Director, Clinical Data & AI
Job in
Andover, Essex County, Massachusetts, 05544, USA
Listed on 2026-07-17
Listing for:
Smith & Nephew
Full Time
position Listed on 2026-07-17
Job specializations:
-
IT/Tech
Data Science Manager, AI Engineer (Applied/Software), Data Analyst, Data Engineering
Job Description & How to Apply Below
Role Overview
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.
Key Responsibilities- Define and execute the global strategy aligned to enterprise evidence-generation and AI transformation goals.
- Establish a unified operating model integrating clinical systems, data lake, data management, AI/ML, and advanced analytics.
- Serve as the enterprise authority on clinical data architecture and AI enablement across all business units 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 the Clinical Data Lake, standardized data models, clinical systems ecosystem, and data pipelines.
- Ensure scalable, compliant, and extensible architecture supporting cross-study analytics, real-world data integration, and 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.
- Automate data quality monitoring, AI-assisted reporting, cross-study insights, and meta-analyses.
- Integrate AI into core workflows and establish best practices for model development, validation, monitoring, and responsible AI.
- Oversee collaboration between data science, statistics, and programming teams.
- Own strategy for synthetic clinical data generation, simulation frameworks, and virtual twin development.
- Align with regulatory expectations for transparency and scientific validity.
- Integrate synthetic data into study design optimization and evidence generation.
- Oversee global clinical data management ensuring high-quality, inspection-ready data.
- Efficient study startup and closeout with risk-based monitoring and analytics-driven review.
- Embed AI and automation to improve efficiency and quality, ensuring regulatory compliance.
- Align statistical and clinical programming workflows for seamless data flow from raw data to reporting.
- Standardize, automate, and reuse across studies and programs.
- Leverage AI to accelerate programming across global clinical and medical affairs.
- Own intake, prioritization, and delivery of data platform initiatives and AI/ML programs.
- Implement scalable delivery models for standardized multi-source clinical outcomes datasets.
- Optimize resourcing across high-throughput work and high-complexity initiatives.
- Ensure compliance with global regulatory requirements and establish robust governance.
- Support regulatory submissions with defensible data strategies.
BA required;
PhD (preferred) or Master’s in Data Science, Biostatistics, Computer Science, or related field.
- 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 that integrates data, AI, and operations into a unified capability.
- Technical depth and breadth 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.
- Transformat…
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