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 environments 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