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

- **Company:** Mount Sinai Health System
- **Location:** United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Big Data, Bioinformatics, Computational Biology, Computer Programming, Data Visualization, Machine Learning, Systems Development Life Cycle, Information Technology, Data Pipelines
- **Published:** May 23, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=0307a3bcaf405d6a

## About the Role

Do you have experience in System development?, We are seeking a highly motivated Staff 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., * Ph.D. in Biological Science or related field

 * Three years experience

Preferred Skills

 * PhD in Computational Biology, Bioinformatics, Genomics, Statistics, Computer Science, or related field (or equivalent 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

Strength through Unity and Inclusion

## Description

The Staff 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.

 * Lead analysis of single-cell RNA-seq and multiome datasets (joint RNA/ATAC profiling)
 * Perform integrative analysis across modalities, including bulk RNA-seq, ATAC-seq, and DNA methylation datasets
 * Develop processing pipelines for novel single cell multiomic technologies
 * Apply statistical modeling and machine learning methods to identify cellular states, regulatory programs, and epigenetic signatures
 * Design and implement integrative multi-omic analyses across cohorts and experimental systems
 * Present findings internally and contribute to publications and grant applications
 * Stay current with emerging single-cell and AI-driven genomic analysis methodologies


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