This role pays less than
79%
of similar roles. Most pay
$164,874–$225,337
— the shaded band above.
At the midpoint, this role pays about
$159k
versus about
$195k
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.
Scientific Technical Lead, Early Stage PDST CMC joins the BTS - PDST team to drive AI-native programs across early and late stage product development. This role focuses on embedding advanced analytics into early-stage biologics pipelines, specifically for process characterization, robustness, and control strategy development. You will architect data solutions, build predictive models using hybrid approaches, and design intelligent workflows involving supervised/unsupervised machine learning and retrieval-augmented generation. Key responsibilities include developing analytical frameworks for digital twins, designing statistically rigorous experiments via Bayesian optimization or active learning, and ensuring GxP compliance. The role requires expert Python proficiency, experience with the scientific ecosystem including PyTorch and TensorFlow, and mastery of tools like R, Dataiku, AWS SageMaker, and Spark. You will solve complex manufacturing and regulatory challenges by translating technical data into actionable insights for drug development.
Develop and deploy predictive models for upstream bioprocess performance, downstream purification, and critical quality attribute outcomes.
Design hybrid modeling approaches that combine first-principles process understanding with data-driven techniques to ensure scientific interpretability.
Build analytical frameworks to support digital twin concepts and in-silico process optimization for accelerated development timelines.
Define data strategies for new programs by determining what data to collect, how to structure it, and how to integrate it across campaigns.
Architect AI-driven workflows using statistical methods, machine learning, or orchestrated pipelines based on specific scientific requirements.
Design statistically rigorous experiments using methodologies such as Design of Experiments (DoE), Bayesian optimization, and active learning.
Translate complex quantitative analyses into actionable insights for cross-functional stakeholders including scientists, engineers, and senior leadership.
Ensure all models, data assets, and analytical workflows comply with GxP principles and regulatory requirements for documentation and traceability.
What we're looking for
Bachelor’s degree in computer science or related field with 7 years experience, Master's with 6 years, or PhD with 2 years in IT and application development.
Experience building and deploying data science or machine learning solutions in scientific or engineering-intensive environments.
Expert-level Python proficiency including the scientific ecosystem (NumPy, pandas, scikit-learn, PyTorch/TensorFlow) and modern data engineering.
Mastery of business analytics tools including R, Dataiku, AWS SageMaker, Spark, and Tableau.
Experience with design of experiments (DoE), Bayesian methods, or active learning in scientific applications.
Familiarity with knowledge graph, retrieval-augmented, or orchestrated AI/LLM-based systems for technical domains.
Experience applying data science in GxP-regulated environments with knowledge of FDA/EMA requirements for process validation and control strategy.
Familiarity with MLOps principles and model lifecycle management in regulated or enterprise environments.
Advanced degree in Data Science, Biostatistics, Chemical Engineering, Computational Biology, or related quantitative fields (preferred).
5+ years of experience building and deploying data science solutions in scientific/engineering environments (preferred).
Experience with lab/manufacturing systems (LIMS, MES, DeltaV) and building scalable pipelines for process analytics (preferred).
Direct experience in biologics or bioprocess development in industrial or academic settings (preferred).
Familiarity with CMC development concepts, including process characterization, scale-up, and regulatory filing support (preferred).
Familiarity with biopharmaceutical data types such as bioreactor process data and chromatography profiles (preferred).
Familiarity with technology transfer workflows and commercial process validation in a pharmaceutical context (preferred).
Track record of scientific communication through publications or technical reports to convey complex analytical work (preferred).