Description

Design, develop, and deploy machine learning models for risk assessment, pricing, claims analytics, and portfolio optimization.

This role is hybrid.

Responsibilities

• Apply regression, classification, clustering, and time-series forecasting to solve complex business problems.
• Lead data exploration, feature engineering, and model validation to ensure accuracy and reliability.
• Build and optimize data pipelines and ETL processes for large-scale analytics and model deployment.
• Deploy and operationalize models on AWS or Azure, ensuring scalability and performance.
• Collaborate with actuarial, underwriting, and IT teams to align analytics with business objectives.

Required Skills

• 10+ years of experience in data science, machine learning, or AI engineering.
• Strong proficiency in Python, SQL, Pandas, NumPy, and Scikit-learn.
• Hands-on experience with data visualization tools such as Power BI, Tableau, Matplotlib, or Seaborn.
• Working knowledge of big data technologies including Spark and Hadoop.
• Experience deploying data pipelines and ML models on AWS or Azure.
• Solid understanding of insurance/reinsurance concepts, including actuarial models and risk assessment.

Preferred Skills

• Experience with R or SAS.
• Exposure to NLP, geospatial analytics, Monte Carlo simulations, or stochastic modeling.

Key Skills

Education

Bachelor's degree