Senior Manager, AI and Data Engineering

The Senior Manager of Data and AI Engineering will focus on building a robust data ecosystem to accelerate availability of data for the development of AI and data science applications. This role will be accountable for architecting, developing, and maintaining scalable data pipelines and platforms, enabling the implementation of AI and Gen AI use cases from rapid prototypes to qualified or validated production application. The ideal candidate has deep expertise in data engineering, understands AI/ML technologies, and possesses a strong grasp of the pharmaceutical R&D data and its lifecycle, including Real-World Data (RWD) analytics, executing aspects of real world studies on external data, tokenization, etc.

The Senior Manager will work with other engineering and AI team members in designing, developing, and implementing robust data layers that support data scientists and key advanced responsible analytics/AI/ML and statistical business solutions. The role will partner with cross-functional data scientists and digital leaders to ensure efficient and reliable data flow across the organization. The role will also partner with IT platform engineers, members of the Data and AI platform, IT infrastructure and other technical teams in Otsuka to both leverage foundational AI and systems capabilities as well as share inputs on platform and infrastructures and ensuring adequate support from IT.

Job Description

• Develop and deliver data and AI engineering solutions to support data scientists and analytics/AI solutions, ensuring data efficient and scalable data representation, quality, reliability, and efficiency by providing deeper cross applications
• Act as data engineering tech lead for projects and develop best practices, standards, and documentation to ensure consistency and scalability solution architecture
• Provide technical input on the AI ecosystem and architecture including platform evolution, and new capability development
• Act as a subject matter expert for data engineering on cross functional teams in bespoke organizational initiatives by providing thought leadership and execution support for data engineering needs
• Guide developers and provide oversight on concepts such as data modeling, database architecture, data pipeline management, data and AI ops and automation, tools, and best practices
• Direct data and AI engineering research to advance design and development capabilities; rapidly experiment to provide insights into feasibility of solutions
• Collaborate with stakeholders to understand data requirements and address them with data solutions
• Demonstrate a proactive approach to identifying and resolving potential 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, RWD, Enterprise functions, etc.) and promote innovative, scalable data and AI engineering approaches to accelerate data science and AI work
• Drive the design, development, and maintenance of the core R&D data layer and engineer data to be readily available to for AI applications
• Implement robust, scalable ETL/ELT pipelines for ingesting, processing, and transforming, monitoring diverse scientific and clinical datasets and AI applications.
• Support best practices for data engineering, responsible AI, security and privacy by design, including data governance, lineage, and quality
• Collaborate with internal data and AI scientist, IT, cloud architects to ensure that data infrastructure and technical solutions are aligned with enterprise architecture and compliance needs

AI and Gen AI implementation

• Lead the industrialization of AI, Gen AI, and data science applications, moving 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 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. 

RWD and advanced analytics

• Lead the development of analytical platforms and data products that leverage Real-World Data (RWD) for advanced analytics in clinical development. Such data may include, EHRs, claims, biomedi