Senior Data Scientist (In-Person Interview Required)

Senior Data Scientist

Location: Raritan, NJ (Onsite)

Duration: Long term contract

Face to Face Interview 

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 (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.

Key Responsibilities

• 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 Google Cloud Platform offerings (Vertex AI, BigQuery, 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 (Google Cloud Platform) and its AI/ML offerings (e.g., Vertex AI, BigQuery, 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.

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.