Description

You will design, develop, and deploy machine learning models for risk assessment, pricing, claims analytics, and portfolio optimization in a hybrid role combining data science, engineering, and business analytics.

Responsibilities

• Design and implement statistical and ML techniques including regression, classification, clustering, and time-series forecasting.
• Build and optimize data pipelines and ETL processes to support large-scale analytics and model deployment on AWS or Azure.
• Lead data exploration, feature engineering, and model validation to ensure accuracy and reliability of outputs.
• Develop interactive dashboards and visualizations using Power BI, Tableau, or Python libraries to communicate insights to stakeholders.
• Mentor junior data scientists and promote best practices in ML, analytics, and data engineering while ensuring regulatory compliance.

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 big data technologies such as Spark and Hadoop.
• Proven experience deploying data pipelines and ML models on cloud platforms (AWS or Azure).
• Experience with data visualization tools including Power BI, Tableau, Matplotlib, or Seaborn.
• Solid understanding of insurance/reinsurance concepts, including actuarial models, risk assessment, and claims analytics.
• Ability to translate technical insights into business value through excellent communication.

Preferred Skills

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

Key Skills

Education

Any Gradute