Data Scientist - RDT Pharma R&D

Roche; Hyderabad; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Roche in Hyderabad seeks a Lead IT Data Scientist to architect, lead, and deliver advanced AI/ML solutions within a highly regulated Pharma R&D context.

The role requires expertise in Generative AI, agentic frameworks, multi-agent orchestration, GraphRAG, and LLMOps, guiding governance and platform engineering across clinical data lifecycles.

Responsibilities

• Lead the architecture, development, and deployment of enterprise-scale AI/ML solutions and agentic systems.
• Drive multiple strategic data science initiatives aligning with business priorities.
• Define standards and reusable frameworks for AI/ML development.
• Design enterprise data science governance, scalability, and operational excellence across AI initiatives.
• Mentor data scientists and foster technical excellence and innovation.
• Lead end-to-end data science projects from problem definition to measurable impact.

Skills

AI/ML

Generative AI

LangGraph

LLMOps

Agentic AI

Tools

AWS AgentCore

LangGraph

Job description

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.

The Position

Job Description

The Lead IT Data Scientist is responsible for architecting, leading, and delivering advanced AI and data science solutions that address complex business and scientific challenges within Pharma R&D. This role serves as a technical leader, guiding the design, development, deployment, and governance of enterprise-scale AI/ML systems while mentoring junior data scientists and driving innovation across the organization.

Operating in a highly regulated GxP environment, you will lead the development of next‑generation AI‑powered platforms and specialized autonomous agents supporting Analytical Data Scientists across the clinical data lifecycle. The role requires deep expertise in Generative AI, Agentic AI frameworks, multi-agent orchestration, Graph‑based Retrieval‑Augmented Generation (GraphRAG), Large Language Model Operations (LLMOps), and AI platform engineering. You will define technical strategy, establish best practices, and collaborate with cross‑functional teams to ensure scalable, compliant, and business‑aligned AI solutions.

Description of the Area

The Clinical Submission Data and Content Generation & Reuse function focuses on transforming the creation, management, and reuse of clinical data and regulatory submission content through advanced digital capabilities and AI‑driven automation. The organization develops structured content management platforms, reusable data and content assets, and intelligent agent ecosystems that support clinical submissions, regulatory compliance, and scientific communication.

The team is at the forefront of leveraging Generative AI, Agentic Workflows, Knowledge Graphs, and advanced analytics to improve quality, accelerate submission timelines, and enable data‑driven decision‑making across the clinical and regulatory landscape.

Job Responsibilities

• Scope / Content Leadership

  Lead the architecture, development, and deployment of enterprise-grade AI/ML solutions and agentic systems.

  Drive multiple strategic data science initiatives simultaneously, ensuring alignment with business priorities.

  Define technical standards, reusable frameworks, and best practices for AI and machine learning development.

  Design and implement enterprise‑wide data science frameworks, governance models, and best practices to ensure consistency, scalability, and operational excellence across AI initiatives.

  Mentor and guide data scientists, fostering technical excellence and innovation across the team.

  Lead complex data science projects end-to-end, from problem definition and solution design through deployment, adoption, and measurable business impact.

• Accountability / Problem Solving

  Solve highly complex and ambiguous business problems using advanced statistical modeling, machine learning, and generative AI techniques.

  Design and implement sophisticated multi-agent workflows using frameworks such as LangGraph and AWS AgentCore.

  Lead development of intelligent automation solutions including autonomous code reviewers, clinical workflow copilots, AI‑driven debugging assistants, and submission content generation agents.

  Drive model validation, monitoring, explainability, and AI governance practices in regulated environments.

• Stakeholder Management

  Partner with senior business leaders, clinical experts, statisticians, and technology teams to identify strategic opportunities for AI adoption.

  Translate complex analytical concepts into actionable business insights for executive and non‑technical audiences.

  Influence key stakeholders on AI strategy, roadmap prioritization, and solution adoption.

  Work closely with senior leadership to inform, shape, and influence strategic business decisions through data‑driven insights, advanced analytics, and AI‑enabled recommendations.

• Impact / Strategy

  Define and execute the technical roadmap for advanced analytics, Generative AI, and agentic AI capabilities within the function.

  Lead high‑impact projects that directly influence organizational objectives, innovation initiatives, and operational efficiency.

  Evaluate emerging technologies and recommend scalable solutions that advance business transformation.

• Complexity / Product Size

  Work with large-scale clinical, regulatory, and enterprise datasets across structured and unstructured formats.

  Design scalable AI architectures supporting production‑grade solutions with high reliability and compliance requirements.

  Drive optimization of existing models and establish frameworks for continuous improvement and performance monitoring.