Bioinformatics Scientist
$110,000–$120,000 year
On-siteMinneapolis, Minnesota, United States
Minneapolis, Minnesota, United StatesOn-siteFull Time$110,000–$120,000 yearSenior LevelDoctorate Or Professional DegreeSmall
Full TimeSenior LevelDoctorate Or Professional DegreeSmall
Job Summary
Design and execute -Omics experiments including bulk, single-cell, spatial transcriptomics, WES, epigenomics, and proteomics, managing sample-to-data workflows. Execute analysis pipelines using R and Python for reproducible, modular exploration of large-scale multi-omics datasets, ensuring data integrity through structured documentation and version control. Process and organize large public datasets using cloud-based computing environments and structured queries. Maintain attention to documentation standards and reproducibility throughout the analytical lifecycle.
Required Qualifications
- A Ph.D or equivalent experience in biological or biochemical sciences
- Strong skills in one or more Omics approaches (transcriptomics, genomics, proteomics)
- Experience in Data Science, Bioinformatics or related computational disciplines
- Experience in working with large omics datasets using R/Python
- Strong understanding of cell biology especially with respect to basic biochemistry, cell signaling, cell replication, transcription, translation processes
- Strong background in tissue-based animal model assays
- Strong background in cell-based assays
- Strong background in cell pathway signaling
- Strong background in biochemistry especially as related to biologically dysfunctional systems
- Demonstrable hands-on experience with experimental sample-to-data workflows for one or more platforms
- Experience executing analysis pipelines using R and/or Python
- Attention to implementing bioinformatics practices for reproducible and modular analysis (markdown documentation, r/v env locks, git)
- Proficiency in data science skills related to exploratory data analysis
- Proficiency in data science skills related to wrangling (knowledge of coding and processes related to key steps - discovery, structuring/transformation, cleaning, enriching/reduction, validating, and publishing)
- Proficiency in data science skills related to visualization using R and/or Python
- Experience with processing and organizing large datasets from public repositories using structured queries
- Experience wrangling data to be compatible with analytical pipelines in a cloud-based computing environment
- Attention to documentation and standards for reproducibility of code
- Attention to data integrity
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