Computational Biologist I, Clinical Data Mining (CDM)

Memorial Sloan Kettering Cancer Center•New York, NY

10d•Hybrid

About The Position

The people of Memorial Sloan Kettering Cancer Center (MSK) are united by a singular mission: ending cancer for life. Our specialized care teams provide personalized, compassionate, expert care to patients of all ages. Informed by basic research done at our Sloan Kettering Institute, scientists across MSK collaborate to conduct innovative translational and clinical research that is driving a revolution in our understanding of cancer as a disease and improving the ability to prevent, diagnose, and treat it. MSK is dedicated to training the next generation of scientists and clinicians, who go on to pursue our mission at MSK and around the globe. Exciting Opportunity at MSK: At Memorial Sloan Kettering (MSK), we're not only changing the way cancer is treated, we're changing the way the world understands it. To help make cancer data more accessible, actionable, and impactful at MSK and beyond, we are seeking a Computational Biologist to join the Clinical Data Mining (CDM) team, one of several teams supporting the Cancer Data Science Initiative (CDSI). In this role, you will collaborate with researchers, oncologists, data scientists, and engineers across MSK to develop machine learning (ML) and natural language processing (NLP) solutions that automate the extraction, standardization, and curation of clinically relevant data from unstructured sources. The mission of CDSI is to accelerate translational research by transforming real-world clinical data into high-quality, research-ready resources. Through the abstraction and integration of patient data from diverse clinical systems, CDSI enables clinicians, biologists, and computational scientists across the institution to access de-identified data that fuels discovery and innovation. Teams supporting CDSI have also developed widely adopted cancer research resources, including cBioPortal and OncoKB, which are used by thousands of researchers and clinicians worldwide. As a Computational Biologist, your work will directly support institutional research projects, including enterprise curation, health disparities research supported by the Geoffrey Canada Center for Translational Cancer Disparities Research, and Cancer AI Alliance (CAIA) use cases. Through these efforts, you will help generate and curate data resources that power scientific discovery, advance cancer research, and ultimately improve patient outcomes.

Requirements

Bachelor's degree in Computer Science, Bioinformatics, Biomedical Engineering, Computational Biology, Data Science, or a related discipline with 2+ years of relevant experience; or a Master's degree in a related field.
Experience programming in Python and/or R.
Working knowledge of SQL and relational databases.
Experience applying machine learning and/or NLP methodologies.
Experience working with source control systems such as Git/GitHub.
Experience using cloud platforms or cloud-based data environments.
Strong communication and collaboration skills with both technical and non-technical stakeholders.

Nice To Haves

M.S. or Ph.D. in Computer Science, Bioinformatics, Biomedical Engineering, Computational Biology, or a related field.
Experience building and deploying machine learning models in research or production environments.
Knowledge of NLP methodologies and AI-driven applications.
Experience with large language models (LLMs), prompt engineering, and AI APIs.
Familiarity with healthcare, clinical research, bioinformatics, cancer genomics, or clinical informatics.
Experience with cloud technologies such as AWS, Azure, Google Cloud Platform, or Databricks.
Experience working with electronic health records (EHRs), clinical data warehouses, or healthcare datasets.

Responsibilities

Partner with clinicians, researchers, engineers, and data scientists to support institutional cancer research initiatives.
Develop and maintain data pipelines to extract, transform, and integrate clinical, genomic, imaging, and other healthcare datasets.
Build and deploy machine learning (ML), natural language processing (NLP), and AI-based solutions for clinical data extraction and curation.
Leverage large language models (LLMs) and emerging AI technologies to improve data abstraction and research workflows.
Collaborate with cross-functional teams to translate research questions into scalable computational solutions.
Support enterprise curation efforts that make high-quality clinical data available to investigators across MSK.
Contribute to the development of cloud-based infrastructure and analytics tools supporting translational research.