Data Science Jobs in Cambridge, MA

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

The Associate Director advances Takeda Research strategy and transformation initiatives, including Labs of Tomorrow. Responsibilities include strategic analysis, roadmap development, scenario planning, executive communications, governance support, and translating enterprise priorities into actionable research plans. The role partners with senior scientific leaders and operational teams while monitoring AI, data science, automation, and emerging research technologies. Candidates need advanced life sciences education, drug discovery expertise, pharmaceutical or biotech strategy experience, strong analytical and communication skills, and consulting-style problem-solving ability.

Leads integration of Takeda Research’s AI, automation, and technology transformation initiatives. Develops strategic recommendations, manages dependencies, evaluates emerging technologies, maintains multi-year roadmaps, supports executive governance, and aligns senior stakeholders. Requires life sciences strategy experience, consulting background, AI and drug discovery fluency, transformation leadership, and advanced executive communication skills.

Leads strategy, development, and growth of WHOOP’s healthcare product portfolio. Builds and scales the Healthcare Product team, partners with clinical, regulatory, engineering, data science, design, and commercial teams, and brings evidence-based digital health products from concept through launch. Priorities include preventive health, clinical monitoring, chronic condition management, digital biomarkers, provider solutions, regulatory requirements, clinical evidence, privacy, quality, adoption, and business performance.

Configure and implement Fenergo solutions for AML and sanctions compliance, translating business and regulatory requirements into effective system configurations. Analyze complex data, apply advanced analytics and AI fluency, develop client business cases, demonstrate FenX capabilities, improve processes, engage stakeholders, and manage team members while delivering strategic consulting insights.

Leads AI modeling and data analytics strategies for AML and sanctions clients in banking and capital markets. Oversees multiple projects, develops strategic solutions, supports business development, maintains executive client relationships, and mentors teams. Responsibilities include improving data quality and architecture, validating analytics integrity and compliance, promoting AI-human collaboration, driving thought leadership, and converting market opportunities into business outcomes.

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.

Develop AI and data analytics solutions for AML and sanctions clients within PwC’s Risk & Regulatory practice. Responsibilities include Python-based data analysis, designing data architectures and pipelines, creating dashboards, interpreting complex datasets, engaging stakeholders, and delivering actionable insights and tailored client solutions. The role also involves client relationship building, problem-solving, continuous improvement, and contributing to strategic business outcomes.

Leads data science for enterprise agentic AI products, developing and deploying machine learning models, designing experiments, analyzing telemetry, and translating findings into business strategy. Partners with data scientists, AI/ML engineers, and product managers while guiding large-scale analysis across numeric and textual data. The role also requires technical leadership, stakeholder communication, innovation, talent development, and expertise in statistical modeling, deep learning, and cloud-based data science.

Leads data science initiatives for customer financial resiliency, partnering with engineers and product managers. Builds, validates, implements, and productionizes supervised and reinforcement learning models across large-scale data. Uses Python, cloud platforms, open-source machine learning tools, and statistical methods to deliver personalized customer solutions. Translates complex analyses into business outcomes, evaluates emerging technologies, and develops team talent.

Leads enterprise data science initiatives for agentic AI products, including AI/ML model development, experimentation, telemetry analysis, and strategic recommendations for senior leadership. Partners with data scientists, engineers, and product managers while translating complex statistical insights into business outcomes. Requires extensive experience in quantitative analytics, large-scale data analysis, production AI/ML deployment, A/B testing, and team leadership.

Leads hands-on development and evaluation of generative AI, agentic systems, MLOps solutions, semantic data layers, knowledge graphs, and GenAI evaluation frameworks. Collaborates across data science, engineering, and business teams; champions best practices, leads working groups, provides technical consulting, and communicates complex findings to stakeholders. This is an individual contributor role focused on enterprise data strategy and AI/ML innovation.

Own long-term product strategy and roadmap for Circle's digital assets and capital markets platform. Lead cross-functional initiatives, manage regulatory and market risks, define forecasting and liquidity models, influence executives, and mentor product managers to scale global stablecoin and capital access infrastructure.

