Clinical Director, Clinical Data and AI Convergence

Purpose:
The Clinical Director, Clinical Data and AI Convergence, is the physician leader within AbbVie’s R&D Convergence Core Team, responsible for identifying and executing opportunities where data convergence, advanced analytics, and AI technologies transform strategic decision-making and optimize end-to-end clinical and translational medicine workflows.

Responsibilities:
- Act as principal clinical integration authority for Convergence initiatives; ensure clinical relevance, scientific rigor, operational feasibility, and regulatory compliance.
- Assess existing workflows, identify gaps, and architect advanced analytical and AI-enabled processes embedded across R&D (early research to late-stage development).
- Serve as primary clinical voice for the Convergence Data and AI team; address real-world medical and operational needs.
- Translate therapeutic area/functional priorities into scalable integrated workflow solutions.
- Partner across R&D to address workflow inefficiencies, bottlenecks, and decision-making gaps via end-to-end data convergence.
- Lead development of enterprise workflows integrating diverse data sources into unified, analytics-ready frameworks.
- Ensure interoperability, user-centric design, and alignment with trial governance, decision forums, and change management.
- Oversee clinical validation of AI outputs for patient selection, endpoint strategies, trial optimization, safety surveillance, and benefit–risk assessment.
- Co-create solutions with clinicians, data scientists, biostatisticians, operations leaders, and regulatory partners; drive adoption via training and demonstrations of impact.
- Disseminate lessons learned, best practices, and standardized methodologies.

Qualifications:
- MD/DO or non-US equivalent with 8–10 years pharmaceutical/biotech industry experience in clinical development or translational medicine; substantial experience in data-enabled workflow transformation.
- Deep understanding of the full clinical development lifecycle (trial design, execution, regulatory submission, post-approval).
- Proven success leading enterprise-level workflow transformations integrating AI/advanced analytics/digital capabilities into regulated clinical operations.
- Strong understanding of therapeutic variability, patient populations, endpoint development, and safety signal interpretation.
- Ability to translate between clinical, technical, and operational perspectives; influence across matrixed organizations.

Preferred:
- Board certification; recent/ongoing clinical practice.
- Experience in translational medicine/biomarker strategy/precision medicine.
- Knowledge of ML/predictive modeling/statistics for clinical research; clinical data standards (e.g., CDISC) and interoperability frameworks.
- Cloud computing, big data architectures, enterprise data integration; change management experience.

Technical Expertise:
- Expert in ML/AI (deep learning, neural networks, ensembles, transfer learning, generative AI) and ML/AI frameworks (TensorFlow, PyTorch, Scikit-learn, Hugging Face).
- Advanced Python/R with software engineering; production code, version control, CI/CD, testing.
- MLOps, model lifecycle management, orchestration (Airflow, Kubeflow, MLflow), and enterprise deployment.
- Cloud/distributed computing (AWS, Azure, etc.) and data architecture/integration for enterprise analytics.

Domain Knowledge:
- Experience in pharmaceutical R&D/clinical development/healthcare analytics.
- Understanding of regulatory requirements, trial design, drug development lifecycle, and healthcare data governance.
- Working knowledge of healthcare data standards (e.g., CDISC, OMOP) and FAIR.

Benefits (explicitly stated):
- Paid time off (vacation, holidays, sick), medical/dental/vision insurance, 401(k) (eligible employees).
- Long-term incentive program eligibility.

Application instructions:
- Not provided in the job description text.