Director, Clinical Data & AI

About this role

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? 1. 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

2. 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

3. 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

4. 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)

5. 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)

6. 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

7. Operational Excellence & Delivery Model • Own intake, prioritization, and delivery across: • Data platform initiatives • AI/ML programs • Study-level data operations

• Implement scalable delivery models for standardized multi-source clinical outcomes datasets from the Clinical Data Lake to key business stakeholder teams • Optimize resourcing across: • High-throughput standardized work • High-complexity AI/data science initiatives

8. Regulatory & Data Governance Leadership • Ensure all clinical data and AI activities are: • Compliant with global regulatory requirements • Traceable, auditable, and reproducible

• Establish strong governance across: • Data standards and lineage • AI model lifecycle • Data privacy and security

• Support regulatory submissions with robust, defensible data strategies

Key Interfaces • Global Clinical Research Operations leadership • Clinical / Medical Affairs / Regulatory Affairs • Statistics, Data Science, and AI teams • IT / Digital / Enterprise Data organizations • External partners, CROs, AI vendors, and regulators

Education • BA required, PhD (preferred) or Master’s in Data Science, Biostatistics, Computer Science, or related field

What will you need to be successful? • Minimum of 10 years experience across clinical data, AI/ML, and data platforms in medtech/pharma/biotech • Proven leadership of multi-domain teams (data management, engineering, data science, AI, programming) • Demonstrated ownership of enterprise data architecture (e.g., data lake/platform) – Databricks preferred • Strong track record supporting regulatory submissions and clinical evidence generation • Enterprise mindset – integrates data, AI, and operations into a unified capability • Technical depth + breadth – credible across data engineering, CDM, AI, and analytics • Regulatory credibility – understand

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