Scientific Python Evaluation Task Author

IXO is engaging specialists to evaluate STEM coding tasks that combine scientific understanding with executable evidence. Your contribution is technical work: make the reasoning inspectable, identify substantive errors and provide evidence that supports a reliable assessment.

Work you will do

• Translate a substantive scientific or technical problem into a well-defined Python-based task.
• Develop and test the computational approach, checking that the result makes sense within the relevant domain.
• Explain the solution and its limitations clearly, and provide reproducible evidence for evaluating other implementations.

Required background and routes

• Demonstrable expertise in a scientific or STEM discipline and practical Python skills, with strong quantitative reasoning and experience checking technical work.
• Identify assumptions, error sources and edge cases and explain solutions clearly. Research, teaching, industry, engineering and independent technical work are valid routes; no fixed degree or years threshold is imposed.
• Use appropriate scientific libraries, such as SymPy, SciPy, NumPy, pandas or Jupyter, without a requirement to know every named package.

DeliverablesSubmit the completed technical artifact or assessment with its supporting evidence, explicit assumptions, reproducible checks where applicable, and concise reasons for each material judgment. Address review findings within the agreed scope.

Location and schedule

Remote assignments are scheduled by agreement, with no guaranteed weekly volume. Availability planning can include 15 hours per week depending on the track. IXO confirms the applicable timing before work.

Pay and working terms

Per completed task; fee agreed before work USD. Payment is for a completed task that meets the agreed acceptance criteria. The fee, task specification and revision requirements are agreed before work; an advertised time estimate does not determine payment. Applying does not guarantee an assignment. Use public, licensed or otherwise authorized material only; do not submit confidential employer information, personal data or restricted research.