Description

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

This role is hybrid.

Responsibilities

• Build and optimize data pipelines and ETL processes to support large-scale analytics and model deployment.
• Apply statistical and ML techniques including regression, classification, clustering, and time-series forecasting.
• Lead data exploration, feature engineering, and model validation to ensure accuracy and reliability.
• Deploy and operationalize models on cloud platforms (AWS or Azure), ensuring scalability and performance.
• Develop interactive dashboards and visualizations using Power BI, Tableau, or Python libraries to communicate insights.

Required Skills

• 10+ years of experience in data science, machine learning, or AI engineering roles.
• 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 (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.
• Bachelor's degree.

Preferred Skills

• 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.

Key Skills

Education

Bachelor's degree