Key Responsibilities
Model Development: Design, train, and validate supervised and unsupervised machine learning models (e.g., regression, classification, clustering, time-series forecasting, NLP, or deep learning) using Python.
Full-Stack ML / Deployment: Own model deployment into production settings via REST APIs, microservices, or cloud-native pipelines (AWS, Azure, or GCP).
MLOps & Pipelines: Build and maintain reproducible data and feature pipelines using tools like PySpark, Airflow, MLflow, or Databricks.
Containerization & Version Control: Version control code and model artifacts (Git, DVC, or MLflow) and package applications using Docker.
Monitoring & Optimization: Implement model drift detection, logging, and performance monitoring to ensure high availability and accuracy in production.
Stakeholder Collaboration: Translate complex business problems into quantitative metrics and present actionable insights to cross-functional product and engineering teams.

📌 Data Scientist Machine Learning Gurugram
🏢 EXL
📍 Gurugram