allocation. You will collaborate closely with underwriting, finance, and product teams to ensure alignment between risk appetite and business objectives. A significant part of the role will focus on **catastrophe modeling and exposure management**. You will work with catastrophe models and geospatial data to assess potential losses from natural disasters and extreme events. Enhancing traditional modeling approaches using machine learning techniques to improve prediction accuracy and scenario analysis will be a key expectatio
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