We are seeking a highly skilled AI/ML Engineer to join our team within the reinsurance domain. This role involves leveraging machine learning, data engineering, and advanced analytics to support key business functions such as risk modeling, pricing strategies, claims analysis, and portfolio optimization. The ideal candidate will combine technical expertise with strong business acumen to provide actionable insights while effectively communicating findings to non-technical stakeholders.
Responsibilities
- Develop and implement machine learning models for risk assessment, pricing, claims analysis, and portfolio optimization.
- Design, build, and manage data pipelines using technologies such as Spark, Hadoop, and cloud platforms (Azure/AWS) to process large datasets.
- Conduct predictive modeling, time-series forecasting, and statistical analysis to enhance business decision-making.
- Create compelling data visualizations using Power BI, Tableau, Matplotlib, and Seaborn to communicate insights effectively to business stakeholders.
- Work closely with cross-functional teams, including actuarial, underwriting, and IT, to drive data-driven decisions and business strategies.
- Stay updated on the latest trends in AI/ML and the reinsurance industry, and apply this knowledge to improve processes and methodologies.
- Understand and apply relevant regulatory frameworks (e.g., Solvency II, IFRS 17) in the context of risk analysis and data modeling.
Qualifications
- Bachelor's or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, or related field
- 3-5 years of experience in AI/ML, data science, or machine learning engineering, ideally within the reinsurance or insurance industry
- Experience with actuarial models, risk assessment, and claims analytics is a plus
- Exposure to regulatory frameworks such as Solvency II and IFRS 17 is advantageous
Required Skills
- Programming: Strong proficiency in Python and SQL
- Data Science Libraries: Experience with Pandas, NumPy, Scikit-learn
- Machine Learning Techniques: Regression, Classification, Clustering, Time Series Forecasting
- Data Visualization: Power BI, Tableau, Matplotlib, Seaborn
- Big Data Technologies: Spark, Hadoop (basic understanding)
- Cloud Platforms: Proficiency in Azure or AWS, specifically for model deployment and data pipelines
- Data Engineering: Experience with ETL processes, feature engineering, and data wrangling
Preferred Skills
- R/SAS for advanced statistical analysis
- Familiarity with Natural Language Processing (NLP)
- Knowledge of geospatial data and mapping tools
- Experience with Monte Carlo simulations and stochastic modeling
- Familiarity with Git and CI/CD pipelines for version control and automated deployment