Data Scientist Fellow — Multi-Omics Analysis

(ID: 2026-3889)

Axleis a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to research centers and healthcare organizations nationally and abroad. With experts in biomedical science, software engineering, and program management, we focus on developing and applying research tools and techniques to empower decision-making and accelerate research discoveries. We work with some of the top research organizations and facilities in the country including multiple institutes at the National Institutes of Health (NIH).

Benefits We Offer:

• 100% 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)
  • Parking Reimbursement Account (PRK)
  • Dependent Care Assistant Program (DCAP)
  • Transportation Reimbursement Account (TRN)

Position Overview

The Bioinformatics Fellow for Multi-Omics Analysis will conduct comprehensive analyses of multi-omics data generated from organoid systems and corresponding normal tissues. The incumbent will characterize organoid fidelity, identify biomarkers of successful differentiation, and develop computational frameworks for organoid quality assessment, while gaining substantive training in integrative analysis of single-cell and multi-omics data. The work is central to the SOM Center’s research objectives, and the position is intended to prepare the Fellow for an independent research career in academia, government, or industry.

Key Responsibilities

• Multi-omics analysis. Analyze complex datasets including single-cell RNA sequencing, bulk RNA sequencing, proteomics, and metabolomics data from a range of organoid systems and their tissue counterparts.

• Pipeline development. Develop and implement computational pipelines for data processing, quality control, and statistical analysis, and document them so that analyses are reproducible across the Center.

• Benchmarking. Integrate SOM-generated data with publicly available datasets in order to benchmark organoid characteristics against normal tissue profiles.

• Method development. Develop and refine analytical approaches — including machine learning methods where appropriate — for classifying cellular states and assessing organoid quality, and contribute to standardized assessment protocols.

• Collaboration and interpretation. Work closely with experimental teams to interpret results and guide protocol optimization, and collaborate with other computational staff on cross-modality data integration.

• Dissemination. Contribute to manuscript preparation and present findings at scientific conferences.

Required Qualifications

• Education. Doctoral degree (PhD) in bioinformatics, computational biology, biostatistics, or a related quantitative field, completed or anticipated prior to the start of the appointment.

• Single-cell analysis. Extensive experience with single-cell data analysis, including familiarity with tools such as Seurat, Scanpy, or comparable platforms.

• Programming. Strong programming proficiency in R and Python, together with experience in statistical analysis and data visualization.

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