Risk & Actuarial AI Expert

Role Description

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.

• 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.
• Focus on catastrophe modeling and exposure management using catastrophe models and geospatial data.
• 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.

Qualifications

• 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) and data visualization tools.
• Familiarity with machine learning techniques and their application in insurance.
• Strong problem-solving skills and ability to communicate complex insights to non-technical stakeholders.

Requirements

• Comfortable working in a dynamic environment, handling ambiguity, and driving innovation through data.
• Proactive mindset and attention to detail.
• Ability to translate analytical findings into strategic recommendations.