Bioinformatician II- Tisch Cancer Center BiNGS Shared Resource

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.

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

• Utilizing ULP WGS and bulk transcriptomics to study Anal cancer progression

• 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.

The successful candidate will:

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  Provide scientific and analytical leadership for complex and high-impact genomics and transcriptomics projects (e.g. WES, ULP WGS, bulk and single-cell RNA-seq 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.

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  Manage multiple collaborative projects simultaneously, including study design, project management, data analysis, data interpretation and ‘story’ development, and presentation of results to investigators.

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  Integrate internally generated datasets with publicly available resources (e.g., COSMIC, TCGA, and GEO) to identify biologically meaningful patterns and generate new hypotheses.

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  Develop publication-quality figures, visualizations, and analytical reports for manuscripts, grant applications, and scientific presentations.

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  Evaluate, implement, and benchmark emerging computational methods for multiomic data integration, visualization, and analysis (e.g., MOFA, Similarity Network Fusion, and related approaches).

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  Perform large-scale analyses using the Mount Sinai HPC environment and oversee data management, storage, archiving, and workflow execution.

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  Maintain and support cloud-based computational resources, preferably using Amazon Web Services (AWS), including deployment of interactive reports and web-based applications.