Data Science Jobs in Cambridge, MA

Leads data scientists and machine learning engineers developing personalized customer experiences at scale. Sets team strategy, roadmaps, and priorities; partners with Product, Engineering, Marketing, Revenue, and Data Science leaders; guides machine learning systems through development, deployment, monitoring, and optimization; advances MLOps, software engineering, experimentation, and production reliability; and manages capacity, resources, technical standards, and team development.

Leads the strategy for scaling data science analyses used in pharmaceutical asset acquisition and diligence. Directs cross-functional Data Science and Engineering teams, determines which one-off analyses should become landscape analyses or durable tools, sets roadmaps, evaluates statistical and technical validity, identifies AI opportunities, and connects scientific findings to business impact, asset valuation, and sourcing decisions.

Analyzes workforce and healthcare data to identify trends, build predictive and machine learning models, and deliver actionable recommendations. Uses Python, Alteryx, and Power BI for analytics, visualization, and reporting. The role supports workforce strategy, employee engagement, organizational effectiveness, talent management, and healthcare cost insights. Responsibilities also include client collaboration, relationship management, team guidance, and applying risk adjustment and medical economics concepts to inform strategic decisions.

Lead the vision, roadmap, discovery, and delivery of AI-enabled analytics products for nursing and medical education. Translate customer and business needs into product strategies, prioritize backlogs, guide cross-functional teams, and measure adoption, usability, customer impact, and business outcomes. Champion responsible AI practices involving quality, privacy, transparency, and trust while using customer feedback and product data to continuously improve products.

Develops AI and machine learning prototypes for insurance applications using Python, large language models, NLP, retrieval-augmented generation, agentic AI, information retrieval, and computer vision. Collaborates with data scientists and engineers on ideation, product design, algorithm development, data analysis, and experimentation in an R&D environment.

Owns the strategy and roadmap for Duck Creek’s billing product domain, including invoicing, payments, collections, commissions, disbursements, reconciliation, and automation. Partners with engineering, architecture, data, finance, sales, and customers to deliver configurable, scalable, compliant, and explainable billing capabilities. Leads discovery, prioritization, AI-enabled decision support, governance, adoption, and financial outcomes for insurance billing workflows.

Own the acquisition product roadmap and engineering priorities to drive durable user growth. Define target audiences and channel mix, build product-driven acquisition mechanics (referrals, growth loops), enable channel effectiveness (discoverability, search/AI readiness, integrations, tracking), test new acquisition tactics, and align product investment with Performance Marketing, SEO, Partnerships, and Brand while measuring long-term subscriber value.

Lead Customer Success for enterprise HR clients by owning onboarding, engagement, renewal, and expansion. Build scalable onboarding and enablement, track customer health and ROI, facilitate executive workshops, and feed customer insights into product and GTM. Hire and develop a high-performing CS team and partner cross-functionally.

Build and maintain backend services and metadata pipelines supporting data discovery, governance, and AI/ML workflows. Responsibilities include integrating relational databases, developing Java, Node.js, and Python services, optimizing distributed pipelines, implementing Terraform infrastructure, supporting GitLab CI/CD, and writing comprehensive tests. The role also involves troubleshooting scalability and reliability issues, designing asynchronous workflows, and collaborating on modern data lake, search, metadata, and LLM-enabled technologies.

Develops and positions Verizon Frontline technology solutions for public safety and public-sector clients. Partners with sales, business development, and cross-functional teams to identify digital transformation needs, create end-to-end solution roadmaps, lead customer engagements, and communicate value propositions through presentations, proposals, white papers, and industry events. Builds executive relationships, supports sales strategy, enables 5G and private-network use cases, and informs product roadmap priorities.

Conduct computational and statistical research using biobank-scale genetics, proteomics, EHR, and other multi-omics datasets. Develop scalable pipelines and causal modeling methods for biomarker and therapeutic target discovery, analyze genetic and perturbation-based sequencing data, and collaborate with wet-lab and computational teams. Communicate findings through publications, presentations, and cross-functional collaboration.

Leads end-to-end AI and data science consulting engagements, from problem framing and prototyping through production implementation. Combines hands-on development, solution architecture, code review, and technical troubleshooting with client advisory, team leadership, mentoring, business development, and executive communication. Translates ambiguous business challenges into scalable AI, machine learning, optimization, and data strategies while managing multidisciplinary delivery teams and contributing to thought leadership.

