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# Associate Scientist - Computational Genomics & Large-Scale Molecular Data Analysis (Dr Sealfon's Lab) - Neurology

- **Company:** Mount Sinai Health System
- **Location:** United States
- **Experience:** Experienced
- **Salary:** $58,661.0 - $98,979.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Big Data, Bioinformatics, Computational Biology, Computer Programming, Data Visualization, Machine Learning, Information Technology
- **Published:** August 15, 2026
- **Apply:** https://ejis.fa.us6.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX/requisitions/preview/3041367

## About the Role

We are seeking a highly motivated Associate Scientist to lead and support the analysis of large-scale molecular datasets in a dynamic, collaborative research environment. This role focuses on cutting-edge functional genomics, with an emphasis on single-cell and multi-omic technologies.

The Associate Scientist will drive computational analysis of high-dimensional datasets, partnering closely with a well-integrated computational-experimental team to generate biological insights from complex genomic data. The ideal candidate has deep expertise in single-cell transcriptomics and epigenomics, experience handling large-scale datasets, and strong quantitative and programming skills. Experience in machine learning and AI approaches is highly desirable.

 * Ph.D. in Biological Science or related field; PhD in Computational Biology, Bioinformatics, Genomics, Statistics, Computer Science, or related field highly preferred
 * Three years' experience
 * Strong experience analyzing bulk and single-cell RNA-seq and epigenomic data
 * Proficiency in R, including common single-cell analysis frameworks
 * Experience working with large-scale genomic datasets and high-performance computing environments
 * Strong statistical background and data visualization skills
 * Experience analyzing DNA methylation data (e.g., array-based or sequencing-based approaches)
 * Experience analyzing long-read RNA sequencing datasets
 * Demonstrated use of machine learning/AI methods for genomic data integration or prediction
 * Familiarity with cloud-based workflows and reproducible pipeline development
 * Track record of publications in peer-reviewed journals



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