Director, Data Enablement and Reporting

Overview

Job Summary The Director of Data Enablement and Reporting is a strategic leader in DnA and will define, execute, and oversee the data enablement and reporting analytics across the R&D organization. This role is responsible for the technical implementation of programs related to the use of data and support information governance policies. The Director will drive R&D data strategy to enable a data-centric culture and ensure data is "AI-ready" by adhering to FAIR principles (Findable, Accessible, Interoperable, Reusable).

The role collaborates with AI, data science, engineering, and business teams to unlock the value of R&D data while ensuring data-related compliance needs are met. The role leverages the internal Data Science and AI team and DnA pillars to deliver reporting analytics and serve as the lead for enabling AI readiness of R&D data, its quality, clinical trial reporting and analytics.

The role will also provide advanced reporting through Gen AI solutions to stakeholders, while relying on the technical team to develop Gen BI solutions.

The role partners with stakeholders in R&D, Biostatistics, Clinical Data Management, Statistical Programming, Real World Data Analytics, Data Enablement or Governance, IT to ensure initiatives leverage foundational capabilities and inform data enablement and reporting needs.

Data Governance Strategy and Implementation

• Develop, champion, and execute the R&D data strategy roadmap, ensuring alignment with enterprise standards and regulatory requirements (e.g., GxP, HIPAA, GDPR).
• Design and operationalize a fit-for-purpose data governance framework, including policies, standards, data ownership models, and stewardship across R&D functions (e.g., Clinical Operations, Research, Regulatory, Safety).
• Oversee the implementation and maintenance of data governance and data capabilities toward AI readiness, including metadata management, semantic annotations for internal and external data, data cataloging, and data lineage tracking.
• Establish data quality standards, define quality metrics (KPIs), and lead remediation initiatives to ensure high data integrity, completeness, and accuracy. Provide metrics on the readiness of data for its use in AI and Data Science solutions.
• Collaborate with stakeholders to understand their processes, AI needs, and convert them to a prioritized AI portfolio in the domain of responsibility.
• Collaborate closely with Ethics and Compliance, Data Privacy, Information Security, IT and various R&D business functions to develop and deliver a data strategy aligned to Otsuka priorities on leveraging and protecting data assets.
• Leverage deep understanding of a 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.

Analytic Reporting and Data Product Delivery

• Partner with R&D business owners and leadership to identify and deliver high-impact, governed data products and analytical reporting that support strategic insights and operational efficiency.
• Collaborate with Data Science and AI Engineering teams to build scalable data models, data lakes, and visualization tools that enable self-service analytics and advanced data applications.
• Establish and monitor service level agreements (SLAs) and usage metrics for data products and reporting dashboards, continuously improving their value and adoption.
• Communicate data governance priorities, reporting metrics, and value realization to executive leadership and business stakeholders.

AI and Data Science Collaboration

• Act as a business liaison for AI and Data Science teams, translating functional R&D requirements into actionable data and governance initiatives in areas of responsibility, such as clinical operations and management.
• Collaborate with AI/ML teams to ensure the data is trusted, compliant, and ready for use in advanced analytics and AI applications.
• Identify and evaluate opportunities to leverage AI technologies to automate reporting analytics and governance workflows, such as metadata tagging and data quality checks.
• Collaborate with enterprise-wide Data…