Data Scientist II, ML Expert
AbbVie
August 25, 2026
Remote friendly (North Chicago, IL)
United States
$84,500 - $162,000 USD yearly
IT
Job Description
Conceive and implement novel experimental approaches to answer scientific questions. Investigate, identify, develop, and optimize new methods and techniques. Contribute to project science in area of expertise. Develop productive collaborations and communications with other groups across science disciplines.
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 pharmacological properties (e.g., PK parameters)
- Build predictive models for each relevant endpoint; benchmark performance and domain of applicability
- Select the appropriate model for each query molecule using data-driven approaches accounting for model uncertainty and heteroscedasticity
- Communicate rationale for model selection and why selected models are best suited for the task
Qualifications
- Bachelor’s (typically 10 years), Master’s (typically 8 years), or PhD (no experience required); relevant degree (chemistry/computer science/ML/cheminformatics/chemical engineering) with experience developing ML models related to chemical and biological data (preferred)
- Thorough theoretical and practical understanding of scientific discipline
- Expertise developing, implementing, and deploying ML/deep learning and cheminformatics computational solutions
- Expertise in AI/ML-enabled molecular generation, pose prediction, affinity prediction, and/or prediction of pharmacological properties (e.g., PK)
- Strong Python skills; data science stack (numpy, pandas, scikit-learn, related libraries)
- Ability to implement/debug/maintain computational tools and proficiency with cloud computing
- Familiarity with deep learning architectures (GNN, CNN, RNN, Transformer, GCNN, MPNN) and paradigms (generative models, GAN, active learning)
- Strong analytical/problem-solving skills; effective collaboration and communication
Benefits
- Paid time off (vacation, holidays, sick), medical/dental/vision insurance, and 401(k) to eligible employees
- Short-term incentive program eligibility
Conceive and implement novel experimental approaches to answer scientific questions. Investigate, identify, develop, and optimize new methods and techniques. Contribute to project science in area of expertise. Develop productive collaborations and communications with other groups across science disciplines.
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 pharmacological properties (e.g., PK parameters)
- Build predictive models for each relevant endpoint; benchmark performance and domain of applicability
- Select the appropriate model for each query molecule using data-driven approaches accounting for model uncertainty and heteroscedasticity
- Communicate rationale for model selection and why selected models are best suited for the task
Qualifications
- Bachelor’s (typically 10 years), Master’s (typically 8 years), or PhD (no experience required); relevant degree (chemistry/computer science/ML/cheminformatics/chemical engineering) with experience developing ML models related to chemical and biological data (preferred)
- Thorough theoretical and practical understanding of scientific discipline
- Expertise developing, implementing, and deploying ML/deep learning and cheminformatics computational solutions
- Expertise in AI/ML-enabled molecular generation, pose prediction, affinity prediction, and/or prediction of pharmacological properties (e.g., PK)
- Strong Python skills; data science stack (numpy, pandas, scikit-learn, related libraries)
- Ability to implement/debug/maintain computational tools and proficiency with cloud computing
- Familiarity with deep learning architectures (GNN, CNN, RNN, Transformer, GCNN, MPNN) and paradigms (generative models, GAN, active learning)
- Strong analytical/problem-solving skills; effective collaboration and communication
Benefits
- Paid time off (vacation, holidays, sick), medical/dental/vision insurance, and 401(k) to eligible employees
- Short-term incentive program eligibility