Data Scientist

Posted yesterday

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.

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

Skills