Director, Clinical Data & AI

What will you be doing?

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.

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

Global 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

Global 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)

Global 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)

Global 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)

Global Statistical & Clinical Programming Integration

• Own alignment and integration of:
• Statistical programming (TFLs, ADaM outputs)
• Clinical programming (data pipelines, transformations)
• Drive standardization, automation, and reuse across studies and programs
• Leverage AI solutions to accelerate programming across Global Clinical and Medical Affairs

Global Clinical & Medical Affairs 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

Global Regulatory & Data Governance Leadership

• Ensure all clinical data and AI activities are:
• Compliant with global regulatory requirements
• Establish strong governance across:
• Data standards and lineage
• AI model lifecycle
• Data privacy and security
• Support regulatory submissions with robust, defensible data strategies

Education & Experience

• 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 – 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

Compensation

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. Compensation decisions are dependent upon the facts and circumstances of each position and candidate.