Junior Data Scientist

Summary: We are seeking a Junior Data Scientist with at least 5 years of hands-onexperience in developing GenAI/machine learning models and deploying them in acloud environment, preferably on Google Cloud Platform (GCP). The ideal candidatewill develop microservice-based solutions, containerize deployments (e.g., GKE), anddrive end-to-end SDLC practices. Experience in the pharma domain is a strongadvantage.Key ResponsibilitiesWork on end-to-end development of GenAI/ML models: problem framing, datapreparation, model selection, training, evaluation, and iteration.Implement microservice-based AI solutions and deploy them in containerizedenvironments (preferably GKE); define APIs and data contracts.Leverage GCP offerings (Vertex AI, BigQuery, Dataflow, Cloud Storage, Pub/Sub, CloudRun, 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 regulatoryrequirements; 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; producereproducible experiments and artifacts.Required QualificationsMinimum 5 years of hands-on experience developing GenAI/ML models and deployingthem in a cloud environment.Proficiency with Google Cloud Platform (GCP) 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 frameworkKnowledge of Retrieval-Augmented Generation (RAG) concepts and processesStrong 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 containerizedsolutions (Docker, Kubernetes; preference for GKE).Solid experience in MLOps: model versioning, experiments, automated training, featurestores, model registries, monitoring, and governance.Data processing and analytics expertise: SQL, data pipelines, ETL/ELT concepts, dataquality, and data visualization support.Excellent problem-solving, communication, and collaboration skills; ability to work withcross-disciplinary teams.Understanding of cloud security concepts, IAM, and basic principles of data privacy andcompliance.Demonstrated ability to translate business problems into scalable ML solutions and tocommunicate technical concepts to non-technical stakeholders.Preferred QualificationsExperience in the pharmaceutical/pharma domain or regulated industries; familiaritywith GxP, or similar data governance requirements.Exposure to other cloud providers (AWS/Azure) is a plus, but a strong preference forGCP.

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 professionaldegrees/certifications.