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Director, Clinical Data & AI

Job in Andover, Essex County, Massachusetts, 05544, USA
Listing for: Smith & Nephew
Full Time position
Listed on 2026-07-16
Job specializations:
  • IT/Tech
    Data Engineering, AI Engineer (Applied/Software), Data Science Manager, Data Analyst
Salary/Wage Range or Industry Benchmark: 165250 - 236000 USD Yearly USD 165250.00 236000.00 YEAR
Job Description & How to Apply Below

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 to analysis‑ready datasets and 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…
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