Data Scientist I

Memorial Sloan Kettering Cancer Center•New York, NY

5d•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 Opportunities at MSK: Data Scientist I Memorial Sloan Kettering Cancer Center (MSK) is seeking a Data Scientist I to join the Strategy & Growth team. This role offers a unique opportunity to apply cutting-edge AI technologies to high-impact business challenges, helping shape how one of the world's leading cancer centers leverages artificial intelligence to drive innovation, efficiency, and growth. Working closely with strategy, commercialization, and innovation leaders, you will build AI-powered solutions that automate complex workflows, enhance decision-making, and create measurable organizational impact. This role is ideal for an early-career data scientist who is passionate about Generative AI, intelligent agents, and building technology that solves real-world problems.

Requirements

1-3 years of experience in data science, machine learning, or applied AI.
Hands-on experience working with large language models (Claude, GPT-4 , open-source LLMs), including prompt engineering, retrieval-augmented generation (RAG), and/or API-based development.
Understanding of machine learning fundamentals and data analysis techniques.
Proficiency in Python and/or R for data science and ML workflows.
Comfort working in relational databases and SQL.
Familiarity with coding assistant tools (e.g., Claude Code, Github Copilot), modern software development practices, and version control (Git).
Exposure to or interest in agentic AI systems (multi-step agents, tool use, orchestration frameworks such as AWS Strands and LangChain) - direct experience is a strong plus.
Curiosity and a strong desire to continuously learn new technologies and approaches.
Comfort operating in ambiguity and adapting to rapidly evolving priorities.
Passion for applying AI and automation to solve practical business challenges.
Ability to balance technical excellence with measurable business impact.
Collaborative communication skills and the ability to explain complex concepts to diverse stakeholders.

Nice To Haves

Familiarity with cloud platforms, particularly AWS (e.g., Bedrock) is a plus.
Experience building AI applications, AI agents, retrieval-augmented generation (RAG) solutions, or productivity-focused automation tools is strongly preferred.
Healthcare experience is a plus but not required

Responsibilities

Design, build, and deploy AI agents and generative AI applications – from early prototypes through production-ready tools – that automate operational workflows, extend the team’s strategic capabilities, and support commercialization and revenue-generation initiatives.
Evaluate the performance, accuracy, and real-world impact of any models or AI solutions built, using appropriate metrics, testing, and monitoring to ensure reliability and effectiveness once in production.
Partner with Strategy & Growth stakeholders to translate business challenges into scalable AI-enabled workflows.
Perform broader data analytics and data engineering tasks as needed to support the team and stakeholders, including building and maintaining ETL pipelines, developing dashboards and reporting tools, and conducting ad hoc data analyses.
Provide data science support for in-house digital products with potential commercial value.
Evaluate emerging AI technologies, tools, and frameworks, and recommend practical applications for the team.
Leverage modern AI development platforms, coding assistance tools, and software engineering best practices to accelerate innovation.