Data Scientist

• Build reusable Python modules for:
• Data preparation
• Feature engineering
• Model training
• Hyperparameter tuning
• Model evaluation
• Model scoring
• Model validation
• Develop configurable workflows to reduce model development turnaround time.
• Build end-to-end automated pipelines.
• Develop scalable ETL/ELT workflows using Python and PySpark.
• Automate data ingestion, transformation, validation, and feature generation processes.
• Implement metadata-driven and reusable pipeline architectures.

MLOps & Deployment

• Develop and maintain CI/CD pipelines for ML solutions.
• Package and deploy models as APIs, batch services, or containerized applications.
• Integrate models with underwriting, claims, and pricing systems.

Implement model versioning, model registry integration, and release management processes

Bachelor's/Master's in Engineering 3-6 years