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Senior Scientist I - II, Computational HitGen / LeadGen

AbbVie · North Chicago, IL · Full time · Other

Posted Sep 28, 2026 · We last checked this listing on Oct 6, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

About your past

Tell me about a computational prediction that turned out wrong. What did you learn?

What they are checking: Whether they can admit a wrong prediction and explain what it taught them, which shows scientific honesty.

Tell me about a method you developed that a team actually used.

What they are checking: Whether their methods actually helped a project team, showing impact beyond publications or prototypes.

How do you keep up with fast-moving AI drug discovery methods?

What they are checking: Whether they have a real habit of learning new methods critically, since AI drug discovery changes quickly and hype is common.

What would you do

A medicinal chemist doesn't trust your model's picks. How do you respond?

What they are checking: Whether they can explain their reasoning clearly and listen to chemists, since models only matter if the team trusts them.

About the work

How would you choose between structure-based virtual screening, ligand-based methods and generative design for a new target?

What they are checking: Whether they choose methods based on what is known about the target, rather than using one favorite approach for every project.

How do you validate a computational hit generation method before trusting it on a project?

What they are checking: Whether they test methods against known actives and decoys before relying on them, so projects do not chase false hits.

What are the strengths and limits of free energy perturbation for predicting binding affinity across diverse chemotypes?

What they are checking: Whether they understand where free energy perturbation works well and where it fails, which decides how much to trust its numbers.

How would you use machine learning to design compounds with target PK properties?

What they are checking: Whether they can use machine learning to balance potency and pharmacokinetic properties together, rather than optimizing one property at a time.

How do you set hit and lead criteria with a project team?

What they are checking: Whether they can help teams set practical criteria for hits and leads, so computational work supports real project decisions.

How would you sample a very large chemical space efficiently?

What they are checking: Whether they know practical ways to search huge libraries efficiently, which is becoming essential in modern hit finding.

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The full job description

As published by AbbVie.

Purpose: 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 methods for computational hit generation. 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 desirable project-relevant hit/lead criteria • Develop and maintain proficiency with the collection of cutting-edge computational methods available to the HGLG team • Support project teams by decisively identifying the method(s) that best fit the project goals, and deploying the most suitable method(s) for the task at hand • Effectively communicate to the project team the rationale for chosen methodologies, explaining how these approaches are optimally suited to the task and how they inform the resulting designs for a given target • Contribute to development of methods / workflows for computational hit generation, including methods to predict binding affinities of diverse chemotypes, to design compounds with specified pharmacological properties (PK parameters, etc.), and to rapidly sample both enumerated and AI-generated chemical spaces. Computational methods to evaluate / optimize synthetic tractability of designs from generative AI are of particular interest for this role. • 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) • 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) • Knowledge and experience in modern computational approaches for medicinal chemistry • Expertise using cutting-edge computational approaches built on Machine Learning/Deep learning and Cheminformatics • Ability to implement, debug, and maintain computational tools in common programming languages (e.g., Python) • Familiarity with AI/ML-enabled molecular generation, pose prediction, affinity prediction and/or prediction of pharmacological properties (e.g. PK) • Familarity with retrosynthetic analysis, to contribute to refining workflows for generation/prioritization of novel molecules on the basis of synthetic tractability • 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 • Preferred: has contributed to molecular design in an early-stage drug discovery campaign (hit gen / lead gen) Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law: ​ • The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and we may ultimately pay more or less than the posted range. This range may be modified in the future. ​ • We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees.​ • This job is eligible to participate in our long-term incentive programs. Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, incentive, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid and may be modified at the Company’s sole and absolute discretion, consistent with applicable law.​ AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community.  Equal Opportunity Employer/Veterans/Disabled. US & Puerto Rico only - to learn more, visit https://www.abbvie.com/join-us/equal-employment-opportunity-employer.html US & Puerto Rico applicants seeking a reasonable accommodation, click here to learn more: https://www.abbvie.com/join-us/reasonable-accommodations.html

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