Director, Clinical Data & AI
Smith+Nephew · Andover, MA · 3 wk ago
$165k–$236k/yrFull-time
About the role
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.
Responsibilities
- 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
- Own the design, governance, and evolution of:
- 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
- Lead development and deployment of AI/ML capabilities across the clinical lifecycle, including:
- 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)
- Own strategy and execution for:
- 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)
- Oversee global clinical data management function, ensuring:
- 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
- Own alignment and integration of:
- 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
- Own intake, prioritization, and delivery across:
- 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
- Ensure all clinical data and AI activities are:
- 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
Requirements
- 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 – understands how data and AI decisions impact submissions
- Execution rigor – delivers scalable, high-quality platforms and outputs
- Transformational leadership – embeds AI into workflows, not as isolated innovation
- Pragmatic innovation – advances capabilities while maintaining compliance and reliability
Qualifications
- BA required, PhD (preferred) or Master’s in Data Science, Biostatistics, Computer Science, or related field
Pay
The anticipated base compensation range for this position is $165,250-$236,000 USD annually. The actual base pay offered to the successful candidate will be based on multiple factors, including but not limited to job-related knowledge/skills, experience, and geographic location.
Benefits
- Medical, dental, and vision coverage
- 401(k) with company match
- Tuition reimbursement
- Medical leave programs
- Parental leave
- Generous PTO and paid company holidays
- 8 hours of volunteer time annually
- Employee Assistance Program (EAP)
- Fertility and Adoption Assistance Program
- Health Savings Account (Employer Contribution of $500+ annually)
- Discounted stock options
- Flexible work model (hybrid for most professional roles)
- Discounts on fitness clubs, travel, and more