Senior Scientist I-II, ML Molecular Optimization

This position brings an opportunity to make innovative contributions that will define how AbbVie leverages AI-enabled drug discovery across our small molecule portfolio. The key responsibilities of this position are to identify, develop, optimize, validate, and deploy innovative machine learning approaches to guide molecular optimization. This role will collaborate closely with project teams comprising medicinal chemists, structural biologists, biochemists, cell biologists, and other computational chemistry / chemoinformatics experts.
Responsibilities:
Consult with project team to establish quantitative targets for driving optimization
Identify suitable models to predict small molecule binding affinities for protein targets, and to predict pharmacological properties (PK parameters, etc.)
Build predictive models for each project endpoint, and carefully benchmarking to evaluate performance and domain of applicability for each model
Decisively identify the model that should be used for each project team query molecule, using data-driven approaches that take account of model uncertainty and heteroscedasticity
Clearly communicate to the project team the rationale for model selection and why the selected models are best suited for the task at hand
Deploy models to ensure accessibility for the project team, facilitating collaborative ideation and optimization
Senior Scientist I: Degree in relevant field (chemistry / computer science / machine learning / cheminformatics / chemical engineering) with experience developing machine learning models related to chemical and biological data (10+ years of experience for BS, 8+ years for MS, 0+ years for PhD). Leveling will be commensurate to experience.
Senior Scientist II: Degree in relevant field (chemistry / computer science / machine learning / cheminformatics / chemical engineering) with experience developing machine learning models related to chemical and biological data (12+ years of experience for BS, 10+ years for MS, 4+ years for PhD). Leveling will be commensurate to experience.
Expertise with developing, implementing and deploying programs and computational solutions employing Machine Learning/Deep learning and Cheminformatics
Expertise in AI/ML-enabled molecular generation, pose prediction, affinity prediction and/or prediction of pharmacological properties (e.g. PK)
Strong programming skills in Python and experience with data science stack including numpy, pandas, scikit-learn, and other related scientific libraries
Ability to implement, debug, and maintain computational tools in common programming languages (Python, etc…) and proficiency with cloud computing capabilities
Familiarity with modern deep learning architectures including GNN, CNN, RNN, Transformer, GCNN and MPNN, and machine learning paradigms such as generative models, GAN, and active learning
Strong analytical and problem-solving skills with demonstrated ability to think critically and creatively, and provide solutions both individually and collaboratively with internal experts
Excellent ability to communicate clearly and concisely with colleagues and collaborators including an ability to explain complex ideas to non-specialists

Employment type
Full-time

Work arrangement
Hybrid