Complete realistic, multi-step scientific data-analysis tasks in computational genomics, quantitative biology, and translational biomedicine
Independently inspect datasets, perform quality control and exploratory analysis, select appropriate statistical methods, and execute analyses in R and Python
Navigate ambiguous research workflows by identifying key analytical decisions, potential confounders, and limitations in the available data
Use scientific software, code, and command-line tools to generate reproducible analyses and structured final outputs
Interpret results in the context of the underlying biological or translational question, clearly communicating assumptions, uncertainty, and conclusions
Work with a multidisciplinary team of scientists and AI research specialists
PhD in computational biology, bioinformatics, statistical genetics, quantitative biology, biostatistics, genomics, or a closely related field
Deep, hands-on experience analyzing biological or biomedical data, especially genomics, sequencing, single-cell, population-genetics, QTL/GWAS, or related omics datasets
Professional fluency in R and Python, including the ability to write, debug, and explain analysis code
Strong foundation in statistical modeling, experimental design, quality control, and scientific inference
Experience independently carrying out multi-step computational research workflows from raw or messy data through final interpretation
Clear scientific writing and the ability to document methods, assumptions, and results precisely
Familiarity with reproducible research practices, including notebooks, scripts, version control, or workflow tools, is a plus
Project-based engagement with competitive, expertise-based pay
Open to qualified experts globally
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