Remote | Computational Biologist (Genetics, Biochemistry & Ecology) — Up to $60/hour

We are sharing a specialised part-time consulting opportunity for PhD-level biologists with strong scientific computing expertise across genetics, biochemistry, ecology, and computational research. This role focuses on designing original, executable research problems grounded in authentic biological workflows. Selected experts will develop challenging coding-based tasks, create rigorous reference solutions and grading criteria, and test and refine problems until they require genuine research-level biological and computational reasoning. Key Responsibilities Computational Biology Problem Design Develop original research-level computational biology problems Build tasks from published papers, public datasets, open-source repositories, or independently designed scientific scenarios Create problems requiring multi-step biological, statistical, and computational reasoning Ensure tasks reflect realistic research workflows rather than standard textbook exercises Design problems with scientifically defensible and reproducible solutions Genetics & Genomic Analysis Develop computational tasks involving genetics and related biological datasets Create problems requiring interpretation of inheritance, variation, population, or molecular genetic information Incorporate realistic biological assumptions, experimental constraints, and data limitations Design tasks requiring quantitative and computational investigation Apply advanced genetics expertise to task validation and difficulty calibration Biochemistry & Molecular Biology Create problems involving biochemical pathways, molecular interactions, kinetics, or related quantitative biology Develop workflows requiring analysis of experimental or simulated biochemical data Evaluate assumptions, parameters, and biological interpretation Incorporate realistic experimental limitations and edge cases Ensure problems require substantive biochemical reasoning rather than surface-level recall Ecology & Quantitative Biology Develop computational tasks involving ecological systems, populations, communities, or environmental datasets Design problems requiring statistical modelling, simulation, or interpretation of complex biological relationships Evaluate assumptions involving sampling, variability, environmental effects, and biological interactions Create scenarios that require careful interpretation of noisy or incomplete data Apply quantitative reasoning to realistic ecological research questions Scientific Computing & Programming Write and validate scientific workflows using Python, R, or another relevant programming language Develop computational setups, reference calculations, and solution validators Debug scientific code and identify implementation or numerical issues Build reproducible workflows suitable for automated testing Ensure computational outputs accurately reflect the underlying biological problem Research Task Development Source appropriate scientific material from research papers, datasets, repositories, or original scenarios Translate complex biological research into clearly specified computational tasks Define inputs, assumptions, constraints, and expected outputs Create assignments requiring both biology expertise and coding proficiency Ensure task difficulty comes from scientific reasoning rather than unnecessary complexity Reference Solutions & Grading Criteria Produce authoritative reference solutions and supporting computational analyses Define clear criteria describing what constitutes a correct solution Identify essential biological reasoning, computational steps, and expected outputs Develop grading logic capable of distinguishing correct solutions from plausible but flawed approaches Ensure evaluation criteria are precise, consistent, and reproducible Testing & Difficulty Calibration Test tasks against advanced computational systems Analyse scientific, computational, and reasoning failure modes Refine prompts, inputs, constraints, and expected outputs based on testing Adjust task difficulty while preserving scientific validity Finalise tasks only when they reliably require advanced biological and computational expertise Research Engineering Workflow Work through a Git/GitHub pull-request workflow Run and validate code within Docker-based environments Respond to automated quality checks and reviewer feedback Maintain clean, reproducible code and supporting documentation Collaborate effectively within structured scientific software workflows Ideal Profile PhD required in Biology, Biological Sciences, Biochemistry, Genetics, Ecology, or a closely related field Demonstrated expertise in at least two of the following: Genetics Biochemistry Ecology Strong working proficiency in Python, R, or another scientific programming language Hands-on experience using code for biological research, modelling, simulation, or data analysis Comfortable with Git/GitHub Experience running code in Docker or other containerised environments Strong understanding of reproducible scientific computing Ability to translate advanced biological research into clearly defined computational problems Peer-reviewed publications are highly valued Prior scientific software or research engineering experience is advantageous Strong written communication and ability to document biological assumptions, methods, and solutions precisely Engagement Details Part-time independent contractor engagement Fully remote 20+ hours per week Initial duration of approximately 6 weeks Immediate start Compensation: Up to $60/hour Work includes computational biology problem design, scientific coding, reference-solution development, grading criteria, testing, and task refinement Projects may be extended, shortened, or concluded based on project needs and performance Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party H1-B and STEM OPT support is unavailable for this engagement About the Platform This opportunity is available through 24-MAG LLC. 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