Director, Clinical Data & AI

Smith & Nephew•Mansfield, MA

7d•Hybrid

About The Position

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.

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.

Nice To Haves

PhD preferred

Responsibilities

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.
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.
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.
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).
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).
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 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.
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.