Remote | Computational Biologist (Single-Cell Genomics) — $60–$90/hour

24-Mag • New York, NY

3d • Remote

About The Position

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.

Requirements

• Graduate-level expertise in single-cell genomics
• Expertise in scientific programming
• Expertise in advanced computational analysis
• Master's degree, PhD, or equivalent research experience in Bioinformatics, Computational Biology, Genomics, Systems Biology, or a closely related STEM discipline
• Strong hands-on experience with single-cell genomics and computational biological analysis
• Proven proficiency with one or more specialised tools such as Scanpy, scVelo, Squidpy, GUDHI, or comparable scientific software
• Experience applying these tools to real research or professional projects
• Strong understanding of single-cell RNA-seq analysis, trajectory inference, spatial transcriptomics, or multi-omic integration
• Strong Python programming skills
• Ability to design rigorous computational problems and independently validate solutions
• Comfortable working in Linux and terminal-based environments

Nice To Haves

• Research publications, open-source contributions, or substantial professional computational biology work are highly valued
• Experience with scientific teaching, problem-set design, computational reproducibility, or containerised environments is advantageous

Responsibilities

• Create original graduate-level computational problems in bioinformatics and single-cell genomics
• Develop tasks based on realistic research workflows and biological datasets
• Design multi-step problems requiring scientific interpretation and computational reasoning
• Create reproducible tasks with clearly defined inputs, expected outputs, and validation criteria
• Refine task difficulty based on testing and feedback
• Develop problems involving preprocessing, quality control, dimensionality reduction, clustering, and downstream analysis
• Design workflows using tools such as Scanpy and related single-cell Python libraries
• Create tasks involving cell-type annotation and biological interpretation
• Evaluate analytical choices, parameter settings, and potential failure modes
• Incorporate realistic edge cases encountered in single-cell datasets
• Create computational problems involving pseudotime and cellular trajectory inference
• Apply tools such as scVelo to RNA velocity and dynamic-state analysis
• Develop tasks requiring interpretation of lineage relationships and cell-state transitions
• Evaluate assumptions underlying trajectory and velocity models
• Design problems where multiple plausible biological interpretations must be distinguished through careful analysis
• Develop problems involving spatially resolved gene-expression data
• Work with tools such as Squidpy and related spatial-analysis frameworks
• Create tasks involving spatially variable gene identification and neighbourhood analysis
• Evaluate spatial relationships between cell populations and molecular features
• Design workflows combining imaging, expression, and spatial information where relevant
• Create problems involving integration of multiple molecular data types
• Develop workflows requiring alignment and interpretation across complementary genomic measurements
• Assess batch effects, biological variation, and integration quality
• Design tasks requiring careful selection of analytical approaches
• Evaluate whether computational conclusions are appropriately supported by the underlying data
• Develop specialised problems involving topological data analysis where relevant
• Apply tools such as GUDHI and related computational frameworks
• Create persistence-based analysis workflows
• Interpret topological structure within high-dimensional biological datasets
• Incorporate advanced quantitative methods where they provide meaningful biological insight
• Write computational problem setups, oracle functions, and solution validators
• Use Python to build reproducible scientific workflows
• Verify numerical and biological correctness of expected outputs
• Identify software limitations, computational edge cases, and analytical failure modes
• Document assumptions, dependencies, parameters, and validation logic clearly
• Test computational tasks against advanced systems
• Determine whether problems require genuine scientific reasoning rather than surface-level pattern matching
• Identify tasks that are too easy, ambiguous, or computationally impractical
• Refine prompts, constraints, datasets, and expected outputs to reach the intended difficulty
• Maintain strong standards of scientific accuracy and reproducibility

Benefits

• Part-time independent contractor engagement
• Fully remote
• Expected commitment of at least 15–20 hours per week
• Flexible scheduling based on project requirements
• Compensation: $60–$90/hour
• Work involves computational problem design, reference-solution development, scientific validation, and iterative 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

What This Job Offers

• Job Type: Part-time
• Career Level: Mid Level