This role pays more than
70%
of similar roles. Most pay
$205,000–$276,245
— the shaded band above.
At the midpoint, this role pays about
$264k
versus about
$241k
for comparable roles.
Based on 240 similar postings.
Employer
About AbbVie
AbbVie is a global biopharmaceutical company focused on discovering and delivering innovative medicines and solutions in immunology, oncology, neuroscience, and eye care. Its products include Humira, Skyrizi, and Rinvoq.
AbbVie currently has
297 open roles
on FindRole.
Listed pay typically runs
$109,500–$208,500
across 271 roles with salary data.
Clinical Director, Clinical Data and AI Convergence serves as the physician leader within the R&D Convergence Core Team. This role focuses on identifying and executing opportunities where data convergence, advanced analytics, and AI technologies can transform strategic decision-making and optimize end-to-end clinical and translational medicine workflows. The Director will act as the primary clinical authority to ensure innovations are scientifically rigorous and compliant with regulatory standards while building integrated, scalable solutions for trial execution and safety surveillance. Key responsibilities include translating therapeutic priorities into unified frameworks and overseeing the validation of AI-derived outputs. Required expertise includes medical degrees, deep knowledge of the clinical development lifecycle, and proficiency in Python, R, and machine learning frameworks like TensorFlow or PyTorch. The role addresses the challenge of integrating diverse data sources to improve decision quality across a complex pharmaceutical portfolio.
Act as the primary clinical authority to ensure AI and data initiatives are scientifically rigorous and regulatory compliant.
Identify systemic gaps in R&D workflows and design advanced analytical processes to improve decision-making.
Develop enterprise-level architectures that integrate diverse data sources into unified, analytics-ready frameworks.
Oversee the clinical validation of AI outputs for patient selection, safety surveillance, and trial optimization.
Translate complex therapeutic priorities into scalable technical solutions across various programs and indications.
Lead cross-functional teams of clinicians, data scientists, and engineers to co-create integrated workflow solutions.
Drive organizational change by training stakeholders and demonstrating the clinical impact of new technologies.
Establish standardized methodologies and best practices for data integration across different therapeutic areas.
What we're looking for
Must hold an M.D., D.O., or non-US equivalent of M.D.
Requires 8-10 years of pharmaceutical or biotech experience in clinical development or translational medicine.
Must have substantial experience in data-enabled workflow transformation and leading enterprise-level integrations of AI and advanced analytics.
Must possess a deep understanding of the entire clinical development lifecycle, including trial design, regulatory submission, and post-approval processes.
Expert-level proficiency in advanced machine learning, artificial intelligence, and programming in Python and R is required.
Experience with cloud computing platforms (AWS, Azure), MLOps principles, and large-scale data processing is required.
Board certification in a relevant specialty or recent clinical practice experience (preferred).
Knowledge of CDISC standards, FAIR data frameworks, and change management for clinical organizations (preferred).
Machine Learning
Artificial Intelligence
Python
R
TensorFlow
PyTorch
Scikit-learn
Hugging Face
MLOps
Airflow
Kubeflow
MLflow
AWS
Azure
CI/CD
CDISC
OMOP
Big Data
Data Architecture
Machine Learning
Deep Learning
Generative AI
Natural Language Processing
Python
R
TensorFlow
PyTorch
Scikit-learn
Hugging Face
MLOps
Airflow
Kubeflow
MLflow
AWS
Azure
CI/CD
Data Architecture
Distributed Computing
Machine Learning
Large Language Models
Generative AI
Natural Language Processing
Python
SQL
Deep Learning
Model Evaluation
Clinical Data
EMR Systems
Data Science
Responsible AI
HIPAA
Artificial Intelligence
Machine Learning
Deep Learning
Data Science
Data Governance
Low-code
Process Automation
Clinical Operations
Good Clinical Practice (GCP)
Biostatistics
Graph Algorithms
Data Visualization
Change Management
Project Management
Program Management