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Mercor is seeking a bioinformatics researcher to perform complex data-analysis tasks on sequencing and omics data. You will craft questions with exact answers, select appropriate public or private datasets, and use notebooks to reproduce analyses.
You will work with Python or R to analyze data, document workflows, and interpret results, communicating uncertainties and conclusions clearly. This role emphasizes rigorous scientific writing and reproducibility.
Complete realistic, multi-step scientific data-analysis tasks in bioinformatics
Write original bioinformatics questions where each answer is exact and objective
Choose and anonymize data (e.g. from public archives such as GEO, SRA/ENA, ArrayExpress, PRIDE, PDB, MGnify or from your own unpublished lab data) for the model to analyze
Use scientific software, code, and command-line tools to generate reproducible analyses and structured final outputs
Develop a Jupyter validation notebook, analyzing the data in Python or R, which reproduces the correct answer to your question
Interpret results in the context of the underlying biological or translational question, clearly communicating assumptions, uncertainty, and conclusions
Masters or PhD (or equivalent research experience) in bioinformatics, computational biology, genomics, statistical genetics, quantitative biology or a closely related field
Hands-on experience analyzing sequencing and omics data (bulk or single-cell RNA-seq, WGS/WES, variant calling, metagenomics, proteomics or similar), from raw reads to interpretation
Experience with one or more of the following tools: samtools, bcftools, bedtools, BWA, Bowtie2, BLAST, plus Python (pandas, Biopython, pysam) or R/Bioconductor (DESeq2, edgeR, limma, GenomicRanges)
Able to write and run Jupyter notebooks that reproduce your work exactly
Clear, precise scientific writing
Access to unpublished or private datasets you have the right to share is a strong plus
Deep knowledge of at least one niche area, such as a non-model organism, an unusual assay, or a specialized area of genomic interpretation is a strong plus