Post Doctoral Research Fellow/Computational Scientist, Biostatistics & Spatial Transcriptomics -Biostatistics & Surgery Memorial Sloan Kettering Cancer Center

Memorial Sloan Kettering Cancer Center (MSK) unites specialists around a singular mission: ending cancer for life. Our care teams deliver personalized, expert care to patients of all ages, informed by basic research from the Sloan Kettering Institute. Scientists across MSK collaborate on innovative translational and clinical research, advancing cancer prevention, diagnosis, and treatment. MSK also trains the next generation of scientists and clinicians.

About the role

The Adusumilli Lab, in partnership with the Department of Epidemiology and Biostatistics, seeks a PhD-level Computational Scientist to advance translational cancer research. This joint role bridges rigorous statistical methodology, advanced single-cell/spatial omics, and clinical translation in surgical oncology. You will develop and apply innovative statistical frameworks to map the tumor immune microenvironment (TIME), decode multi-cellular architectural niches, and collaborate with a multidisciplinary team of computational biologists, statisticians, and surgeons.

Requirements

• PhD in Biostatistics, Statistics, Computational Biology, Bioinformatics, or a closely related quantitative field with strong core statistical training.
• Proven track record in analyzing and modeling single-cell and high-plex spatial profiling data.
• Direct experience processing data from multiplexed imaging or sequencing-based spatial platforms (e.g., Imaging Mass Cytometry (IMC), 10x Genomics Xenium, NanoString CosMx, and/or 10x Genomics Visium).
• Advanced proficiency in Python and/or R programming and expert-level familiarity with single-cell and spatial ecosystems, specifically Seurat, Giotto, and relevant Bioconductor packages.
• Strong foundation in spatial statistics, including spatial autocorrelation, cell deconvolution algorithms, and distance-based neighborhood modeling.
• Demonstrated experience working with paired single-cell RNA-seq and V(D)J/TCR sequencing datasets.
• Experience analyzing translational data involving tumor-infiltrating lymphocytes (TILs), immunophenotyping markers, and clinical-pathological correlation.
• Ability to bridge complex mathematical/algorithmic theory and practical clinical interpretation.
• Outstanding communication skills and a proven ability to collaborate across multidisciplinary teams of clinicians, statisticians, and computational biologists.

Responsibilities

• Develop, scale, and implement novel statistical methods and machine learning frameworks for high-dimensional spatial omics and single-cell landscapes, including pipelines for multiplexed spatial imaging data and paired single-cell multi-omics.
• Implement advanced spatial point pattern analysis, cell-cell interaction modeling, and neighborhood/niche identification to map cell-to-cell spatial proximity and architectural differences within the tumor microenvironment across clinical cohorts.
• Design analytical strategies to model the immune microenvironment (e.g., PD-L1 expression patterns, tumor-infiltrating lymphocyte densities) and statistically associate spatial phenotypes with clinical outcomes.
• Provide expert statistical guidance on study design, sample size estimation, and power calculations for translational protocols and grant proposals (NIH/NCI).

Benefits

MSK offers a competitive salary and benefits package, fostering a highly collaborative and innovative environment.

Pay

$72,000 – $87,564 (based on PGY*).