Data Science Jobs in Cambridge, MA

Lead product strategy and roadmaps for APM UI, business logic (data quality, KPIs, dependency management), and advanced insights. Drive discovery, workflow mapping, and AI-embedded workflows; partner with UX, Data Science, Sales, and Engineering to prototype, ship, instrument, and iterate on features to improve time-to-action and operational efficiency for renewable asset operators and analysts.

The role involves leading AI and advanced analytics initiatives in life sciences, managing client relationships and P&L, developing strategies for growth, and contributing to the team's thought leadership while enhancing analytical models and solutions.

Ten-week, full-time paid Data Science internship (June–August 2027) working on machine learning, deep learning, and large-scale data analysis. Interns evaluate modeling tools, integrate internal and external data/APIs, design data science models, and present insights to stakeholders. In-person attendance at an assigned U.S. location is required; interns may be considered for full-time roles after graduation.

Ten-week, full-time paid Data Science internship for current PhD students. Interns will build and evaluate modeling and analytics tools, integrate internal and external data sources, design data science models, and communicate results. Candidates must continue their PhD after the internship and may be evaluated for full-time roles upon graduation.

Lead research at Galois by developing externally funded programs in Formal Methods, collaborating with teams, mentoring staff, and engaging with clients.

The Head of Data Science & AI Engineering leads Ipsen's technical strategy in advanced analytics and AI, overseeing data products, mentoring teams, and ensuring compliance in a dynamic biopharmaceutical environment.

Lead development and maintenance of scalable computational frameworks for pooled, arrayed, single-cell, and optical CRISPR screens; oversee image analysis for high-content screens; integrate functional genomics with transcriptomics and proteomics; apply ML/LLM and network methods for target prioritization; manage and mentor PhD-level scientists and collaborate across disciplines to support therapeutic target identification.

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.

Lead the development of AI applications and data products to enhance Immunology R&D. Manage a technical team and collaborate across departments.

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.

As the Director of Product Marketing, you will lead the strategy for positioning, pricing, and packaging products while managing a team and collaborating cross-functionally to increase user acquisition and engagement.

The GNC Mission Analyst analyzes performance of weapon system concepts, supports research, and collaborates on mission designs, addressing complex national challenges.

Apply advanced AI, ML, and computational modeling to drug discovery and early development. Build scalable data pipelines and predictive models, integrate diverse scientific datasets, ensure model validation and reproducibility, and communicate insights to multidisciplinary teams to support decision-making.

Lead data science and knowledge engineering initiatives to transform proposal, RFP, and due diligence content. Build NLP, semantic retrieval, and recommendation systems, design taxonomies and content governance, partner with stakeholders to improve first-draft quality and reuse, and measure impact through metrics like reuse rates, first-draft quality, SME touch frequency, and cycle-time reduction.

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 a multidisciplinary engineering and data science team in a forward-deployed model to deliver production-ready, AI-powered solutions. Own portfolio delivery, coach and develop talent, act as player‑coach on engagements, drive engineering excellence and responsible AI practices, and partner with leaders to ensure operational health and reusable accelerators.

Lead development and deployment of production-grade AI/ML and agentic systems for computational toxicology. Fine-tune foundation models, integrate multimodal datasets, establish MLOps and governance, collaborate with toxicologists and scientists, and ensure explainability, reproducibility, and regulatory-ready practices for preclinical safety assessments.

Lead development and deployment of AI-driven phenotyping pipelines using EMR and real-world data to define cohorts, extract features, and validate phenotypes. Integrate clinical, genomics, and biomarker datasets, build reproducible SQL/Python/R pipelines, apply statistical genetics and rare variant analyses, and improve data infrastructure to support Genetics-led programs.

Lead multimodal oncology biomarker discovery using advanced AI/ML to integrate RNAseq, WES, ctDNA, digital pathology, proteomics, and clinical data. Drive translational analyses across internal stakeholders, validate biomarkers, inform diagnostic development, and manage a team of computational scientists supporting drug development.

Lead design and implementation of ML, multi-objective optimization, GenAI and LLM solutions to support clinical trial operations (enrollment forecasting, cost estimation/optimization, site selection), perform stochastic enrollment simulations, harmonize clinical data, and communicate results to stakeholders while mentoring junior colleagues.

Lead multi-modal biomarker discovery in oncology by analyzing large clinico-genomic datasets (RNA-seq, WES/WGS, proteomics, imaging, single-cell, epigenetics). Apply deep learning, statistical learning, and predictive modeling, collaborate with AIML teams, and present findings to cross-functional stakeholders to inform translational and clinical development decisions.

Lead development of ML architectures that integrate multimodal datasets (single-cell, spatial omics, histopathology, functional genomics, real-world clinical data). Validate models with wet‑lab and clinical collaborators, perform counterfactual/in-silico perturbation validation, extract interpretable features to generate testable oncology hypotheses, contribute reusable, well‑engineered tooling, and publish/represent the team in high-impact venues.

Lead and execute Real-World Evidence (RWE) studies end-to-end: design, data feasibility, extraction, cleaning, statistical analysis, programming, and interpretation. Develop shared tools and best practices, mentor team members, produce protocols and analysis-ready deliverables, and support regulatory interactions alongside therapeutic area scientists.

Lead and manage a data science team building and maintaining AML/CTF models and analytics (including LLMs) to detect financial crime. Own model lifecycle, stakeholder engagement, production deployment, performance monitoring, and delivery of user-facing analytics and tools to drive risk detection and operational efficiency.