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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
1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. Business / Technical Ability
- Demonstrate deep expertise across multiple AI/ML domains including predictive modeling, NLP, knowledge graphs, GraphRAG, and agent-based systems.
- Apply strong software engineering principles to develop secure, scalable, and maintainable AI products.
- Lead technical decision-making related to model architecture, framework selection, cloud infrastructure, and deployment strategies.
Qualifications
Education / Experience
- Master's or PhD in Data Science, Computer Science, Statistics, Artificial Intelligence, Bioinformatics, Mathematics, or related quantitative discipline.
- 8-12 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 from end-to-end and delivering significant, measurable business impact.
- Demonstrated success in delivering enterprise-scale solutions that influence strategic business outcomes.
- Proven track record of 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.
- Experience working within regulated environments such as Pharmaceutical, Healthcare, Life Sciences, or other compliance-driven industries is preferred.
Technical Skills
Programming & Data Engineering
- Expert-level proficiency in Python for AI/ML development, agent orchestration, backend services, and automation solutions.
- Strong hands-on expertise in R for clinical statistical programming, NextGen programming frameworks (e.g., Admiral), and analysis workflows.
- Working knowledge of SAS and clinical programming standards is desirable.
- Strong understanding of software engineering concepts, APIs, microservices, CI/CD pipelines, GitOps, and testing frameworks.
Generative AI & Agentic Systems
- Deep expertise in Large Language Models (LLMs), Prompt Engineering, Fine-tuning, Agentic AI, and AI application architecture.
- Extensive experience with LangChain, LangGraph, CrewAI, AWS AgentCore, AutoGen, Semantic Kernel, and Model Context Protocol (MCP).
- Proven experience designing multi-agent architectures, autonomous workflows, reasoning systems, and human-in-the-loop AI solutions.
- Strong understanding of AI safety, governance, observability, evaluation frameworks, and responsible AI practices.
RAG, Knowledge Graphs & Search
- Advanced experience in Retrieval-Augmented Generation (RAG), 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.
Machine Learning & Advanced Analytics
- Strong expertise in supervised and unsupervised learning, predictive modeling, NLP, deep learning, and time-series analysis.
- Experience with TensorFlow, PyTorch, Scikit-learn, XGBoost, and modern ML frameworks.
- Strong foundation in experimental design, statistical inference, model evaluation, and explainable AI techniques.
Cloud & MLOps / LLMOps
- Experience deploying AI solutions on AWS, Azure, or GCP platforms.
- Strong expertise in MLOps and LLMOps practices including model lifecycle management, monitoring, evaluation, governance, and automation.
- Experience with containerization technologies such as Docker, Kubernetes, and cloud-native AI deployments.
Data Visualization & Reporting
- Expertise in developing impactful visualizations and dashboards using tools such as Tableau, Power BI, R Shiny, Plotly, or Python visualization libraries.
- Ability to communicate complex analytical insights through compelling storytelling and executive-ready presentations.
Additional Qualifications
- Exceptional communication and presentation skills with the ability to influence senior stakeholders and executive leadership.
- Strong leadership and mentoring capabilities with experience building high-performing teams.
- Ability to balance strategic thinking with hands-on technical execution.
- Demonstrated innovation mindset and ability to identify emerging technology opportunities.
- Strong collaboration skills across business, clinical, regulatory, and technology organizations.
- Demonstrated experience establishing enterprise-wide data science standards, operating models, governance practices, and reusable frameworks across large organizations.
- Experience supporting GxP-compliant systems, validation processes, and regulatory requirements related to AI solutions is highly desirable.
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Who we are
A healthier future drives us to innovate. Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact.
Let’s build a healthier future, together.
Roche is an Equal Opportunity Employer.
