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