Associate Scientist, Single-Cell & Multi-Omic Genomics

Icahn School of Medicine at Mount Sinai; New York (NY); On-site; USD 59,000 - 99,000; Full time

Job summary

Mount Sinai Health System seeks an Associate Scientist to lead computational analysis of large-scale molecular datasets focusing on single-cell and multi-omic technologies. Collaborate with an integrated team to translate complex genomic data into biological insights, leveraging deep expertise in single-cell transcriptomics and epigenomics.

The role emphasizes machine learning approaches, cloud-based workflows, and reproducible pipelines, with engagement in publications and grant activities.

Qualifications

• Ph.D. in Biological Science or related field; PhD in Computational Biology, Bioinformatics, Genomics, Statistics, Computer Science, or related field highly preferred

Responsibilities

• 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
• Performs other related duties.

Skills

Single-cell RNA-seq

Epigenomics

R

Python

Machine learning

Large-scale data analysis

Cloud computing

Education

Ph.D. in Biological Science or related field

Tools

R

Python

Cloud platforms

Machine learning frameworks

Job description

Mount Sinai Health System seeks an Associate Scientist to lead computational analysis of large-scale molecular datasets focusing on single-cell and multi-omic technologies. Collaborate with an integrated team to translate complex genomic data into biological insights, leveraging deep expertise in single-cell transcriptomics and epigenomics.

The role emphasizes machine learning approaches, cloud-based workflows, and reproducible pipelines, with engagement in publications and grant activities.