Job Summary
The Senior Director, Data and AI Engineering will be a visionary and strategic leader responsible for defining and executing the data and AI engineering strategy across the entire R&D value chain with a deep knowledge of AI and R&D Data. This role will empower scientific, and business functions—including drug development, clinical trials, real-world data evidence—by delivering robust, scalable, and compliant data and AI foundations and implementing advanced data, AI and Generative AI (Gen AI) solutions.
As a technical leader, the Senior Director will manage a high-performing team of data engineers, AI/MLOps specialists, and application support team to foster a culture of innovation, and ensure the adoption of best-in-class engineering practices within a regulated pharmaceutical environment. This leader will bridge the gap between scientific ambition and technical execution, translating complex R&D challenges into impactful data and AI engineering solutions. The role will oversee a team of data and AI engineers, manage resources, and collaborate with internal and external partners to deliver high-quality data and analytic products.
The Data and AI Engineering team will focus on establishing and enhancing Otsuka’s Data and AI solutions foundations on top of the AI platform provided by Data and AI platform team in IT. This role will oversee team is a centralized capability and comprises of programmers, full-stack AI and data engineers who work closely with other Data and Analytics, AI Scientists, Data Scientists, various roles in IT, and other business functions.
Job Description
Strategy and Vision
Define and drive the strategic vision and roadmap for data engineering, AI, and Gen AI capabilities within R&D, aligning with overall business objectives and scientific priorities.
Serve as a key thought leader and trusted advisor to senior R&D leadership on opportunities and risks related to AI and data technologies and AI. Represent data and AI engineering in key AI and Data solutions initiatives in Otsuka as needed.
Evaluate and adopt emerging data and AI technologies, balancing new opportunities with the need for scalability, reliability, and regulatory compliance.
Execution and Technical Leadership
Deliver leading edge engineering approaches, effective enterprise data strategy (specifically clinical, operational, and real-world data), robust and responsible AI/ML model development, fit-for-purpose clinical development data stewardship strategies to enable key data insights to inform decision making and assist in business transformation initiatives.
Lead and deliver the design, development, and deployment of extensible and scalable data engineering for complex R&D Data, pipelines, and architecture for AI applications to support all R&D data AI needs, including structured, unstructured (text, imaging, and data from sensors, medical devices, and digital apps), and real-time data.
Oversee the implementation of AI and Gen AI use cases from ideation to production deployment, and application life cycle management. Hands on experience in architecting engineering foundation for a diverse set of AI solutions.
Lead and deliver data processing, representations, and AI engineering in R&D for applications ranging across: clinical trial optimization, patient recruitment, content authoring automation, asset management, pharmacovigilance, quality, regulatory, and enterprise functions, Real-World Data, sensor and medical device data, omics data, .
Champion engineering best practices, review AI solution architectures, and data & AI observability to ensure technical consistency, scalability, and effective governance of AI engineering across the portfolio of oversight.
Architect and scale MLOps practices for in house applications to support the entire model lifecycle—from experimentation and deployment to monitoring and retraining—while ensuring automation and reproducibility.
Drive awareness of AI/ML applications and the importance of strong Data and AI engineering foundation across the organization
Provide technical and engineering support for Data and Information Governance, quality, and FAIR data
Team and Organizational Leadership
Build, mentor, and lead a high-performing and collaborative team of data engineers, AI/ML engineers, life cycle support.
Foster a culture of innovation, continuous learning, and accountability within and across team.
Effectively manage budget, resource allocation, and project timelines for the data and AI engineering portfolio.
Oversight and Governance
Ensure that all data and AI initiatives and applications adhere to internal governance standards, data privacy regulations (e.g., GxP, HIPAA), and responsible AI principles.
Collaborate with Legal and Compliance teams to embed auditability and traceability into AI and Gen AI workflows.
Serve as AI and Data engineering expert for internal portfolio as well as organization wide initiatives, reviews, and AI committees.
Establish and manage robust monitoring, alerting, and incident response processes for all AI capabilities.
Effectively manage and provide oversight on vendors and vendor resources providing support to data and AI engineering portfolio
Lead or participate in meetings related to Data or AI, its impact, enablement, governance, determining or enabling roadmaps and strategy as needed.
Cross-functional Collaboration
Work closely with Data and AI Platform team and leverage platform level capabilities, solutions provided by IT as well as share data and AI solution needs as well as guidance with respective providers (Data and AI platform, IT infrastructure, etc.)
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.
Lead as subject matter expert, while communicating, and collaborating effectively to ensure portfolio success.
Qualifications/ Required
Knowledge/ Experience and Skills:
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.
Strong team leadership, internal and cross-functional collaboration, project management skills with a focus on delivering impactful initiatives.
12+ years of progressive leadership experience in data engineering, AI/ML, and Gen AI with at least 5 years in senior technical leadership or management roles.
Strong and demonstrated experience in architecting, building and maintaining large-scale data and AI solutions in a scientific, regulated, or research-heavy environment.
Strong experience working within the pharmaceutical, biotech, or life sciences industry, particularly within R&D, is highly desirable along with a deep understanding of AI and Machine Learning and its applications in Pharma.
Proven track record of implementing and deploying Gen AI and large language model (LLM) applications in production environments.
Creative problem solving using responsible use of technology.
Educational Qualifications
Masters in Mathematical, Engineering, or Scientific disciplines such as Computer Science, Mathematics, Engineering, Physics, Statistics, or a related field with focus on advanced and modern Data Science, including the use of AI and machine learning. PhD is strongly preferred.