Data Scientist III

Roles and Responsibilities:

• Collaborate with business to design, develop, and deploy advanced data science and AI solutions leveraging machine learning, large language models (LLMs), and modern data platforms.
• Work with large-scale structured and unstructured datasets including clinical data, publications, real-world evidence, and other healthcare datasets.
• Build predictive models and advanced analytics solutions to generate actionable insights and support data-driven decision making.
• Develop LLM-powered applications such as AI assistants, knowledge-grounded reasoning systems, and Retrieval-Augmented Generation (RAG) pipelines.
• Develop and maintain semantic layers, ontologies, and contextual data frameworks that enable consistent interpretation of data across AI systems and analytics workflows.
• Implement evaluation frameworks for AI systems, response quality assessment, hallucination mitigation, and benchmark-driven validation.
• Partner with data engineering and platform teams to ensure scalable pipelines, high-quality data availability, and production-ready AI systems.
• Communicate analytical findings and AI-driven insights clearly to both technical and non-technical stakeholders through reports, visualizations, and presentations.
• Mentor junior data scientists and contribute to knowledge sharing and adoption of modern AI development practices.

Skills and Competencies:

• Strong expertise in machine learning and predictive modeling techniques.
• Strong programming proficiency in Python for data science and AI system development.
• Experience working with modern data platforms such as Databricks (Spark, MLflow, Delta Lake).
• Experience building LLM-based applications, including Retrieval-Augmented Generation (RAG), AI copilots, and knowledge-grounded AI systems.
• Experience implementing embedding pipelines and vector database architectures (e.g., FAISS, Pinecone, Weaviate, OpenSearch Vector, pgvector).
• Familiarity with agentic AI frameworks such as LangGraph, LangChain, CrewAI, or similar orchestration frameworks.
• Experience designing multi-step AI workflows, including tool calling, context management, and agent-to-agent interaction.
• Understanding of LLMOps practices, including model deployment, monitoring, prompt engineering, evaluation workflows, and experiment tracking.
• Experience developing and deploying AI or ML services using FastAPI or similar frameworks for scalable APIs.
• Experience with semantic data modelling, ontologies, and contextual data frameworks to enable AI reasoning across complex datasets.
• Proficiency in working with large datasets using SQL and distributed data processing frameworks.
• Excellent communication and presentation skills to convey complex technical concepts to both technical and non-technical audiences.

Experience:

• BTech/Masters in a relevant field such as Data Science, Statistics, Computer Science, or a related discipline.
• 5+ years of experience in data science, machine learning, or applied AI roles.
• Proven experience building and deploying machine learning or LLM-powered systems in production environments.
• Experience working with large-scale structured and unstructured datasets.
• Experience developing AI-powered decision support tools or knowledge-based systems.
• Experience applying data science or AI techniques in healthcare, life sciences, or biopharma domains is highly desirable.
• Familiarity with clinical data, healthcare datasets, or real-world evidence sources is a plus.
• Understanding of the pharma ecosystem and healthcare data sources is beneficial.
• We hire for skills and capabilities, not just credentials - if this role excites you, but doesn't perfectly match your resume, we encourage you to apply anyway.