Data Scientist

On-siteMidML Engineerposted 1mo ago

✦Role summaryAI-generated

This role focuses on designing scalable, reusable Python-based ML pipelines and workflows to accelerate model development, emphasizing automation and metadata-driven architectures. The candidate will bridge data engineering and MLOps, ensuring end-to-end pipeline efficiency and deployment readiness.

Skills required

Python Spark Feature engineering Hyperparameter tuning Ml pipelines Etl elt CI/CD Model validation Automated data ingestion Model evaluation ETL ELT MLOps

About this role

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.

Responsibilities

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