This role pays less than
53%
of similar roles. Most pay
$164,250–$254,812
— the shaded band above.
At the midpoint, this role pays about
$205k
versus about
$210k
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 Research Scientist II, Computational Sciences, MPDD serves as a scientific leader within the Molecular Profiling and Drug Delivery organization. This role focuses on advancing computational sciences to support drug discovery and development across synthetic modalities. The individual will lead the identification, development, and implementation of stage-appropriate computational models to optimize drug candidates from lead generation through clinical proof of concept. Key responsibilities include designing benchmarking studies, establishing performance metrics, and integrating tools into screening funnels in collaboration with medicinal chemists and data scientists. The role requires expertise in quantum mechanics, atomistic molecular simulations, including molecular dynamics, as well as machine learning, artificial intelligence, and hybrid physics-informed AI/ML models. This position addresses the technical challenge of establishing structure–property–performance correlations to ensure robust manufacturing processes and improve the developability of small-molecule candidates.
What does a Research Scientist earn?
Median $216250 from 42 postings across 13 companies.
Apply quantum mechanics, molecular dynamics, and AI/ML models to design and optimize drug candidates across synthetic modalities.
Lead the development and implementation of a portfolio of internal computational models and workflows for small-molecule needs.
Provide scientific leadership in using computational chemistry for molecular design, chemical transitions, and materials design.
Guide team members in executing benchmarking studies to evaluate model performance, applicability, and limitations.
Translate complex computational results into actionable recommendations for both technical and non-technical cross-functional partners.
Mentor early- and mid-career scientists within a matrixed environment to foster a collaborative research culture.
Identify emerging scientific trends and integrate them into long-term computational science strategies and functional objectives.
Build and maintain partnerships with external collaborators to source and leverage innovative technologies for drug development.
What we're looking for
A Bachelor's degree with 16+ years of experience, a Master's with 14+ years, or a PhD with 8+ years in a relevant scientific field is required.
Extensive knowledge and experience applying computational chemistry and data science to medicinal chemistry, drug discovery, and molecular optimization are required.
Expertise in quantum mechanics, molecular dynamics, atomistic molecular simulations, machine learning, artificial intelligence, and hybrid physics-informed AI/ML approaches is required.
Demonstrated experience developing, evaluating, and applying computational models and workflows across multiple stages of the drug discovery and development process is required.
Experience establishing model performance benchmarks, defining domains of applicability, and communicating limitations to multidisciplinary teams is required.
Proven ability to translate complex computational concepts into actionable recommendations for both technical and nontechnical audiences is required.
Experience leading complex research programs and initiatives in matrixed or direct-reporting environments is required.
Demonstrated experience mentoring and developing early- and mid-career scientists in a collaborative team environment is required.
Python
R
Machine Learning
Genomics
Data Science
Cloud Computing
Large Language Models
Generative AI
Statistics
Computational Biology
Toxicology
Pharmacology
Molecular Profiling
Analytical Workflows
Dashboards