Single Cell Sequencing Data Jobs

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Bioinformatics/Data Scientist

Axle Frederick, MD

Frederick, MD

Today

$105k - $120k/yr Full time Python Bioinformatics Data Analysis

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Job Description

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.

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