Description

Lead data science operations, manage a distributed team, and architect scalable ML pipelines on AWS and Databricks.

This role is on-site.

Responsibilities

• Lead and mentor a team of 6+ data scientists and ML engineers across onshore/offshore locations.
• Architect and manage AWS Data Lake solutions and big data processing pipelines at petabyte scale.
• Drive MLOps maturity, including CI/CD for ML, automated retraining, drift detection, and performance monitoring.
• Define data science strategy and governance standards for model development, deployment, and responsible AI.
• Partner with stakeholders to translate business objectives into actionable AI/ML project plans.

Required Skills

• 12+ years of experience in data science or ML engineering, including 3–4 years in leadership.
• Expert-level AWS Cloud skills: SageMaker, S3, EMR, Glue, Lambda, Redshift, Athena, Lake Formation.
• Expert proficiency in Databricks for data engineering and MLflow/Unity Catalog for governance.
• Strong experience with Apache Spark, Python, and SQL for large-scale data processing.
• Proven track record delivering production-grade ML solutions in multinational organizations.
• Experience managing distributed teams and coordinating onshore/offshore delivery models.
• Deep understanding of ML lifecycle: feature engineering, model training, validation, and inference.

Preferred Skills

• Experience with Generative AI, including LLMs, RAG architectures, and prompt engineering.
• Background in advanced analytics techniques such as NLP, time-series forecasting, or deep learning.

Key Skills

Education

Any Graduate