Computational Biology Expert

What you would do

• Analyze biological datasets and pharmaceutical data, marking errors, gaps, and areas requiring domain expertise
• Create realistic problem sets and scenarios drawn from actual drug discovery challenges and workflows
• Evaluate AI model outputs for scientific accuracy and propose targeted improvements based on your expertise
• Collaborate asynchronously with other specialists to review annotations and strengthen data standards
• Document methodology improvements that help scale data quality across future computational biology projects

Who they want

• Advanced degree holder (PhD, PharmD, or MSc) in computational biology, bioinformatics, or medicinal chemistry
• Demonstrated track record in drug discovery, molecular design, or cheminformatics applications
• Proficiency with computational tools for chemistry analysis, such as molecular modeling or cheminformatics software
• Strong technical communication skills to explain complex chemical concepts to AI engineers and other audiences
• Experience working on scientific projects that bridged domain knowledge with technology development

Main skills

What the interview asks about

1. 1.Identifying dataset quality issues

   AI training data must be accurate in pharmaceutical contexts where molecular properties directly impact drug safety. Interviewers need confidence you catch errors others miss.

   For example: “You're reviewing a dataset of drug-protein binding affinities and notice several entries show impossible values. How would you decide whether to correct, flag, or remove these records?”

2. 2.Designing educational scenarios

   Problem sets you create train the next generation of AI models to reason about real drug discovery decisions. Your scenarios must reflect genuine constraints and trade-offs.

   For example: “Create a case study where a lab must choose between three lead compounds with different potency, selectivity, and synthetic complexity profiles. What information should an AI system consider?”

3. 3.Validating model reasoning

   You assess whether AI systems understand the underlying chemistry, not just pattern-matching. This requires you to trace the model's logic against your domain knowledge.

   For example: “An AI model predicts that adding a methyl group to a compound will improve its blood-brain barrier penetration. How would you test whether that prediction reflects real chemistry or just correlation?”

4. 4.Communicating with non-chemists

   Your feedback must guide AI engineers who may not have chemistry training. Clear, structured explanations of why data is correct or incorrect enable teams to iterate.

   For example: “An AI engineer asks why certain molecular descriptors in your dataset seem inconsistent with each other. Walk through how you'd explain the chemical relationships they're seeing.”

5. 5.Methodology refinement

   Your role includes improving how data is curated and annotated for future projects. You identify patterns in errors and suggest systematic changes that scale.

   For example: “After annotating 200 protein structures, you notice your reviews catch conformational issues but miss glycosylation errors. What process change would help the team catch both?”

A task you may get

Review a small pharmaceutical dataset with 5-8 entries (molecular structures, properties, test results), identify errors, explain your reasoning for each, and propose one annotation standard improvement.

How to prepare

• Refresh your knowledge of current drug discovery workflows and regulatory constraints in pharmaceutical development
• Review examples of how computational models handle molecular representations and property predictions
• Prepare specific examples of errors you've caught in real datasets and how you resolved them
• Think through how you'd explain 2-3 complex chemistry concepts (e.g., selectivity, bioavailability) to an engineer without chemistry training

The facts

Pay

$90–120/hr

Open to

Bangladesh, Hong Kong, India, Indonesia, Japan, Kazakhstan, Kyrgyzstan, Malaysia, Pakistan, Philippines, Singapore, Sri Lanka, Taiwan, Thailand, Uzbekistan, Vietnam, Austria, Belarus, Belgium, Denmark, France, Germany, Greece, Italy, Netherlands, Portugal, Russia, Spain, Switzerland, United Kingdom, Argentina, Brazil, Chile, Colombia, Mexico, Peru, Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Tunisia, United Arab Emirates, United States, Canada, Nigeria, Kenya, South Africa, Ghana, Ethiopia

Field

Applied Engineering

Role type

Generalist

Posted

7/28/2026

Places left

30

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