Data Science Jobs in Cambridge, MA

Build and maintain full-stack internal applications: backend services/APIs, frontend UIs (React), and workflow automation. Own features end-to-end, drive architecture discussions, ensure testability, maintainability, and quality through code reviews.

The Analytical Data Science Lead guides technical strategy and operational execution for multiple clinical programs, ensuring high-quality and regulatory-compliant analytical outputs, while managing a team of programmers and fostering innovation.

Provide scientific and technical leadership for data science across drug development programs. Design and execute analyses on multimodal clinical and omics data, develop predictive models and AI/ML methods, define reproducible pipelines (Python/R/SQL/cloud), mentor data scientists, influence trial and biomarker strategy, and translate results to inform clinical development decisions.

Partner with large clients to design, run, and analyze controlled A/B tests, automate analyses with SQL and Python, build Tableau visualizations, communicate results to stakeholders, train teams, and influence incrementality product improvements.

Lead design and delivery of AI-ready scientific data products and integration architectures across discovery, preclinical, clinical, safety, and real-world evidence. Define metadata, data quality, lineage, and interoperability standards; partner with scientists, knowledge architects, AI engineers, and AWS platform teams; build predictive AIML models to support translational safety decision-making and enable knowledge graphs and semantic reasoning use cases.

Lead design and implementation of semantic and knowledge architectures for translational drug discovery. Develop ontologies, RDF-based knowledge graphs, semantic mappings, and reasoning frameworks to enable AI-driven translational intelligence across discovery, preclinical, clinical, and post-marketing domains. Partner with scientists, data scientists, and engineering teams; govern ontology lifecycle, ensure interoperability with industry standards, and support GraphRAG/LLM retrieval and agentic AI use cases.

Lead development and deployment of hybrid ML and mechanistic modelling pipelines (PK/PD, QSP) to generate actionable insights across drug discovery and development. Drive reproducible workflows, integrate diverse datasets, advance AI-augmented modelling, and communicate results to influence translational decision-making.

Lead design, development, validation, and deployment of machine learning and predictive analytics for U.S. Commercial. Integrate claims, EHR, specialty pharmacy, and commercial data to generate patient and provider insights. Partner with cross-functional teams, manage vendors, and communicate findings to leadership to inform commercial strategy and improve patient access, engagement, and treatment outcomes.

Lead digital marketing measurement and data science, analyze digital data, develop statistical models, and collaborate with marketing teams for optimization and insights.