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# Senior Data Scientist - Genetics

- **Company:** University of Pennsylvania
- **Location:** Philadelphia, PA, United States
- **Experience:** Expert
- **Salary:** $100,000.0 - $160,000.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Cloud Computing, Perl (Programming Language), Python (Programming Language), Software Systems, Information Technology
- **Published:** July 24, 2026
- **Apply:** https://dejobs.org/x/x/C09287B6A763478982C32871FD4E36C8/job/

## About the Role

This requires a strong background in biology to help ensure the right questions are asked, but linked to strong technical and personal communication skills, to help ensure insights are broadly adopted and appropriate analyses are performed. You may work with molecular (WGS, panel-sequencing, RNA-Seq, proteomic) or imaging (digital pathology, radiomic) data, and practical experience in one of these two areas is essential. Beyond your strong inter-personal skills and computer science background, you will have experience with implementation skills at least one of R, Perl or Python. You will be comfortable in UNIX/Linux environments and producing well-documented code. Multi-year experience (>10years) with cloud-computing or HPC is essential.

Master of Science and 1 to 2 years of experience, or an equivalent combination of education and experience required. Ph.D. preferred.

## Description

The successful candidate will be working with a broad team of quantitative analysts, including Statisticians, Data Scientists, Clinical and Basic trainees, and other bioinformaticians. Their role will involve developing and implementing new quantitative strategies to improve our understanding and ability to diagnose genetic disorders through a large-scale genomic clinically grade testing. Senior Data Scientists are passionate about applying their knowledge of biology and of methods for molecular, cellular or radiologic data-analysis to uncover general principles of genetic disorders and use these to create clinically useful biomarkers. They will develop, extend and apply computational pipelines for data-analysis to patient cohorts ranging from tens of individuals to hundreds of thousands. These will be executed at scale via distributed software solutions on both local HPC and cloud-based assets. Our datasets comprise petabytes, and are growing rapidly, linked to key clinical endpoints.


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