Genomics Bioinformatics Expert - Reproducible Data Analytics

Mercor; San Francisco (CA); On-site; USD 100,000 - 150,000; Full time

Job summary

Mercor is seeking a skilled bioinformatics researcher to perform realistic, multi-step data-analysis tasks in bioinformatics, from raw data to interpretation, using code, scientific software, and command-line tools.

You will develop a Jupyter validation notebook in Python or R, pose original questions with exact answers, and analyze public archives or private datasets, ensuring reproducible analyses and clear communication of assumptions and uncertainty.

Qualifications

• Masters or PhD (or equivalent research experience) in bioinformatics or related field.
• Hands-on experience analyzing sequencing and omics data from raw reads to interpretation.
• Experience with command-line tools such as samtools, bcftools, bedtools, BWA, Bowtie2, BLAST; Python or R with Biopython/pandas or DESeq2/edgeR.
• Ability to write and run Jupyter notebooks that reproduce work exactly.
• Clear, precise scientific writing; access to unpublished data is a plus.

Responsibilities

• Perform realistic, multi-step data-analysis tasks in bioinformatics.
• Write original bioinformatics questions whose answers are exact and objective.
• Choose and anonymize data from public archives or private datasets for analysis.
• Use scientific software, code, and command-line tools to generate reproducible analyses and outputs.
• Develop a Jupyter validation notebook in Python or R to reproduce the correct answer.
• Interpret results and clearly communicate assumptions, uncertainties, and conclusions.

Skills

Bioinformatics analysis

Sequencing data analysis

Python programming

R programming

Jupyter notebooks

Education

Master's degree in bioinformatics or related field

PhD in bioinformatics or related field

Tools

samtools

bcftools

bedtools

BWA

Bowtie2

BLAST

Job description

Mercor is seeking a skilled bioinformatics researcher to perform realistic, multi-step data-analysis tasks in bioinformatics, from raw data to interpretation, using code, scientific software, and command-line tools.

You will develop a Jupyter validation notebook in Python or R, pose original questions with exact answers, and analyze public archives or private datasets, ensuring reproducible analyses and clear communication of assumptions and uncertainty.