Senior Director, Data Science Innovation Lead

Senior Director, Data Science Innovation Lead

The Senior Director Data Science Innovation lead will pioneer transformative solutions in real-world evidence generation in the Real-World Data, Measurement, and Analytics (RWDMA) organization, supporting the entire drug development life cycle from early development to late-phase clinical trials and post-approval market access and reimbursement. Leveraging the latest advancements in data sciences, such as multimodal AI, generative AI, knowledge graphs, causal AI and agentic AI, the Data Science Innovation Lead will develop and optimize statistical methodologies in comparative effectiveness analyses, precision medicine, predictive modelling, and evidence synthesis. In addition, the Innovation Lead will support AI-driven automation tools and deployment of intelligent systems for more efficient data processing and automate complex data analyses and QC processes, thereby accelerating development timelines while ensuring compliance with regulatory standards.

Key Responsibilities

       Data Science Strategy & Leadership

·       Align RWDMA Data Science initiatives with RWD organizational drug development goals, regulatory requirements (e.g., FDA, EMA), and payer expectations, ensuring strategic impact and compliance, particularly in RWD analytics.

·       Lead RWDMA Data Science through a matrix organization, collaborating with biostatisticians, clinical and other subject matter experts, and regulatory specialists to lead innovative applications of Data Science in RWE generation and embed Data Science into RWD workflows to improve efficiency of data processing and analysis.

       Innovative Applications of Data Science in RWE Generation

·       Design customized Data Science models tailored to specific RWD analytic applications, including:

·       Comparative Effectiveness: Applying Data Science methodologies to evaluate treatment outcomes across diverse patient populations, supporting real world biostatistics and statistical programming efforts.

·       Precision Medicine: Leveraging RWD to identify patient subgroups and biomarkers for tailored therapies.

·       Predictive Modelling: Using advanced Data Science techniques (e.g., transformers, recurrent neural networks) to forecast disease progression, trial outcomes, and patient responses, and enhance insights from digital measurement and patient reported outcomes.

·       Evidence Synthesis: Utilizing data science methodologies to integrate and synthesize findings from RWD and RCTs, including meta-analysis, indirect treatment comparisons, and network meta-analysis, to support comprehensive evaluations of treatment efficacy and safety.

      Automation & Process Optimization

·       Automate coding, including clinical coding and patient identification, and quality control (QC) processes using AI-driven anomaly detection and pattern recognition to ensure the validity of statistical programs, as well as data integrity across large-scale RWD datasets.

·       Develop Natural Language Processing (NLP) tools to automate the creation, review, and validation of analytic plans and protocols, ensuring compliance with regulatory and payer standards, benefiting data strategy and operational efficiency.

·       Build AI systems to streamline administrative tasks, such as assessing analytic consistency with market access requirements, enhancing operational efficiency across drug development phases.

       Data strategy

·       In alignment with DDF and D3 initiatives and the RWDSP team, assess the gaps in data needs in RWD and use potential Data Science applications to inform data strategy.

·       Collaborate with the RWDSP, DDF, and data tech teams to optimize RWD storage, management, and access control to optimise RWD analytical workflows.

·       Provide technical expertise and leadership on the usage of synthetic data in RWD and drug development.

       Collaboration & Thought Leadership

·       Mentor team members in advanced Data Science methodologies, fostering a culture of innovation and technical excellence across real world biostatistics, digital measurement, and other focus areas.

·       Spearhead methodological innovation and development in RWD Data Science, providing opportunities for mentoring and professional growth of junior RWDMA staff.

·       Develop and manage an external engagement strategy with academic partners and key op