Risk & Actuarial AI Expert

Weekday AI (YC W21); France; Sur place; EUR 117 462 - 140 954; Temps partiel

Résumé du poste

Weekday AI (YC W21) is seeking a Risk & Actuarial AI Expert to analyze risk and optimize portfolio performance. This part-time position, open to remote work from France, requires a strong foundation in actuarial science, risk management, and data science. Ideal candidates will have experience in portfolio risk assessment and catastrophe modeling. Proficiency in Python and machine learning techniques is essential. Join us in leveraging AI to enhance decision-making in insurance and risk portfolios.

Qualifications

• 2–8 years of experience in actuarial analysis, risk management, or insurance analytics.
• Progress toward actuarial certification (e.g., IFoA, SOA, or equivalent) preferred.
• Strong problem-solving skills and ability to communicate complex insights to non-technical stakeholders.

Responsabilités

• Evaluate and optimize portfolio performance through loss ratio and combined ratio analysis.
• Conduct comprehensive portfolio risk assessments using statistical models and AI-driven techniques.
• Focus on catastrophe modeling and exposure management using data and models.

Connaissances

Actuarial analysis

Risk management

Data science

Machine learning

Python

R

Data visualization

Formation

Bachelor's or Master's degree in Actuarial Science, Mathematics, Statistics, Data Science

Outils

Catastrophe modeling tools

Description du poste

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)

Overview

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.

Key Responsibilities

• 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.

• 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. Collaborate closely with underwriting, finance, and product teams to ensure alignment between risk appetite and business objectives.

• Focus on catastrophe modeling and exposure management. 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. Experience with tools such as Python, R, and cloud‑based analytics platforms will be valuable.

Qualifications & Skills

• 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

What We're Looking For

We value individuals who combine technical rigor with business intuition. You should be comfortable working in a dynamic environment, handling ambiguity, and driving innovation through data. A proactive mindset, attention to detail, and the ability to translate analytical findings into strategic recommendations will set you apart in this role.