Leads the strategic application of medical imaging across Roche’s cardiovascular, renal, and metabolism drug development pipeline. Defines scalable, regulatory-ready imaging endpoints and roadmaps; applies AI, machine learning, and advanced analytics; oversees imaging algorithms from assessment through deployment; and drives global cross-functional initiatives, governance, partnerships, and data standardization.

Leads imaging science strategy for Roche’s Cardiovascular, Renal & Metabolism drug development portfolio. The role defines scalable, regulatory-ready imaging endpoints; integrates AI, machine learning, and advanced analytics; directs imaging algorithms from assessment through deployment; and leads global, cross-functional initiatives. Responsibilities include roadmap development, governance, stakeholder influence, clinical trial support, external collaboration, and standardization of imaging data assets.

Lead data science for US Card customer management by partnering with data scientists, engineers, product managers, and business teams. Build, validate, and implement machine learning models using large-scale numeric and textual data. Apply statistical modeling, clustering, classification, sentiment analysis, time series, and deep learning to generate customer insights and forecasts. Translate technical findings into business outcomes while contributing to innovation, stakeholder collaboration, and talent development.

Leads translational and clinical biomarker strategies across neuroscience drug development programs. Develops decision-centric approaches for target engagement, pharmacodynamics, patient stratification, dose selection, and clinical outcomes. Interprets complex biomarker and clinical datasets, evaluates emerging technologies, partners cross-functionally, communicates recommendations to stakeholders, mentors scientists, and supports publications and external collaborations.

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.

Leads pricing and revenue data science initiatives for consumer markets clients. Develops predictive models, forecasts trends and customer behavior, manages dashboards and visualizations, ensures data quality, and translates complex analytics into strategic decisions. Oversees client engagements, validates outcomes, directs complex projects, and coaches diverse, high-performing teams.

Leads AI partnership between Clinical Development, Enterprise AI, and Regulatory teams. Prioritizes regulatory AI use cases, guides buy-versus-build decisions, establishes value metrics, and coordinates responsible, compliant AI delivery. The role connects regulatory domain experts with AI, data, IT, product, and engineering teams to develop audit-ready solutions aligned with clinical data standards, governance, infrastructure, and GxP requirements.

Tenure-track and tenured faculty will teach undergraduate and graduate courses, mentor students and postdoctoral researchers, conduct internationally recognized and independently funded AI or computer science research, and provide university and professional service. Research may focus on physical AI, human-centered AI, AI and software engineering, or AI safety, security, and alignment. Candidates will also contribute to inclusive teaching, experiential learning, and interdisciplinary collaboration across Northeastern’s colleges and research institutes.

Individual contributor data scientist leading applied GenAI initiatives from research through production. Develops LLM-based applications, machine learning solutions, and efficient inference tools; collaborates with engineering and senior business stakeholders; identifies AI/ML use cases; manages multiple initiatives; communicates analytical findings; and mentors team members while delivering measurable business impact.

Designs, develops, and improves AI and machine learning models for diverse R&D projects, including generative AI, predictive maintenance, data intelligence, and bioinformatics. Collaborates with technical teams and customers to define requirements, solve problems, conduct research, and deliver software solutions. Requires strong programming, analytical, mathematical, communication, and problem-solving skills, along with the ability to obtain and maintain a U.S. government security clearance.

Designs and develops machine learning and artificial intelligence models, improves model performance, and applies deep learning to complex problems across research and development projects. Collaborates with technical teams and customers to define requirements, conducts research with guidance, solves well-defined problems, and communicates progress and challenges. Requires strong analytical, mathematical, software development, and communication skills.

Design, develop, deploy, and optimize machine learning models and production services for healthcare products. Build data pipelines, feature workflows, and training datasets; apply testing, validation, MLOps, cloud infrastructure, and monitoring practices. Collaborate with technical and non-technical stakeholders, improve model performance, develop reusable tools and frameworks, evaluate emerging AI technologies, support incident remediation, and provide technical guidance. The role requires production machine learning experience and proficiency in Python, SQL, and Unix environments.