We are sharing a specialised part-time consulting opportunity for computational biologists and bioinformatics professionals with graduate-level expertise in single-cell genomics, scientific programming, and advanced computational analysis.
This role supports a research project focused on challenging computational biology problems grounded in real scientific workflows. Selected experts will design original graduate-level tasks involving single-cell RNA sequencing, trajectory inference, spatial transcriptomics, multi-omic analysis, and related computational methods, then develop reference solutions and refine tasks through iterative testing.
Key Responsibilities
Computational Biology Problem Design
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Create original graduate-level computational problems in bioinformatics and single-cell genomics
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Develop tasks based on realistic research workflows and biological datasets
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Design multi-step problems requiring scientific interpretation and computational reasoning
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Create reproducible tasks with clearly defined inputs, expected outputs, and validation criteria
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Refine task difficulty based on testing and feedback
Single-Cell RNA-Seq Analysis
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Develop problems involving preprocessing, quality control, dimensionality reduction, clustering, and downstream analysis
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Design workflows using tools such as Scanpy and related single-cell Python libraries
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Create tasks involving cell-type annotation and biological interpretation
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Evaluate analytical choices, parameter settings, and potential failure modes
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Incorporate realistic edge cases encountered in single-cell datasets
Trajectory & Dynamic Analysis
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Create computational problems involving pseudotime and cellular trajectory inference
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Apply tools such as scVelo to RNA velocity and dynamic-state analysis
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Develop tasks requiring interpretation of lineage relationships and cell-state transitions
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Evaluate assumptions underlying trajectory and velocity models
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Design problems where multiple plausible biological interpretations must be distinguished through careful analysis
Spatial Transcriptomics
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Develop problems involving spatially resolved gene-expression data
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Work with tools such as Squidpy and related spatial-analysis frameworks
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Create tasks involving spatially variable gene identification and neighbourhood analysis
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Evaluate spatial relationships between cell populations and molecular features
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Design workflows combining imaging, expression, and spatial information where relevant
Multi-Omic & Integrated Analysis
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Create problems involving integration of multiple molecular data types
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Develop workflows requiring alignment and interpretation across complementary genomic measurements
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Assess batch effects, biological variation, and integration quality
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Design tasks requiring careful selection of analytical approaches
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Evaluate whether computational conclusions are appropriately supported by the underlying data
Topological & Advanced Data Analysis
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Develop specialised problems involving topological data analysis where relevant
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Apply tools such as GUDHI and related computational frameworks
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Create persistence-based analysis workflows
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Interpret topological structure within high-dimensional biological datasets
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Incorporate advanced quantitative methods where they provide meaningful biological insight
Scientific Programming & Validation
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Write computational problem setups, oracle functions, and solution validators
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Use Python to build reproducible scientific workflows
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Verify numerical and biological correctness of expected outputs
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Identify software limitations, computational edge cases, and analytical failure modes
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Document assumptions, dependencies, parameters, and validation logic clearly
Problem Testing & Refinement
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Test computational tasks against advanced systems
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Determine whether problems require genuine scientific reasoning rather than surface-level pattern matching
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Identify tasks that are too easy, ambiguous, or computationally impractical
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Refine prompts, constraints, datasets, and expected outputs to reach the intended difficulty
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Maintain strong standards of scientific accuracy and reproducibility
Ideal Profile
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Master's degree, PhD, or equivalent research experience in Bioinformatics, Computational Biology, Genomics, Systems Biology, or a closely related STEM discipline
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Strong hands-on experience with single-cell genomics and computational biological analysis
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Proven proficiency with one or more specialised tools such as Scanpy, scVelo, Squidpy, GUDHI, or comparable scientific software
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Experience applying these tools to real research or professional projects
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Strong understanding of single-cell RNA-seq analysis, trajectory inference, spatial transcriptomics, or multi-omic integration
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Strong Python programming skills
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Ability to design rigorous computational problems and independently validate solutions
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Comfortable working in Linux and terminal-based environments
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Research publications, open-source contributions, or substantial professional computational biology work are highly valued
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Experience with scientific teaching, problem-set design, computational reproducibility, or containerised environments is advantageous
Engagement Details
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Part-time independent contractor engagement
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Fully remote
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Expected commitment of at least 15–20 hours per week
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Flexible scheduling based on project requirements
- Compensation: $60–$90/hour
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Work involves computational problem design, reference-solution development, scientific validation, and iterative task refinement
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Projects may be extended, shortened, or concluded based on project needs and performance
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Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party
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H1-B and STEM OPT support is unavailable for this engagement
About the Platform
This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.
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