Onsite || Junior Data Scientist || Raritan NJ

Hello

We are seeking a Junior Data Scientist with at least 5 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 develop microservice-based solutions, containerize deployments (e.g., GKE), and drive end-to-end SDLC practices. Experience in the pharma domain is a strong advantage.

Work on end-to-end development of GenAI/ML models: problem framing, data preparation, model selection, training, evaluation, and iteration.

Implement microservice-based AI solutions and deploy them in containerized environments (preferably GKE); define APIs and data contracts.

Leverage Google Cloud Platform offerings (Vertex AI, BigQuery, Dataflow, Cloud Storage, Pub/Sub, Cloud Run, GKE, etc.) to develop scalable AI solutions and efficient data workflows.

Deploy, monitor, and maintain models in production; implement observability (logs, metrics, tracing), cost optimization, and performance tuning.

Ensure cloud security, data governance, and compliance in line with regulatory requirements; manage IAM roles, data access controls, and data lineage.

Collaborate with cross-functional teams (data engineers, software engineers, product, regulatory/compliance, analytics) to translate business needs into robust ML solutions.

Stay current with GenAI advancements and evaluate new tools/approaches; produce reproducible experiments and artifacts.

Minimum 5 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.

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.

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