Bioinformatician II- Tisch Cancer Institute BiNGS Core

The Bioinformatics for Next Generation Sequencing (BiNGS) Shared Resource at the Tisch Cancer Center, Icahn School of Medicine at Mount Sinai is seeking an experienced and highly motivated Bioinformatician II to lead transcriptomics, epigenomics, and multiomics data analysis projects focused on cancer biology. The mission of BiNGS is to accelerate biomedical discovery by providing investigators with state-of-the-art next-generation sequencing (NGS) analysis, computational tools, training, and bioinformatics expertise.

BiNGS supports a broad spectrum of genomic applications, including bulk RNA-seq, ATAC-seq, ChIP-seq, CUT&RUN, Hi-C, single-cell RNA-seq, single-cell ATAC-seq, single-cell Multiome, Spatial Transcriptomics, DNA methylation, whole-genome sequencing (WGS), whole-exome sequencing (WES), and emerging long-read sequencing technologies. In addition to data analysis, BiNGS develops bioinformatics tools, manages computational infrastructure, provides access to high-performance computing (HPC) resources, and delivers advanced computational training to the Mount Sinai research community.

As a senior member of the BiNGS team, you will work closely with investigators across the Tisch Cancer Center and the broader Mount Sinai community to design, execute, interpret, and communicate complex genomic analyses. You will lead collaborative projects from inception through publication, mentor junior bioinformaticians, and contribute to the continued growth of the core and its services. Success in this role requires scientific curiosity, attention to detail, excellent communication skills, and the ability to work both independently and collaboratively.

The successful candidate will be expected to lead multidisciplinary projects, mentor junior scientists, and communicate complex analyses clearly to investigators with diverse scientific backgrounds. BiNGS offers a unique opportunity for senior bioinformaticians who wish to expand their expertise in transcriptomics, and epigenomics, while contributing to impactful cancer research. The position also provides opportunities to develop leadership skills, collaborate on high-impact publications and grant applications, and contribute to an inclusive scientific environment through mentorship of trainees, including those from historically underrepresented backgrounds.

• BiNGS? BiNGS offers an exceptional opportunity to: Work on cutting-edge cancer genomics and multiomics projects using the latest sequencing technologies. Collaborate with internationally recognized investigators across the Tisch Cancer Center and the broader Mount Sinai research community. Lead scientifically impactful projects from study design through publication. Contribute to high-impact manuscripts, grant applications, and the development of innovative computational methodologies. Access world-class computational resources, including Mount Sinai's HPC infrastructure and cloud computing platforms. Mentor junior scientists and help shape the future direction of a rapidly growing bioinformatics core. Expand expertise in transcriptomics, epigenomics, chromatin biology, spatial genomics, and emerging sequencing technologies. Foster an inclusive scientific environment through mentorship and support of trainees from historically underrepresented backgrounds. Responsibilities Provide scientific and analytical leadership for complex and high-impact transcriptomics and epigenetics BiNGS projects (e.g. bulk and single-cell RNA-seq and ATAC-seq, single-cell Multiome, CUT&RUN, and Micro
• C datasets), translating investigator questions into rigorous computational strategies and leading analyses using the Mount Sinai HPC environment, interpretation, visualization, and manuscript contributions through publication and peer review. Manage multiple collaborative projects simultaneously, including study design, project management, data analysis, data interpretation and ‘story’ development, and presentation of results to investigators. Integrate internally generated datasets with publicly available resources (e.g., ENCODE, TCGA, and GEO) to identify biologically meaningful patterns and generate new hypotheses. Develop publication-quality figures, visualizations, and analytical reports for manuscripts, grant applications, and scientific presentations. Evaluate, implement, and benchmark emerging computational methods for multiomic data integration, visualization, and analysis (e.g., MOFA, Similarity Network Fusion, and related approaches). Perform large-scale analyses using the Mount Sinai HPC environment and oversee data management, storage, archiving, and workflow execution. Maintain and support cloud-based computational resources, preferably using Amazon Web Services (AWS), including deployment of interactive reports and web-based applications. Qualifications M.S. in Bioinformatics, Biomedical Informatics, Computational Biology, or Genomics. Alternately, M.S. in a discipline requiring strong computational and analytical skills supplemented with some biology exposure. Ph.D in a related field preferred. Those with a Bachelors degree and additional post-graduate experience are considered. 2+ years post-graduate experience in a research environment, including the manipulation of large biological datasets. Advanced knowledge of genetics and/or statistical analysis software and online resources. Experience in programming environments such as MatLab, R statistical package, BioConductor, Perl and C++. Preferred Skills Master's degree or PhD in Bioinformatics, Computational Biology, Computer Science, or a related quantitative discipline. Proven experience analyzing bulk and single cell epigenetics and transcriptomics datasets (e.g. ATAC-seq, CUT&RUN, ChIP-seq, single-cell ATAC-seq, single-cell RNA-seq and Micro
• C). For candidates with master’s degree, at least 2
• 3 years of experience. Strong programming skills in Python, R, Linux, and Bash. Experience using standard genomics software, including Bowtie2, STAR, Cell Ranger, Samtools, MACS2, Seurat, Signac, Cicero, ChromVAR, SCENIC+, and the UCSC Genome Browser. Experience working in Linux-based high-performance computing environments with parallel file systems. Experience managing analyses and data using Amazon Web Services (AWS) or comparable cloud computing platforms. Experience using modern AI-assisted software development tools (e.g., Claude Code, GitHub Copilot, or similar) to accelerate software development, debugging, and workflow optimization. Strong understanding of chromatin biology, transcriptional regulation, and next-generation sequencing technologies. Excellent analytical, organizational, communication, and problem-solving skills. Demonstrated ability to work independently while collaborating effectively within multidisciplinary research teams.

Full time · Day shift