Data Scientist - Machine Learning

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.