AI ML Engineer

Key Responsibilities

• 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.

Required Qualifications

• 10+ years of experience in data science, machine learning, or AI engineering roles.
• Strong proficiency in Python, SQL, Pandas, NumPy, 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.

Preferred Qualifications

• 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.

Soft Skills

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