Data Scientist f Submission Data and Content Generation & Reuse (AIDCG) - Pharma R&D

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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
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
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

Work arrangement
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