Senior Manager, AI and Data Scientist

Job Summary

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.

The ideal candidate would have deep expertise and successful hands on experience in foundations of AI/ML in an Industry setting, understand AI/ML technologies, and possess a strong grasp of the pharmaceutical R&D processes, drug development, R&D and Enterprise data, and its lifecycle management.

The Senior Manager AI and Data Scientist is a strategic leader who identifies and develops AI-driven solutions to complex industry challenges, from drug discovery to clinical trials. This role requires a unique blend of deep domain knowledge in life sciences, deep technical proficiency in AI and data science, and proven product management expertise. Beyond technical and product skills, they must possess exceptional communication, leadership, and problem-solving abilities to align diverse teams, navigate ambiguity, and deliver innovative solutions that provide tangible business and scientific value.

• Develop, 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; ensure that these principle AI and Gen AI components such as RAG, agentic architectures, and other forms of data analytics ranging from traditional to newer technologies.
• Provide technical inputs on the AI ecosystem and architecture including platform evolution, and new capability development.
• 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.
• Demonstrate a proactive approach to identifying and resolving potential AI system issues both during development and production support of data analytics and AI applications.
• 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.

• 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.
• Collaborate with data scientists, AI, and machine learning scientists to 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.
• Ensure that all data and AI solutions comply with pharmaceutical industry regulations, including HIPAA, GxP, and other relevant standards.
• Drive awareness of AI/ML applications and the importance of strong Data and AI engineering foundation across the…