Sr Data Scientist

Overview

JD – Senior Data Scientist (5B Grade)

Summary:

We are seeking a seasoned Senior Data Scientist with overall 10-12 yrs and at least 5-7 years of hands-on experience in developing GenAI/machine learning models and deploying them in a cloud environment, preferably on Google Cloud Platform (GCP). The ideal candidate will design microservice-based solutions, containerize deployments (e.g., GKE), and drive end-to-end SDLC practices. Experience in the pharma domain is a strong advantage.

• Lead end-to-end development of GenAI/ML models: problem framing, data preparation, model selection, training, evaluation, and iteration.
• Architect and implement microservice-based AI solutions and deploy them in containerized environments (preferably GKE); define APIs and data contracts.
• Incorporate and operationalize defined ML pipelines with MLOps practices: model versioning, feature stores, experiment tracking, CI/CD for ML, monitoring, and rollback strategies.
• Leverage GCP offerings (Vertex AI, Big Query, Dataflow, Cloud Storage, Pub/Sub, Cloud Run, GKE, etc.) to design scalable AI solutions and efficient data workflows.
• Knowledge of Retrieval-Augmented Generation (RAG) concepts and processes
• Proficiency with Google Cloud Platform (GCP) and its AI/ML offerings (e.g., Vertex AI, Big Query, Dataflow, Cloud Storage, GKE).
• Deploy, monitor, and maintain models in production; implement observability (logs, metrics, tracing), cost optimization, and performance tuning.
• Collaborate with cross-functional teams (data engineers, software engineers, product, regulatory/compliance, analytics) to translate business needs into robust ML solutions.
• Uphold SDLC standards: requirements gathering, design, development, testing, deployment, maintenance, and documentation; promote reusable patterns and best practices.
• Mentor and guide junior scientists; contribute to code reviews, standards, and knowledge sharing.
• Stay current with GenAI advancements and evaluate new tools/approaches; produce reproducible experiments and artifacts.

• Overall 10-12 yrs and Minimum 5-7 years of hands-on experience developing GenAI/ML models and deploying them in a cloud environment.
• Proficiency with Google Cloud Platform (GCP) and its AI/ML offerings (e.g., Vertex AI, Big Query, Dataflow, Cloud Storage, Pub/Sub, Cloud Run, GKE).