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Single-cell Analytics Innovation Lab (SAIL) at the Sloan Kettering Institute is looking for creative individuals with passion for developing and implementing machine learning methods to extract insights from biological data. The SAIL team strides for biological discovery by applying state-of-the-art statistical and AI algorithms on data sets from novel spatial transcriptomics and single-cell genomics technologies across variety of biological samples collected from patients, advanced mouse and organoid models of cancer, and developmental systems. These analyses are used to explore diverse questions, including the subjects of tumor heterogeneity, drug resistance, tumor stem cells, mechanism of immunotherapy, the emergence of metastasis and the tumor-immune environment. SAIL computational biology is embedded in the group of Dr. Dana Pe’er, a leader in single-cell data analysis and methods development, providing guidance and a highly stimulating intellectual environment for analytical methods development.

This role involves adapting, developing, and implementing machine learning methods on cutting-edge data being generated at SAIL to extract biological insights. You should have a background in statistics, machine learning or computer science. Prior research experience in developing deep learning methods is preferred but not required; any academic or research exposure to genomics is a plus, and you should be excited to learn how to develop and apply sophisticated algorithms to big biological data at Memorial Sloan Kettering Cancer Center (MSK). Your implementation of novel algorithms will allow data scientists, biologists, and clinicians to interact with and interpret the data leading to important implications in cancer biology. A key focus is the single-cell profiling of patient samples, with the goal of improving immunotherapy and precision medicine. Join us if you are eager to learn and want to make an impact!

You will:

• Develop, implement, and evaluate machine learning methods to process, normalize, organize and visualize high-throughput single-cell sequencing and spatial transcriptomics data
• Provide initial analysis and biological interpretation of the data
• Enhance current efforts at SAIL to develop computational and AI infrastructure to enable single-cell scientists at MSK query and interact with their data; help to improve software pipelines at SAIL
• Benchmark data from cutting-edge technologies and closely collaborate with research scientists, providing consultation, guidance and training on SAIL computational tools

You have: 

• Minimum of bachelor’s with core biology and computer science courses, or equivalent experience; prior experience in developing machine learning methods is preferred
• Analytical reasoning, statistical, mathematical, and problem-solving skills
• Excellent ability to communicate with biologists; familiarity with high-throughput sequencing data is a plus
• Knowledge of Python or R (preferably Python)

Interested applicants may send their resume to Roshan Sharma (sharmar1 at mskcc dot org).