Description

You will design, develop, and deploy machine learning models across risk assessment, pricing, claims analytics, and portfolio optimization.

Responsibilities

• Design, develop, and deploy ML models for risk assessment, pricing, claims analytics, and portfolio optimization.
• Lead data exploration, feature engineering, and model validation to ensure accuracy.
• Build and optimize data pipelines and ETL processes for large-scale analytics.
• Deploy and operationalize models on AWS or Azure, ensuring scalability.
• Mentor junior data scientists and promote ML best practices.

Required Skills

• 7+ years of experience in data science or machine learning engineering.
• Strong proficiency in Python, SQL, Pandas, NumPy, and Scikit-learn.
• Extensive experience with statistical modeling and ML techniques.
• Hands-on experience with data visualization tools like Power BI or Tableau.
• 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 risk assessment.
• Excellent ability to translate technical insights into business value.
• Familiarity with CI/CD Pipelines.

Key Skills

Education

Bachelor's degree