Data Science Jobs in Cambridge, MA

Leads cross-functional execution for WHOOP’s Core Algorithm Development team, coordinating engineers, data scientists, firmware, software, product, and validation teams. Owns program planning, milestones, dependencies, risk mitigation, algorithm validation, integration, release readiness, reporting, and executive communications. The role establishes scalable development and release processes to improve delivery predictability, quality, and velocity across concurrent physiological algorithm programs.

Manage delivery of a portfolio of AI and data science projects from planning through implementation. Coordinate technical and non-technical teams, dependencies, requirements, budgets, timelines, and stakeholder expectations. Lead Agile ceremonies, drive accountability, translate AI capabilities into actionable requirements, and proactively identify and mitigate risks across complex, concurrent initiatives.

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.

Design, build, and evaluate generative AI and agentic systems; develop enterprise semantic layers and knowledge graphs; create GenAI evaluation frameworks assessing performance, reliability, and safety; collaborate across data science, engineering, and business teams; champion best practices; lead cross-functional working groups; communicate technical findings; and provide technical consulting on high-impact initiatives.

Lead externally informed strategy and communications for Data Science & Digital Health. Synthesize market trends, produce strategic content and case studies, translate AI/data science signals into insights for leadership, maintain stakeholder mapping, and align internal and external messaging to inform enterprise decisions.

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.

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.

As a Data Science Manager, you will lead a team in developing personalized experiences through machine learning, aligning efforts with product and engineering to drive impact and innovation.

As a Lead Data Science Analyst, you will develop predictive models, conduct statistical analyses, and create AI-driven solutions to enhance GTM strategy and customer outcomes.

Prospect, develop, and close enterprise sales opportunities for Snowflake’s data platform. Responsibilities include conducting discovery calls, demos, and presentations; advising customers on IT roadmaps; expanding regional accounts; achieving quarterly and annual quotas; developing territory sales strategies; and collaborating with sales engineering, professional services, marketing, legal, and finance teams.

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.

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.

Leads AI enablement, advanced analytics, automation, process mining, and digital transformation across Global Clinical Operations. Develops analytics assets, low-code/no-code solutions, workflow automations, reusable AI frameworks, and platform capabilities. Evaluates emerging technologies, defines business requirements, supports proofs of concept, promotes responsible AI, and drives adoption through communities of practice, super-user networks, coaching, and stakeholder engagement. Partners with clinical operations, enterprise technology teams, vendors, and global matrixed organizations to improve clinical trial execution and operational performance.

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.

Provides executive scientific and strategic leadership in quantitative clinical pharmacology consulting. Leads Pop-PK, PK-PD, NCA, MIDD, regulatory strategy, trial design, and quantitative decision-making engagements. Develops thought leadership, manages major client relationships, supports business development, mentors quantitative teams, and collaborates with statisticians, software teams, business developers, and data scientists. The role also involves regulatory authority engagement, technical oversight, publications, presentations, and client travel.

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.

Lead data science projects to advance drug development, implementing models for patient selection and clinical trial optimization, and collaborating with cross-functional teams.

Drive business growth across Boston and New England by selling biomedical data, analytics, bioinformatics, informatics, and consulting services to biotech, pharma, and life sciences organizations. Responsibilities include prospecting, pipeline development, relationship building, consultative selling, coordinating technical presentations and proposals, negotiating contracts, closing deals, meeting sales targets, and maintaining CRM forecasts and reporting.

Leads enterprise AI strategy and execution by identifying opportunities, building business cases, defining product vision and roadmaps, and guiding AI product development. Partners with business, data science, and technology teams to deliver customer-driven solutions, translate stakeholder needs, prioritize initiatives, and support innovation. The role also mentors team members, applies design thinking and UX practices, and evaluates build, buy, and partnership options.

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.

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.

Leads Scribd’s content intelligence and UGC supply portfolio, owning corpus classification, metadata, quality, gap analysis, AI infrastructure, contributor experiences, and content acquisition strategy. Partners with ML, engineering, data science, consumer product, and business development teams to connect content supply with business value. Manages senior product leaders and established engineering and analytics programs while setting portfolio roadmaps and representing content strategy with executives.