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Roche

Global pharmaceuticals and diagnostics company

Lead IT Data Scientist - Pharma R&D, Clinical Submission Data and Content Generation & Reuse

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeHyderabad, Telangana, IndiaNo salary listed

About the Job

Requirements

  • A Master's or PhD in Data Science, Computer Science, Statistics, Artificial Intelligence, Bioinformatics, Mathematics, or a related quantitative discipline.
  • Eight to twelve years of experience in Data Science, Machine Learning, Artificial Intelligence, Advanced Analytics, or similar domains.
  • Proven experience leading end-to-end AI/ML initiatives from ideation through production deployment and business adoption.
  • Extensive experience leading complex data science projects end-to-end and delivering measurable business impact.
  • Demonstrated success delivering enterprise-scale solutions that influence strategic business outcomes.
  • Proven track record partnering with senior executives and business leaders to drive data-informed strategic decision-making.
  • Experience mentoring data scientists and providing technical leadership across cross-functional teams.
  • Expert-level proficiency in Python for AI/ML development, agent orchestration, backend services, and automation solutions.
  • Hands-on expertise in R for clinical statistical programming, NextGen programming frameworks such as Admiral, and analysis workflows.
  • Strong understanding of software engineering concepts, APIs, microservices, CI/CD pipelines, GitOps, and testing frameworks.
  • Deep expertise in Large Language Models, prompt engineering, fine-tuning, Agentic AI, and AI application architecture.
  • Experience with LangChain, LangGraph, CrewAI, AWS AgentCore, AutoGen, Semantic Kernel, and Model Context Protocol.
  • Experience designing multi-agent architectures, autonomous workflows, reasoning systems, and human-in-the-loop AI solutions.
  • Understanding of AI safety, governance, observability, evaluation frameworks, and responsible AI practices.
  • Experience in Retrieval-Augmented Generation, Agentic RAG, Hybrid Search, and GraphRAG implementations.
  • Expertise in chunking strategies, embedding models, vector databases, semantic retrieval, re-ranking techniques, and metadata-driven search.
  • Experience with Neo4j, knowledge graphs, graph databases, ontologies, and enterprise search architectures.
  • Expertise in supervised and unsupervised learning, predictive modeling, natural language processing, deep learning, and time-series analysis.
  • Experience with TensorFlow, PyTorch, Scikit-learn, XGBoost, and modern machine learning frameworks.
  • Foundation in experimental design, statistical inference, model evaluation, and explainable AI techniques.
  • Experience deploying AI solutions on AWS, Azure, or GCP platforms.
  • Expertise in MLOps and LLMOps practices, including model lifecycle management, monitoring, evaluation, governance, and automation.
  • Experience with Docker, Kubernetes, and cloud-native AI deployments.
  • Expertise developing visualizations and dashboards using Tableau, Power BI, R Shiny, Plotly, or Python visualization libraries.
  • Ability to communicate complex analytical insights through storytelling and executive-ready presentations.
  • Ability to influence senior stakeholders and executive leadership.
  • Leadership and mentoring capabilities, including experience building high-performing teams.
  • Ability to balance strategic thinking with hands-on technical execution.
  • Experience establishing enterprise-wide data science standards, operating models, governance practices, and reusable frameworks across large organizations.

Responsibilities

  • Architect, lead, and deliver advanced AI and data science solutions for complex business and scientific challenges within Pharma R&D.
  • Guide the design, development, deployment, and governance of enterprise-scale AI/ML systems and lead development of AI-powered platforms and specialized autonomous agents supporting Analytical Data Scientists across the clinical data lifecycle.
  • Lead the architecture, development, and deployment of enterprise-grade AI/ML solutions and agentic systems.
  • Drive multiple strategic data science initiatives simultaneously and align them 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 support consistency, scalability, and operational excellence across AI initiatives.
  • Mentor and guide data scientists to foster technical excellence and innovation across the team.
  • Lead complex data science projects from problem definition and solution design through deployment, adoption, and measurable business impact.
  • Solve complex and ambiguous business problems using statistical modeling, machine learning, and generative AI techniques.
  • Design and implement multi-agent workflows using frameworks such as LangGraph and AWS AgentCore.
  • Lead 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.
  • 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 stakeholders on AI strategy, roadmap prioritization, and solution adoption.
  • Work with senior leadership to inform and shape strategic business decisions using data-driven insights, advanced analytics, and AI-enabled recommendations.
  • Define and execute the technical roadmap for advanced analytics, generative AI, and agentic AI capabilities within the function.
  • Lead high-impact projects that influence organizational objectives, innovation initiatives, and operational efficiency.
  • Evaluate emerging technologies and recommend scalable solutions that advance business transformation.
  • Work with large-scale clinical, regulatory, and enterprise datasets in structured and unstructured formats.
  • Design scalable AI architectures for production-grade solutions with high reliability and compliance requirements.
  • Optimize existing models and establish frameworks for continuous improvement and performance monitoring.
  • Apply software engineering principles to develop secure, scalable, and maintainable AI products.
  • Lead technical decision-making on model architecture, framework selection, cloud infrastructure, and deployment strategies.

Desired Qualifications

  • Experience in regulated environments such as pharmaceutical, healthcare, life sciences, or other compliance-driven industries.
  • Working knowledge of SAS and clinical programming standards.
  • Experience supporting GxP-compliant systems, validation processes, and regulatory requirements related to AI solutions.

About the Company

Roche

Global pharmaceuticals and diagnostics company

Roche is a global leader in pharmaceuticals and diagnostics. It develops medicines and diagnostic tools that help detect, monitor, and treat diseases, using research and biotechnology to drive new therapies and tests. Roche combines drug development with in-house diagnostic products to support personalized medicine, where treatments are tailored to individual patients based on test results. Its approach differs from many peers by integrating drug discovery with diagnostic capabilities and by expanding its research through strategic acquisitions (like Genentech) to strengthen its biotechnology and R&D capabilities. The company’s goal is to improve patient outcomes and healthcare worldwide by delivering reliable medicines, accurate diagnostic tests, and data-driven care.

Company size
1-10
Company stage
IPO
Headquarters
Basel, Switzerland
Founded
1896

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