About the job Remote | Bioinformatics Scientist (Genomics & Statistical Genetics) - Up to $75/hour We are sharing a specialised part-time consulting opportunity for PhD-level computational genomics and bioinformatics professionals with deep expertise in statistical modelling, biological data analysis, R, Python, and reproducible computational research. This role focuses on completing realistic, multi-step scientific analysis workflows across computational genomics, quantitative biology, and translational biomedicine. Selected experts will work from complex biological datasets through quality control, statistical analysis, interpretation, and structured scientific reporting. Key Responsibilities Computational Genomics Analysis Complete complex data-analysis tasks involving genomics and biomedical datasets Work with sequencing, single-cell, population-genetics, QTL/GWAS, and related omics data Analyse biological datasets from initial inspection through final interpretation Identify relevant biological signals, analytical limitations, and potential confounders Apply appropriate computational methods to realistic research questions Statistical Bioinformatics Select and apply appropriate statistical methods for biological and biomedical data Develop and evaluate statistical models for complex genomic datasets Assess assumptions, uncertainty, model limitations, and robustness Distinguish meaningful biological findings from technical or statistical artefacts Apply rigorous scientific inference throughout multi-stage analyses Data Quality & Exploratory Analysis Inspect raw or messy datasets for completeness, structure, and data-quality issues Perform quality control and exploratory data analysis Identify outliers, batch effects, missingness, confounding, and other analytical challenges Determine appropriate preprocessing and transformation strategies Document key analytical decisions and their implications R & Python Workflows Develop and execute scientific analysis workflows in R and Python Write, debug, and explain analysis code Use relevant statistical, genomic, and scientific software libraries Build reusable scripts and structured computational workflows Validate analytical outputs for correctness and reproducibility Scientific Workflow Design Navigate ambiguous research problems requiring independent analytical judgement Determine appropriate analytical steps when workflows are not fully specified Identify alternative approaches and evaluate their strengths and limitations Progress from raw data through modelling, interpretation, and final output Ensure analytical choices remain aligned with the underlying scientific question Biological & Translational Interpretation Interpret statistical results within their biological or translational context Connect quantitative findings to underlying mechanisms or research objectives Communicate assumptions, uncertainty, limitations, and conclusions clearly Avoid overinterpretation of weak or incomplete evidence Produce conclusions that accurately reflect both statistical and biological evidence Reproducible Research & Documentation Create reproducible scientific analyses using scripts, notebooks, and structured workflows Document methods, assumptions, parameters, and analytical decisions precisely Use command-line tools and remote computational environments where appropriate Maintain clear provenance between source data, transformations, analyses, and conclusions Apply version-control or workflow-management practices where relevant Ideal Profile PhD required in computational biology, bioinformatics, statistical genetics, quantitative biology, biostatistics, genomics, or a closely related field Deep hands-on experience analysing biological or biomedical datasets Strong experience with genomics, sequencing, single-cell, population genetics, QTL/GWAS , or related omics workflows Professional fluency in R and Python Ability to write, debug, and explain scientific analysis code Strong foundation in statistical modelling, experimental design, quality control, and scientific inference Demonstrated ability to independently complete multi-step computational research workflows Strong experience working from raw or imperfect data through final scientific interpretation Clear scientific writing and ability to document methods, assumptions, and results precisely Familiarity with notebooks, scripts, version control, workflow tools, or other reproducible-research practices is advantageous Comfortable collaborating with multidisciplinary scientific and technical teams Engagement Details Part-time independent contractor engagement Fully remote Open to qualified experts globally Flexible scheduling based on project requirements

Compensation:

Up to $75/hour Project-based work involving computational genomics, statistical bioinformatics, quantitative biology, and translational biomedical analysis Work may include data inspection, quality control, statistical modelling, coding, scientific interpretation, and structured reporting 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

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