Bioinformatics/Data Scientist

Axle in Frederick, MD (onsite) is seeking a Bioinformatics/Data Scientist to analyze multi-omics data from organoid systems and normal tissues, supporting the NIH SOM Center with a salary range of USD 105,000 - 120,000 per year.

Responsibilities

• Analyze complex datasets including single-cell RNA sequencing, bulk RNA sequencing, proteomics, and metabolomics from organoid systems and their tissue counterparts.
• Develop and implement computational pipelines for data processing, quality control, and statistical analysis.
• Integrate SOM generated data with publicly available datasets to benchmark organoid characteristics against normal tissue profiles.
• Collaborate with experimental teams to interpret results, guide protocol optimization, and contribute to manuscript preparation and presenting findings at scientific conferences.

Requirements

• PhD in bioinformatics, computational biology, biostatistics, or a related quantitative field.
• Extensive experience with single-cell data analysis, including familiarity with Seurat, Scanpy, or similar platforms.
• Strong programming skills in R and Python, with experience in statistical analysis and data visualization.
• Knowledge of proteomics and metabolomics data analysis workflows.

Technologies

• Seurat
• Scanpy
• R
• Python

Benefits

• Medical, Dental & Vision Coverage for Employees
• Paid Time Off and Paid Holidays
• 401K match up to 5%
• Educational Benefits for Career Growth
• Employee Referral Bonus
• Flexible Spending Accounts: Healthcare (FSA)
• Flexible Spending Accounts: Parking Reimbursement Account (PRK)
• Flexible Spending Accounts: Dependent Care Assistant Program (DCAP)
• Flexible Spending Accounts: Transportation Reimbursement Account (TRN)

Cover Letter Required

Please answer the following questions and submit application at: BIOINFORMATICS/DATA SCIENTIST:

• What experience do you have working with large datasets and compute clusters?
• Describe your experience building workflows/pipelines and/or using bioinformatics software and tools to analyze data.
• What programming languages are you comfortable using for bioinformatics data analysis?
• What bioinformatics areas are you familiar with? (e.g., single cell, bulk genomics, transcriptomics, epigenetics, flow, spatial, metagenomics, etc.)
• What life science disciplines do you have experience in? (e.g., immunology, infectious disease, cancer research, etc.)
• Provide examples of basic machine learning/AI concepts and/or how you've applied them in bioinformatics data analysis.