Life Sciences Research Expert (Python)

$60–120/hr

rate as listed

Workload: ~15 hrs/week
Engagement: Project-based
Location: Worldwide
Field: Sciences

Create, solve, and review life sciences research problems used to evaluate advanced AI systems. The work combines domain expertise with reproducible Python analysis, code review, and scientific error detection.

The work

Contractors create technically rigorous problems in biology and related fields, then develop or review solutions. Day-to-day work may include analyzing scientific data, building models or simulations, checking code and reasoning, documenting reproducible workflows, and identifying scientific or computational errors.

What the screening covers

The platform looks for verifiable research experience in areas such as biomedical science, computational biology, neuroscience, molecular and cellular biology, biotechnology, bioinformatics, genetics, genomics, ecology, evolutionary biology, biostatistics, or biological data science. Python is expected as a strong, hands-on skill for scientific computing, data analysis, modeling, or simulation. Familiarity with NumPy, pandas, SciPy, Matplotlib, scikit-learn, Jupyter, or relevant domain-specific libraries is preferred.

Process and working terms

The process includes screening questions, an AI interview of approximately 30 minutes, and hiring manager review. The role is global, fully remote, and structured as contractor work at approximately 15 hours per week, with flexible scheduling and the option to work weekends.

Compensation and start timing

Pay band: $60–120/hr. Compensation is output-based, with payment for tasks that meet project specifications, and minimum submission requirements apply. The platform says it typically fills roles within 48 hours and expects selected experts to begin their first task within 24 to 48 hours after onboarding.

What they look for

Python
Biology
Medicine
Materials
Neuroscience
Astronomy

Included with this listing

Interview prep — 10 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: This tests whether you can substantiate domain depth through a defined research area, methods, outputs, and personal responsibility rather than broad familiarity with biology.
A strong answer: A strong answer identifies a focused domain, explains the research questions and methods involved, and names verifiable outputs such as publications, thesis work, computational projects, or industry research. It distinguishes the candidate's own contribution from the work of the wider team.
Where people lose it: Candidates lose points by listing many fields without demonstrating depth in any one of them or by describing a laboratory's work without clarifying their own role.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.