Bioinformatics Analyst III
6 Months
North Chicago, IL (Remote ok)
May be remote, onsite, or hybrid. Working hours are typically 9AM-5PM Central Time, with some flexibility depending upon applicant location.
What are the top 3-5 skills, experience or education required for this position:
- PhD in Bioinformatics, Genetics, Molecular and Cellular Biology, or related field or MS with at least 6 years of experience.
- Single-cell and/or spatial transcriptome QC and analysis (e.g., with Seurat or Scanpy)
- Bulk RNAseq data processing and analysis
- Facility with R and/or Python for data exploration and statistical analysis
- Experience with high-performance computing (HPC) and job submission systems
Targeted Experience:
- -Fresh PhD or Master with close to 6 years of experience - Bioinformatics, Human Genetics
- -Single Cell and Bulk RNAseq experience
- -Communication
- -Comfortable working in a Linux environment - structure and submit their own jobs
- -HPC - managing their data
- -R (preferred) or Python experience
- -Human disease/systems/biology background - preferred
Job description
- We are seeking a Bioinformatician to join our cross-functional team and provide advanced bioinformatics support for studies of age-related diseases. The successful candidate will use a variety of ‘omics data to test and refine biological hypotheses in coordination with wet-lab and computational colleagues. Key responsibilities include:
- Upstream processing of ‘omics data (QC, normalization, exploratory data analysis).
- Downstream processing of bulk, single-cell, and/or spatial transcriptomic data (e.g., cell type annotation, differential gene expression analysis, gene set enrichment analysis, results visualization).
- Additional responsibilities may include analysis of proteomics or other data modalities depending upon project needs. Applicants should be familiar with high-performance computing systems, shell scripting in a Linux environment, and have strong facility with R and/or Python, including relevant bioinformatics packages (e.g., Seurat, edgeR/limma). Those with experience integrating multi-modal data (e.g., scRNAseq + scATACseq, spatial RNAseq + spatial proteomics) are especially encouraged to apply. A background in human disease biology is helpful but not required.