Senior Manager, AI and Data Scientist

About the role

The Senior Manager Artificial Intelligence and Data Scientist will focus on building robust scalable AI solutions and applications across R&D and corporate functions. This role will be accountable for architecting, developing, and maintaining scalable AI and Gen AI solutions. To do so, the role will leverage the foundational AI platform and engineering solution to focus on implementation of AI and Gen AI use cases from rapid prototypes to qualified or validated production application.

Responsibilities

• Create, implement, and deliver AI/ML, and Gen AI applications to provide insights, support decisions, and operational efficiencies across R&D functions
• Create product vision, set roadmap, and manage end-to-end AI product lifecycle while ensuring regulatory compliance
• Effectively translate complex AI concepts for non-technical stakeholders, guide cross-functional teams of data scientists and engineers, and drive the adoption of new technologies
• Provide technical expertise on responsible and ethical AI use for projects and develop best practices, standards, and documentation to consistently enable responsible AI solutions
• Guide developers and other extended team members or vendor resources to provide oversight on architecture, solution, AI/ML model development, testing, and its validation
• Collaborate with stakeholders to understand their processes, AI needs, and convert them to prioritized AI portfolio in the domain of responsibility
• Design and oversee enterprise Data Science and AI solutions that support analytics, AI, and GenAI solutions, ensuring structures are scalable, secure, and aligned with responsible and ethical AI use, and governance policies
• Ensure development of reusable data and AI components and promote their use across the data and AI ecosystem, business functions (e.g., clinical operations, asset management, clinical development, quality, safety, regulatory, Enterprise functions, etc.) and promote innovative, scalable data and AI engineering approaches to accelerate data science and AI work
• Leverage deep understanding of variety of R&D data (Clinical trials, Textual data, Clinical data, safety, etc.) to develop pragmatic operational AI use cases; these may include analytics, traditional AI/ML, or Gen AI
• Participate in design and architecture reviews for Data Science and AI solutions where requested
• Collaborate with internal data and AI engineers, other AI scientist, IT, cloud architects to ensure that data infrastructure and technical solutions are aligned with enterprise architecture, compliance needs, and organizational priorities
• AI and Gen AI implementation: Lead the industrialization of AI, Gen AI, and data science applications, moving from prototypes and proofs-of-concept to full production systems
• Develop and implement scalable engineering solutions, data repositories, data representations, and knowledge engineering to support the data and AI strategy execution and enable efficient model training, validation, and deployment of AI/ML models
• Build robust engineering frameworks that enable Retrieval-Augmented Generation (RAG), Agentic architectures, and other Gen AI workflows in R&D
• Introduce new AI/ML and Gen AI technologies into existing drug development processes, identifying opportunities for innovation and automation
• Drive awareness of AI/ML applications and the importance of strong Data and AI engineering foundation across the organization
• Provide AI support for Data and Information Governance, quality, and FAIR data
• Team and Organizational Leadership: Foster a culture of innovation, continuous learning, and accountability within and across teams
• Serve as AI and Data engineering expert for internal portfolio as well as organization wide initiatives, reviews, and AI committees
• Cross-functional Collaboration: Build strong, trusted relationships with key stakeholders across R&D, Scientific and Operations AI Data Science teams, other DnA pillars, IT, and external partners
• Partner with R&D teams to translate complex scientific challenges into clear, executable data and AI projects
• Communicate strategy, progress, and outcomes to diverse stakeholders, including technical teams and executive leadership

Requirements

Masters degree in Data Science, Computer Engineering, Computer Science, Physics, Statistics, Information Systems, or a related discipline with focus on advanced and modern Data Science, including the use of AI and machine learning. PhD is preferred.

Experience in software/product engineering

Strong proficiency in SQL and programming languages like Python or R

Strong experience working within the pharmaceutical, biotech, or life sciences industry, particularly within R&D, is highly desirable

Proven track record of implementing and deploying Gen AI and large language model (LLM) applications in production environments

Expertise in real-world data assets and using them to generate scientific evidence and guide operational effectiveness and efficiencies

Deep expertise across data engineering, representation, Gen AI, AI and machine learning techniques and experience in architecting and delivering AI/ML use cases

Highly self-motivated to deliver both independently and with strong team collaboration

Strong internal and cross-functional collaboration, project management skills with a focus on delivering impactful initiatives