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.

Research Environment

The successful candidate will contribute to diverse, multidisciplinary projects, for example:

• Investigating how oncogenic RAS mutations drive leukemia using single-cell RNA-seq and ATAC-seq.
• Defining the role of histone variants in cancer through enhancer analysis, chromatin accessibility profiling, transcriptomics, and transcription factor network analysis.
• Integrating bulk and single-cell epigenetics and transcriptomics datasets to understand the role of mutant p53 in cancer.
• Applying FIBER-seq (PacBio long-read sequencing) to investigate how chromatin remodeling complexes shape chromatin architecture.
• These projects provide opportunities to work with cutting-edge sequencing technologies while collaborating with leading cancer biologists and computational scientists.

Key Responsibilities

The successful candidate will

• Lead computational analyses of bulk and single-cell RNA-seq, bulk and single-cell ATAC-seq, single-cell Multiome, CUT&RUN, and Micro-C datasets.
• Manage multiple collaborative projects simultaneously, including study design discussions, project management, 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.
• Train investigators, trainees, and laboratory members in NGS data analysis workflows and best practices.
• Mentor junior bioinformaticians and contribute to the continued development of BiNGS services, computational infrastructure, and analytical workflows.
• 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 datasets (e.g. ATAC-seq, CUT&RUN, ChIP-seq, single-cell ATAC-seq, and Micro-C). For candidates with master’s degree, at least 3–4 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 developing reproducible computational pipelines and interactive data visualization tools.
• Experience using modern AI-assisted software development tools (e.g., Claude Code, GitHub Copilot, or similar) to accelerate software development, debugging, and workflow optimization.
• Familiarity with AI and machine learning approaches for genomic data analysis.
• 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.
• Previous experience mentoring or supervising students, trainees, or junior bioinformaticians.
• Experience teaching workshops or courses in bulk RNA-seq, single-cell genomics, or next-generation sequencing data analysis.