Collaborate with business and technical stakeholders to identify data science use cases in the insurance domain.
Build and deploy predictive models, segmentation models, and forecasting solutions to optimize operations such as risk scoring, claim fraud detection, and customer LTV.
Work with large and complex data sets across multiple systems using Databricks, Spark, and cloud data warehouses (BigQuery, Synapse, etc.).
Design, develop, and productionize ML pipelines on GCP, Azure, or Databricks platforms.
Leverage statistical and machine learning methods to derive actionable insights for strategic business decisions.
Communicate findings and recommendations clearly to non-technical stakeholders.
Ensure model performance, governance, and compliance with industry regulations.
Required Skills & Qualifications:
5+ years of experience in data science / advanced analytics, preferably in insurance (life, health, P&C) or financial services.
Proficient in Python, SQL, and data modeling techniques.
Solid experience working on GCP (BigQuery, Vertex AI) or Azure (Data Lake, ML Studio).
Hands-on experience with Databricks for big data processing and model deployment.
Strong understanding of insurance processes such as underwriting, claims, fraud, retention, or pricing.
Experience with ML frameworks like scikit-learn, XGBoost, TensorFlow, etc.
Familiarity with data governance and MLOps practices is a plus.
