Principal Research Scientist I, Biologics CMC Developability Modeling

Overview
The Development Sciences Biologics Drug Product Development organization within AbbVie develops and characterizes drug product formulations and manufacturing processes to provide clinical dosage forms to enable the development of AbbVie’s pipeline. AbbVie is seeking a highly motivated person to join the Biologics CMC Developability Team. Our team utilizes start-of-the-art biophysical and analytical instrumentation to assess candidates coming out of the Discovery pipeline. We collaboratively influence protein design paradigms to yield biologic candidates that will be successful drug products. Key to our success is developing models of protein structure and function to augment decision making in protein design and development. We utilize high throughput instrumentation and digital data systems to capture ML-ready data on industry leading datasets of therapeutic proteins. To drive maximum benefit from this data, we are seeking an experienced scientist to lead modeling efforts within our team. The successful candidate will bring deep experience of modeling protein stability or related properties to solve foundational questions such as viscosity, chemical degradation, and aggregation. Applicable modeling techniques could range from first principles to statistical based approaches. This role includes the opportunity to design molecules and custom studies for data generation, so knowledge of biochemistry experimentation is useful. The candidate will be a member of a global team and will work closely with scientists from multiple functional areas, including Discovery, Development Science Digital Transformation, and Information Research.
Key Responsibilities:
Serve as a technical expert for modeling within Biologics CMC Developability Team
Represent BCD in modeling communities within AbbVie; collaborates with other modelers or teams to ensure business needs are met by the correct modeling framework
May perform in vitro laboratory experiments; works closely with teams for design of wet lab studies to build both models and fundamental knowledge of protein solution behavior
Identifies emerging scientific and technological trends from multiple internal and external sources, assesses relevance and integrates applicable trends into BCD plans and strategies as appropriate. Present data at internal and external professional conferences and prepare manuscripts for publication in peer reviewed journals or patent applications
May serve as BCD Lead, coordinate developability assessment activities for 1-2 Discovery Projects at a time
Experience with cross-functional decision making across reporting lines
BS, MS, or PhD in Pharmaceutics, Pharmaceutical Sciences, Chemistry, Chemical Engineering, or related disciplines with 14+ (Bachelors), 12+ (Masters), or 6+ (PhD) years of relevant industry experience.
Proven ability to solve critical scientific problems.
Direct experience with applying modeling techniques (mechanistic or statistical) to protein long-term stability or formulation screening such as kinetic degradation modeling
Well versed in predictive machine learning and statistical modeling techniques
Familiar with complex ML architectures including deep learning and hierarchical or multi-task frameworks
Skilled with feature attribution, sensitivity analysis, and model interpretability
Key Competencies:
Build strong relationships with peers and cross functionally with partners outside of the team to enable higher performance.
Excellent oral communication skills in a scientific setting as well as sound technical writing and documentation competencies are required.
The candidate must be able to work independently, respond to changing priorities and short lead times for multiple tasks; able to identify problems and work toward solutions.
Raises the bar and is never satisfied with the status quo.
Creates a learning environment, open to suggestions and experimentation for improvement.
Embraces the ideas of others, nurtures innovation and manages innovation to reality.

Skills
Machine Learning, Deep Learning, Technical Writing, Documentation

Employment type
Full-time

Work arrangement
Yes