Training Turk

Bioinformatics Scientist

$90–120/hr · micro1

Analyze medicinal chemistry datasets and evaluate AI model outputs for scientific accuracy, guiding development of AI systems for drug discovery and molecular analysis.

What you would do

  • Analyze large-scale omics and cheminformatics datasets using specialized bioinformatics tools and methodologies
  • Curate and annotate chemical and biological datasets for AI training, ensuring accuracy and relevance to drug discovery
  • Evaluate AI-generated chemical insights and recommendations for scientific accuracy and practical feasibility
  • Synthesize findings across biological, chemical, and clinical data to provide comprehensive scientific assessment
  • Deliver written feedback and improvement recommendations based on deep domain expertise and analysis

Who they want

  • Advanced degree (MSc, PhD preferred) in computational biology, medicinal chemistry, chemistry informatics, or related discipline
  • Comprehensive knowledge of medicinal chemistry with focus on molecular structure-activity correlations and therapeutic design concepts
  • Demonstrated experience handling and interpreting large omics or cheminformatics datasets from real research
  • Proficiency with bioinformatics software tools, databases, and programming languages like Python, R, RDKit, KNIME
  • Strong scientific communication skills for articulating complex concepts and technical feedback clearly

Main skills

Medicinal chemistry

What the interview asks about

  1. 1.Structure-activity relationship interpretation

    AI models need to learn how molecular modifications affect biological activity, requiring experts to evaluate whether models understand these critical relationships.

    For example: “An AI predicts that removing a methyl group from a compound will increase binding affinity. Your experimental data shows the opposite result. How would you explain this discrepancy and assess the model's understanding?”

  2. 2.Dataset accuracy and relevance assessment

    Poor quality training data leads to flawed AI reasoning, so experts must identify questionable data points, measurement errors, or irrelevant samples.

    For example: “A cheminformatics dataset includes 50 compounds with similar structures but highly variable bioactivity. How would you evaluate whether this data is suitable for AI training or if quality issues need addressing?”

  3. 3.AI output scientific validity

    AI systems produce plausible-sounding but sometimes incorrect chemical predictions, requiring experts to spot errors in reasoning about molecular properties and reactions.

    For example: “An AI recommends a new drug candidate with a cyclopropane ring, claiming it will improve membrane permeability. Is this recommendation sound? What scientific questions would you raise about this suggestion?”

  4. 4.Experimental design understanding

    Evaluating AI model performance on experimental data requires understanding study design, controls, and potential confounds that affect data interpretation.

    For example: “AI evaluated bioactivity from a cell-based assay but didn't account for the assay's known interference with lipophilic compounds. How would you document this limitation in your feedback to the development team?”

A task you may get

Analyze a medicinal chemistry dataset with 20-30 compounds, identify quality issues, and provide written assessment of whether the data is suitable for AI training.

How to prepare

  • Review recent advances in AI applications for drug discovery and molecular property prediction to understand current model capabilities
  • Collect and organize samples of your prior work with large-scale chemical or biological datasets showing your analysis depth
  • Study how structure-activity relationships and molecular properties are typically described in scientific literature and databases
  • Familiarize yourself with current standards and best practices for annotating chemical and biological datasets for machine learning

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

We wrote this page from the public micro1 listing. It may be out of date, so read the full posting before you apply.