Senior Scientist I - II, Computational HitGen / LeadGen
Posted Sep 28, 2026 · We last checked this listing on Oct 6, 2026
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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As published by AbbVie.