Collaborate with cross-functional teams to curate key experimental and omics datasets with an emphasis on quality and correctness to ensure that our complex scientific data are trustworthy
Perform exploratory data analyses on key experimental and omics datasets
Evaluate and implement automation tools and AI/ML approaches to enhance data curation and EDA workflows that increase the speed and accuracy of data handling
Collaborate with cross-functional teams to develop and adopt best practices for data engineering
Requirements
Ideally an advanced degree (PhD, MS, or BS) in Computational Biology, Bioinformatics, Data Science, Computer Science, or related field with relevant experience
A minimum of 5-8 years in either academia or industry working in an equivalent position in computational biology, bioinformatics, data engineering, or related field
At least 4-5 years of experience working with molecular biology or omics data
Demonstrated statistical and analytic rigor while performing exploratory data analyses and drawing scientific conclusions from experimental data (e.g., scRNAseq, RNAseq, ChIPseq, DNAseq, proteomics, compound screens, or CRISPR screens)
Fluent in one or more programming languages with bioinformatics applications (R, Python)
Knowledge of version control, reproducible workflows, Unix / Linux
Curiosity, creativity, strong organizational skills, solution-oriented problem solving
Ability to work independently, prioritize tasks, determine project next steps, manage multiple projects and stakeholders simultaneously
Excellent written and verbal communication skills, including the ability to explain complex concepts to diverse audiences.
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