Top Biotech Data Science Jobs

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.

As a Senior Quantitative Data Scientist, you will ensure data quality, apply analytical methods for evidence generation, and collaborate with cross-functional teams to meet data strategy and analytic priorities.

The Data Scientist supports AI-driven initiatives, conducts analytics, builds machine learning algorithms, and develops technical reports. They work with large datasets to drive business improvements and enhance patient lifetime value.

The Principal Scientist will apply expertise in human genetics and statistical genetics to support new medicine development, perform complex analyses, and collaborate with multidisciplinary teams to address scientific questions.

Lead design and deployment of graph ML and network-biology analyses to generate drug target hypotheses from RWD and multi-omic data. Prototype methods, integrate solutions with engineers, prioritize tasks, and communicate results to cross-functional stakeholders while championing data science best practices.

The Senior Scientist in Bioinformatics will analyze clinical biomarker data, perform statistical analysis, and collaborate with teams to support oncology trials.

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.

The Data Manager – AI Development coordinates data planning, collection, annotation, and governance for AI medical devices, ensuring data readiness and compliance.