This role pays more than
78%
of similar roles. Most pay
$136,187–$198,000
— the shaded band above.
At the midpoint, this role pays about
$205k
versus about
$167k
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.
Principal Data Scientist, Data and AI Convergence serves as a strategic leader and technical expert within the R&D Convergence Core team. This role focuses on driving enterprise-level transformation by integrating advanced AI and machine learning capabilities into research and development workflows to enhance decision-making across the clinical development continuum. The individual will architect scalable solutions, develop production-grade workflow orchestrations, and build reusable components for various therapeutic areas. Key technical requirements include expert proficiency in Python, R, and frameworks such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face. Additionally, the role demands expertise in MLOps, cloud computing platforms like AWS or Azure, and deep learning architectures. The work addresses complex challenges in pharmaceutical research, specifically focusing on drug development lifecycles, clinical trial optimization, and navigating regulatory requirements within a highly regulated healthcare environment.
What does a Data Scientist earn?
Median $162025 from 276 postings across 61 companies.
Identify and address systemic gaps in data convergence and AI/ML integration across R&D workflows.
Architect scalable, production-grade AI/ML pipelines that integrate multiple data sources into cohesive enterprise solutions.
Translate advanced technologies like deep learning and generative AI into practical tools for clinical trial optimization and decision-making.
Establish governance frameworks and best practices to ensure AI models meet regulatory requirements and ethical standards.
Develop reusable, modular AI components and workflow templates to be shared across different therapeutic areas.
Partner with senior leadership and cross-functional teams to align technical initiatives with corporate strategic priorities.
Create metrics and monitoring systems to evaluate the impact of AI integrations on R&D efficiency and quality.
What we're looking for
PhD in Computer Science, Statistics, Bioinformatics, Computational Biology, Applied Mathematics, Data Science, or related field (preferred); Master's with extensive experience considered.
4-5+ years of experience building and scaling advanced AI/ML solutions in enterprise environments.
5+ years of experience working in highly matrixed, complex organizational environments (Consulting in pharma/biotech preferred).
Expert proficiency in deep learning, neural networks, ensemble methods, transfer learning, and generative AI.
Mastery of ML/AI frameworks such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face.
Advanced programming skills in Python and R with experience in production code, version control, and CI/CD pipelines.
Deep understanding of MLOps principles, model lifecycle management, and cloud computing platforms like AWS or Azure.
Experience in pharmaceutical R&D, clinical development, or healthcare analytics (preferred).
South San Francisco, CA14 days ago
$185,200–$343,900
Python
R
Machine Learning
Generative AI
Data Architecture
System Integration
Experimental Design
Data Analysis
GxP
Data Privacy
Product Development
Scalability
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
Artificial Intelligence
Python
R
TensorFlow
PyTorch
Scikit-learn
Hugging Face
MLOps
Airflow
Kubeflow
MLflow
AWS
Azure
CI/CD
CDISC
OMOP
Big Data
Data Architecture