Top Biotech Data Science Jobs in New York City, NY

The Data Scientist II will lead RWE studies, derive insights from real-world clinical data, and contribute to methodologies using advanced statistical techniques and AI tools, collaborating closely with pharmaceutical partners.

As an intern, you will lead research projects in cheminformatics and machine learning, developing new tools for drug discovery programs and working closely with experienced scientists.

Lead data analysis for drug discovery in neuroscience, integrating diverse datasets and collaborating with cross-functional teams to develop therapeutic candidates and validate targets.

The Senior Director of Data Science Initiatives and Partnerships will lead high-impact partnerships, oversee data science initiatives, and drive strategic vision at JAX, bridging research and external collaboration.

The Principal Bioinformatics Data Scientist will develop advanced computational methods and ML models for genomic analysis and lead innovation in bioinformatics, driving integration of large-scale genomic data.

The Data Management Intern will assist in developing an AI chatbot interface for data querying, optimize SQL queries, and support data transformations and integrations with AWS services while gaining hands-on experience in data management and analytics.

The Principal Data & Analytics Architect oversees the design and enhancement of data management and analytics, driving business outcomes through data strategies and creating data products.

The role involves building bioinformatics and ML pipelines, analyzing multi-omics data, mentoring, and collaborating across teams to develop actionable insights and scientific breakthroughs.

Partner with Membership to build data pipelines, ensure membership data quality, and develop predictive models (churn, retention, LTV). Design and analyze experiments, create dashboards, and collaborate with Marketing, Finance, and Product to optimize pricing, offers, and lifecycle programs.

Analyze biological datasets using machine learning and deep learning, collaborate on research projects, and communicate findings through visualizations and reports.