Director, Clinical Data & AI

Life Unlimited. At Smith+Nephew we design and manufacture technology that takes the limits off living. The Director of Clinical Data & AI is the global functional leader responsible for the strategy, architecture, and operational execution of clinical data and AI capabilities supporting end-to-end evidence generation. This role owns the clinical data lifecycle—from data acquisition and management to advanced analytics, AI enablement, and synthetic/simulated data—ensuring all data assets are high-quality, interoperable, and fit-for-purpose for regulatory, scientific, and operational decision-making.

The Director serves as the enterprise authority on clinical data platforms and AI-enabled evidence generation, driving integration across clinical systems, data engineering, AI/ML, and statistical/clinical programming. This position has full accountability for the strategy, execution, quality, and evolution of the Clinical Data & AI function globally. What will you be doing?

• Global Clinical Data & AI Strategy Define and execute the global strategy for Clinical Data & AI aligned to enterprise evidence-generation and AI transformation goals Establish a unified operating model integrating:
  Clinical systems (EDC, eCOA, registries) Clinical Data Lake & central data model Data management and data engineering AI/ML and advanced analytics Serve as the enterprise authority on clinical data architecture and AI enablement for clinical & medical affairs across all BUs and geographies Partner with Clinical Study Management, Clinical Strategy, Regulatory, Medical Affairs, Statistics, and IT to define data-driven evidence strategies
• Clinical Data Architecture & Platforms Own the design, governance, and evolution of:
  Clinical Data Lake (CDL) and standardized data models Clinical systems ecosystem (EDC, eCOA, registry ingestion, integrations) Data pipelines, transformation, and interoperability frameworks Ensure scalable, compliant, and extensible architecture supporting:
  Cross-study analytics Real-world data integration Device + clinical data linkage Drive standardization (e.g., CDISC-based models) and elimination of data silos
• AI, Data Science & Advanced Analytics Lead development and deployment of AI/ML capabilities across the clinical lifecycle, including:
  Data quality automation and monitoring AI-assisted clinical study reporting and analytics Cross-study insights and meta-analyses Drive integration of AI into core workflows, not point solutions Establish best practices for:
  Model development, validation, monitoring Responsible AI (traceability, reproducibility, regulatory alignment) Oversee collaboration between data science, statistics, and programming teams
• Synthetic Data, Simulation & Virtual Twins Own strategy and execution for:
  Synthetic clinical data generation Simulation frameworks for study design and operational planning Virtual twin development for patient- and study-level modeling Ensure alignment with regulatory expectations for transparency and scientific validity Integrate synthetic and simulated data into:
  Study design optimization Evidence generation (e.g., hybrid designs, external controls)
• Clinical Data Management & Quality Oversee global clinical data management function, ensuring:
  High-quality, consistent, and inspection-ready data Efficient study startup (eCRF design, database builds) and closeout Risk-based monitoring and analytics-driven data review Embed AI, machine learning modeling, and automation into CDM workflows to improve efficiency and quality Ensure alignment with regulatory and compliance standards (FDA, EU MDR, GDPR, HIPAA)
• Statistical & Clinical Programming Integration Own alignment and integration of:
  Statistical programming (TFLs, ADaM outputs) Clinical programming (data pipelines, transformations) Ensure seamless data flow from raw data → analysis-ready datasets → reporting Drive standardization, automation, and reuse across studies and programs Leverage AI solutions to accelerate programming across Global Clinical and Medical Affairs
• Operational Excellence & Delivery Model Own intake, prioritization, and delivery across:
  Data platform initiatives AI/ML programs Study-level…