AI ML Engineer

We are seeking a highly experienced Senior AI/ML Engineer with strong data science expertise to support advanced analytics initiatives within the reinsurance domain. This role focuses on leveraging machine learning, statistical modeling, and large-scale data pipelines to drive insights in risk modeling, pricing strategies, claims analysis, and portfolio optimization. The ideal candidate combines deep technical expertise with strong business acumen and the ability to communicate complex analytical insights to diverse stakeholders.

• Design, develop, and deploy machine learning models for risk assessment, pricing, claims analytics, and portfolio optimization.
• Apply advanced statistical and ML techniques including regression, classification, clustering, and time-series forecasting.
• Lead data exploration, feature engineering, and model validation efforts to ensure accuracy and reliability.
• Build and optimize data pipelines and ETL processes to support large-scale analytics and model deployment.
• Develop interactive dashboards and visualizations using Power BI, Tableau, or Python libraries to communicate insights.
• Collaborate closely with actuarial, underwriting, claims, and IT teams to align analytics with business objectives.
• Deploy and operationalize models on cloud platforms (AWS or Azure), ensuring scalability and performance.
• Ensure data quality, governance, and compliance with industry and regulatory standards.
• Mentor junior data scientists and promote best practices in ML, analytics, and data engineering.
• Stay current with emerging AI/ML techniques and reinsurance industry trends.

• 10+ years of experience in data science, machine learning, or AI engineering roles.
• Strong proficiency in Python, SQL, Pandas, Num Py, and Scikit-learn.
• Extensive experience with statistical modeling and machine learning techniques.
• Hands-on experience with data visualization tools such as Power BI, Tableau, Matplotlib, or Seaborn.
• Working knowledge of big data technologies (Spark, Hadoop).
• Experience deploying data pipelines and ML models on AWS or Azure.
• Solid understanding of insurance/reinsurance concepts, including actuarial models, risk assessment, and claims analytics.
• Excellent communication skills with the ability to translate technical insights into business value.

• Experience with R or SAS.
• Exposure to NLP, geospatial analytics, Monte Carlo simulations, or stochastic modeling.
• Familiarity with CI/CD pipelines, Git, and MLOps practices.
• Knowledge of regulatory frameworks such as Solvency II and IFRS 17.

• Strong problem-solving and analytical thinking capabilities.
• Ability to manage multiple initiatives and prioritize effectively.
• Collaborative mindset and passion for continuous learning.
• High attention to detail and data integrity.