Leads data scientists and machine learning engineers developing personalized customer experiences. Sets strategy, roadmaps, and priorities; partners with Product, Engineering, Marketing, Revenue, and Data Science leaders; oversees machine learning systems from design through deployment, monitoring, and optimization; advances MLOps, software engineering, experimentation, and production reliability practices; and manages capacity, resources, technical standards, and team development.

Data Science Interns will support the development, deployment, and operation of machine learning solutions in production systems. They will contribute to model development, engineering design, scalable deployment, internal R&D, and projects involving recommendation engines, fraud detection, and marketing optimization. Interns will collaborate with experienced data science and machine learning engineers and receive mentorship while learning end-to-end workflows.

Own and grow BizzyCar’s AI-powered automotive retail products. Responsibilities include developing roadmaps, gathering requirements, coordinating software development, commercializing solutions, designing rapid-cycle tests, managing KPIs, and representing the company with customers and partners. The role requires strong product management experience in B2B SaaS, generative AI proficiency, analytical skills, and familiarity with data science, machine learning, and AI.

Lead data science strategy for autonomous vehicle fleet and simulation data. Build statistical, predictive, and generative models to assess safety and performance across operating conditions, translate telemetry into launch-readiness metrics, support rapid operational investigations, and advise autonomy and systems teams. Mentor and scale a four-person team while communicating analytical findings to engineers and executives.

Develop and evaluate AI and machine learning methods for low-molecular-weight drug discovery. Build generative, multimodal, foundation, and agentic models; establish rigorous benchmarking; connect model performance to experimental decisions; and integrate AI into design-make-test-analyze workflows. Collaborate with medicinal chemistry, DMPK, biology, engineering, and data science teams while communicating results to multidisciplinary stakeholders.

Provides strategic leadership for enterprise data science, analytics governance, automation, and data enablement across therapeutic areas. Oversees analytics pipelines, data architectures, visualization, AI/ML adoption, regulatory compliance, and clinical data standards. Advises executives, leads cross-functional collaboration, manages analytics teams, drives organizational change, and advances scalable analytical capabilities. Requires extensive experience with large datasets, programming, statistics, data visualization, database concepts, and people management in clinical or digital health settings.

Lead and grow a Data Science team for Provider Products, set analytical direction, own experimentation and measurement, review methods, partner with product and engineering, hire and develop data scientists, standardize analytics and integrate generative AI to improve workflows and outputs.

Conduct computational oncology research using statistics, machine learning, AI, and multi-omics data. Analyze NGS, functional genomics, CRISPR, and single-cell datasets; nominate targets, biomarkers, and drug combinations; integrate biological evidence; apply foundation models and LLM-based tools; and collaborate with experimental, engineering, and program teams to support reproducible discovery research.

In this role, you will design and deploy advanced machine learning and Generative AI solutions, leading projects from conception to deployment while mentoring junior team members.

Lead the strategy and execution of real-world data capabilities, focusing on data lifecycle management, vendor relationships, and collaboration with various teams to deliver analysis-ready datasets.

Advise C-level life sciences and healthcare executives on AI and data strategies, lead large-scale digital transformations, develop scalable solutions, oversee high-quality consulting delivery, build long-term client relationships, generate new business, and manage cross-functional engagement teams and multimillion-dollar client portfolios.

Develop statistical and machine learning algorithms for weather, climate, and catastrophe modeling. Collaborate with scientists and engineers, analyze large atmospheric datasets, and conduct model sensitivity and validation testing. The role requires advanced knowledge of statistical inference, extreme value models, distribution fitting, estimation methods, and goodness-of-fit testing, along with demonstrated machine learning experience.

Build and maintain metadata capture, transformation, and delivery pipelines for a data management platform. Develop backend services with Python, Java, and Node.js; integrate relational databases and REST APIs; implement Terraform infrastructure; and support GitLab CI/CD. Optimize distributed systems for performance, reliability, scalability, and secure asynchronous processing. Write automated tests, troubleshoot services using operational metrics, and contribute to AI/ML metadata pipelines, search, data lake, and LLM-powered initiatives.