Clinical Director I / Senior Principal Data Scientist, Director I, Data and AI Convergence

AbbVie•North Chicago, IL

About The Position

The Director, Clinical Data and AI Convergence, will serve as 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 can transform strategic decision-making processes and optimize end-to-end clinical and translational medicine workflows. This role brings together deep medical expertise, strategic vision, and applied data science capabilities to develop integrated, scalable workflow solutions - moving beyond isolated point tools - to accelerate trial execution, improve decision quality, and enhance operational efficiency across AbbVie’s therapeutic portfolio. The Director will Act as the principal clinical integration authority for Convergence initiatives, ensuring solutions are clinically relevant, scientifically rigorous, operationally feasible, and compliant with regulatory standards. Lead efforts to assess existing workflows, identify systemic gaps, and collaborate with teams to architect advanced analytical and AI-enabled processes that seamlessly embed into R&D processes, from early research through late-stage development. Ensure that clinical workflow innovations create measurable value for AbbVie’s pipeline and shape a sustainable foundation for enterprise-wide adoption of advanced data capabilities.

Requirements

MD with 8-10 years of pharmaceutical/biotech industry experience in clinical development or translational medicine; substantial experience in data enabled workflow transformation.
Deep understanding of the entire clinical development lifecycle, including trial design, execution, regulatory submission, and post-approval processes.
Proven success in leading enterprise level workflow transformations integrating AI, advanced analytics, or digital capabilities into regulated clinical operations.
Strong grasp of therapeutic area variability, patient population considerations, endpoint development, and safety signal interpretation.
Exceptional ability to translate between clinical, technical, and operational perspectives for diverse audiences.
Demonstrated skill in influencing across matrixed organizations.
PhD in Computer Science, Statistics, Bioinformatics, Computational Biology, Applied Mathematics, Data Science, or related quantitative field strongly preferred; Master's degree with exceptional demonstrated expertise and extensive experience considered
8-10+ years of progressive experience building, deploying, and scaling advanced AI/ML solutions in enterprise environments, with demonstrated leadership of large-scale, cross-functional data and analytics initiatives.
Proven track record of leading enterprise-wide workflow projects that resulted in measurable organizational impact and sustainable capability development
Minimum 5+ years of experience working in highly matrixed, complex organizational environments (Preferred experience in Consulting across pharmaceutical, biotech, healthcare, or similarly regulated industries strongly preferred)
Expert-level proficiency in advanced machine learning and artificial intelligence, including deep learning, neural network architectures, ensemble methods, transfer learning, and generative AI technologies
Demonstrated mastery of ML/AI frameworks and platforms (e.g., TensorFlow, PyTorch, Scikit-learn, Hugging Face) and their application to complex, real-world problems
Advanced programming capabilities in Python and R, with strong software engineering principles; experience with production code development, version control, CI/CD pipelines, and testing frameworks
Deep understanding of MLOps principles, model lifecycle management, workflow orchestration tools (e.g., Airflow, Kubeflow, MLflow), and enterprise deployment architectures
Experience with cloud computing platforms (AWS, Azure, others) and distributed computing frameworks for large-scale data processing and model training
Strong expertise in data architecture, data integration patterns, and modern data platforms supporting enterprise analytics
Success leading enterprise-scale initiatives that transform organizational workflows and decision-making processes through data and AI integration
Ability to influence senior leadership, build cross-functional coalitions, and drive adoption of complex technical solutions across large, matrixed organizations
Change management acumen and experience driving organizational transformation in regulated environments
Record of successful collaboration with multidisciplinary teams including data scientists, software engineers, clinicians, scientists, and business stakeholders

Nice To Haves

Board certification in relevant specialty
Recent or ongoing clinical practice experience.
Experience in translational medicine, biomarker strategy, or precision medicine.
Knowledge of machine learning, predictive modeling, and statistical methodologies relevant to clinical research.
Experience with clinical data standards (e.g., CDISC) and interoperability frameworks.
Experience implementing change management for new workflows or technologies in clinical organizations.
Knowledge of cloud computing platforms, big data architectures, and enterprise data integration.
Domain knowledge in pharmaceutical R&D, clinical development, or healthcare analytics strongly preferred.
Knowledge of regulatory requirements, clinical trial design, drug development lifecycle, and healthcare data governance preferred.
Knowledge of healthcare data standards (e.g., CDISC, OMOP) and FAIR data frameworks preferred.

Responsibilities

Serve as the primary clinical voice within the Convergence Data and AI team, ensuring initiatives address real-world medical and operational needs.
Translate therapeutic area and functional priorities into integrated workflow solutions that can scale across programs and indications.
Partner across R&D to identify workflow inefficiencies, bottlenecks, and decision-making gaps that can be addressed through end-to-end data convergence and advanced technological strategies.
Lead the collaborative development of architecture and enterprise level workflows integrating diverse data sources into unified, analytics-ready frameworks.
Ensure new workflows are interoperable, user centric, and aligned with trial governance, decision forums, and change management plans.
Oversee the clinical validation of AI derived outputs for patient selection, endpoint strategies, trial optimization, safety surveillance, and benefit–risk assessment.
Facilitate co-creation of solutions with clinicians, data scientists, biostatisticians, operations leaders, and regulatory partners.
Champion cultural adoption of integrated data and AI workflows through stakeholder engagement, targeted training, and transparent demonstration of business/clinical impact.
Disseminate lessons learned, best practices, and standardized methodologies across functions and therapeutic areas to accelerate adoption.