Data Scientist
Role details
Job location
Tech stack
Requirements
We are seeking a seasoned Senior Data Scientist with overall 10-12 years 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 (Google Cloud Platform).
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.
Required Qualifications
Overall 10-12yrs 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 (Google Cloud Platform) and its AI/ML offerings (e.g., Vertex AI, BigQuery, Dataflow, Cloud Storage, Pub/Sub, Cloud Run, GKE).
Must have experience working with any agentic framework Knowledge of Retrieval-Augmented Generation (RAG) concepts and processes
Strong software engineering skills: Python (primary), experience with ML frameworks (TensorFlow, PyTorch, scikit-learn), and API development (REST/GraphQL).
Experience designing and deploying microservices architectures and containerized solutions (Docker, Kubernetes; preference for GKE).
Solid experience in MLOps: model versioning, experiments, automated training, feature stores, model registries, monitoring, and governance.
Data processing and analytics expertise: SQL, data pipelines, ETL/ELT concepts, data quality, and data visualization support.
Excellent problem-solving, communication, and collaboration skills; ability to work with cross-disciplinary teams.
Understanding of cloud security concepts, IAM, and basic principles of data privacy and compliance.
Demonstrated ability to translate business problems into scalable ML solutions and to communicate technical concepts to non-technical stakeholders.
Preferred Qualifications
Experience in the pharmaceutical/pharma domain or regulated industries; familiarity with GxP, or similar data governance requirements.
Exposure to other cloud providers (AWS/Azure) is a plus, but a strong preference for Google Cloud Platform.
Experience with distributed training, large-scale data processing, and fine-tuning of large language models.
Knowledge of privacy-preserving ML methods (differential privacy, synthetic data) and data lineage tools.
Education:
- Minimum qualification: Graduate degree in Information Technology.
- Preferred: Higher education (e.g., Master's degree in Computer Science, Information Technology, Data Science, or a related field) or relevant professional degrees/certifications.