AI ML Engineer

Data Scientist

We are seeking a highly analytical and detail-oriented Data Scientist to join our team in the reinsurance domain. The ideal candidate will leverage data to drive insights into risk modeling, pricing strategies, claims analysis, and portfolio optimization. This role requires strong technical expertise, business acumen, and the ability to communicate complex findings clearly.

Technical Skills Primary

• Programming Languages: Python, Pandas, NumPy, Scikitlearn, SQL
• Statistical Modeling: Machine Learning, Regression, Classification, Clustering, Time Series Forecasting
• Data Visualization: Power BI, Tableau, Matplotlib, Seaborn
• Big Data Technologies: Spark, Hadoop (basic understanding)
• Cloud Platforms: Azure or AWS (especially for data pipelines and model deployment)
• Data Engineering: ETL processes, data wrangling, and feature engineering
• Insurance/Reinsurance Domain Knowledge: Exposure to actuarial models, risk assessment, and claims analytics

Technical Skills Secondary Nice to Have NOT MANDATORY

• R or SAS for statistical analysis
• Experience with Natural Language Processing (NLP)
• Familiarity with geospatial data and mapping tools
• Knowledge of Monte Carlo simulations and stochastic modeling
• Experience with Git and CI/CD pipelines
• Exposure to regulatory frameworks Solvency II, IFRS 17

Soft Skills

• Strong problem solving and critical thinking abilities
• Excellent communication and storytelling skills for nontechnical stakeholders
• Collaborative mindset with cross-functional teams: actuarial, underwriting, IT
• Ability to manage multiple projects and prioritize effectively
• Curiosity and continuous learning attitude
• High attention to detail and data integrity

Qualifying Questions:

Can you describe a project where you applied machine learning to solve a business problem in the insurance or financial domain?

How do you ensure the quality and reliability of your data before building models?

Have you worked with actuarial teams or underwriting departments before? If yes, how did you contribute to their decision-making process?