Remote | Bioinformatics & Single-Cell Genomics Consultant — $60–$90/hour

24-Mag•New York, NY

8h•Remote

About The Position

We are sharing a specialised part-time consulting opportunity for bioinformatics and single-cell genomics professionals experienced in scanpy, scvelo, squidpy, gudhi, single-cell RNA-seq analysis, trajectory inference, spatial transcriptomics, topological data analysis, Python scientific computing, and benchmark evaluation workflows. This role supports current and upcoming remote consulting opportunities focused on bioinformatics problem design, scientific software evaluation, single-cell analysis workflow development, Python-based validation, and high-quality project execution. Selected professionals will design challenging research-grade computational problems, create precise expected outputs, develop validation logic, and refine task difficulty based on structured calibration feedback.

Requirements

Graduate-level expertise in bioinformatics, computational biology, single-cell genomics, genomics, systems biology, biomedical data science, or a closely related STEM field
MS, PhD, or equivalent research experience in a relevant domain
Hands-on experience using scanpy, scvelo, squidpy, gudhi, or other specialized bioinformatics tools in real research workflows
Strong Python programming skills for scientific computing, validation logic, and task setup
Experience writing code that calls scientific software libraries to solve actual research problems
Ability to understand software edge cases, pipeline limitations, and what makes a problem genuinely difficult
Comfort working independently and iterating on problem designs based on feedback
Ability to work in Linux, terminal-based environments, or remote compute sandboxes

Nice To Haves

Experience across multiple bioinformatics tools, single-cell methods, or computational biology domains
Familiarity with benchmark design, evaluation design, calibration workflows, or technical task validation
Background in scientific pedagogy, exam design, problem-set creation, or graduate-level technical assessment
Experience with computational reproducibility, containerized environments, remote compute, or research software packaging
MS, PhD, or equivalent research experience in bioinformatics, computational biology, genomics, single-cell biology, biomedical data science, systems biology, molecular biology, statistics, or a related technical field is preferred
Research publications, open-source contributions, professional scientific software work, or applied computational biology experience are highly relevant
Experience designing or validating single-cell analysis pipelines may be especially valuable

Responsibilities

Design original graduate-level computational problems grounded in real bioinformatics and single-cell genomics workflows
Create tasks involving single-cell RNA-seq analysis, trajectory inference, spatial transcriptomics, multi-omic integration, or topological data analysis
Develop problems around cell-type annotation, pseudotime ordering, spatial variable gene identification, persistence-based analysis, or analysis pipeline interpretation
Design tasks that require precise outputs from fully specified computational setups as well as strategic reasoning about partial observations and hidden structure in data
Work with scanpy, scvelo, squidpy, gudhi, or related specialized bioinformatics software tools
Create problems that require sophisticated use of domain-specific libraries rather than surface-level biological knowledge alone
Evaluate whether tasks require genuine computational biology reasoning, methodological judgment, and careful interpretation of outputs
Identify edge cases, software limitations, pipeline artifacts, or reasoning traps that make a problem genuinely challenging
Write Python problem setups, oracle functions, and solution validators
Refine tasks through calibration feedback until the difficulty reaches the target range
Create structured evaluation materials that support reproducible scoring and consistent review
Maintain technical accuracy, reproducibility, and clear documentation across submitted task materials