Remote | Life Sciences Research Expert (Python) — $60–$120/hour
About The Position
This remote freelance consulting role is for an experienced life sciences researcher with strong Python programming skills. The consultant will contribute to advanced AI training and evaluation initiatives by developing and solving scientific research problems, conducting reproducible computational analyses, and assessing AI-generated scientific solutions for technical accuracy and methodological rigor. The work combines scientific expertise with Python-based data analysis, computational modeling, code validation, and structured research evaluation. The role covers scientific problem development, Python-based scientific computing, biological modeling, interdisciplinary research, AI solution evaluation, and research quality assurance. Relevant subject areas include biology, medicine, biomedical science, computational biology, neuroscience, genetics, genomics, molecular and cellular biology, biotechnology, bioinformatics, ecology, evolutionary biology, and biostatistics. The engagement is fully remote and open worldwide, with an advertised compensation range of $60–$120 per hour. Payment is output-based and made per completed task that meets project specifications. The expected commitment is approximately 15 hours per week, with flexible scheduling, including the option to choose working days and hours. Selection may include screening questions, an approximately 30-minute AI interview, and hiring manager review. Selected professionals should be ready to begin initial assignments within 24–48 hours after onboarding. The opportunity is available through 24-MAG LLC, which connects professionals with remote consulting and project-based work.
Requirements
- PhD, MD, or master's degree with substantial research experience in a relevant life sciences discipline.
- Strong background in scientific or applied research, demonstrated through publications, thesis work, computational projects, industry research, or equivalent accomplishments.
- Practical Python programming experience for scientific computing, data analysis, modeling, or simulation.
- Expertise in at least one relevant area, such as medicine, biomedical science, computational biology, neuroscience, or molecular and cellular biology.
- Strong understanding of research methodologies, statistical reasoning, and computational problem-solving.
- Ability to develop technically challenging scientific questions and produce accurate, well-supported solutions.
- Ability to review scientific code, validate computational results, and identify reasoning or implementation errors.
- Strong analytical, critical-thinking, and problem-solving capabilities.
- Strong written communication skills, including the ability to explain sophisticated scientific concepts and computational methods clearly.
- Exceptional attention to detail and commitment to scientific accuracy, reproducibility, and research quality.
- Ability to work independently, manage assigned research tasks, and meet established technical standards.
Nice To Haves
- Experience in biotechnology, bioinformatics, genetics, genomics, ecology, evolutionary biology, or biostatistics.
- Familiarity with scientific computing libraries such as NumPy, pandas, SciPy, Matplotlib, and scikit-learn, or with domain-specific research tools.
- Experience with Jupyter notebooks, reproducible computational workflows, and scientific datasets.
- Familiarity with AI evaluation, machine learning, or interdisciplinary computational research.
Responsibilities
- Develop technically rigorous scientific problems reflecting real-world challenges in biology, medicine, and related research disciplines.
- Create challenging research questions and realistic scientific scenarios involving biological systems, experimental data, and computational methods.
- Evaluate scientific concepts, assumptions, and methodologies for technical accuracy.
- Develop reproducible Python analyses, computational models, simulations, and data-processing workflows.
- Analyze scientific datasets using appropriate statistical, numerical, and computational techniques.
- Use Python libraries such as NumPy, pandas, SciPy, Matplotlib, and scikit-learn for research-oriented problems.
- Build clear, documented computational solutions that demonstrate sound scientific reasoning.
- Evaluate the accuracy, efficiency, and reproducibility of scientific code and analytical workflows.
- Apply relevant scientific expertise to problems in areas such as computational biology, neuroscience, genetics, and biomedical research.
- Interpret experimental findings, biological datasets, and computational results within appropriate scientific frameworks.
- Assess modeling techniques, statistical assumptions, and research methodologies.
- Review and validate AI-generated scientific analyses, computational solutions, and Python code.
- Identify errors in technical reasoning, scientific assumptions, mathematical calculations, and programming logic.
- Evaluate scientific responses for correctness, clarity, completeness, and methodological consistency.
- Provide structured feedback explaining inaccuracies, alternative approaches, and opportunities for improvement.
- Review scientific solutions and computational workflows for reproducibility, accuracy, and technical completeness.
- Explain analytical methods, assumptions, results, and scientific conclusions, and document Python implementations and research findings.
- Apply quality standards to scientific problem-solving, code review, and research evaluation, and provide constructive technical feedback.
- Complete assigned research tasks to project specifications and established technical standards.
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What This Job Offers
Career Level
Senior
