Top Data Science Jobs in Boston, MA

Lead measurement and data science strategy for Klaviyo’s AI agents, Composer and Customer Agent. Develop frameworks for adoption, quality, engagement, customer value, cost, and revenue impact. Apply experimentation, causal inference, statistical modeling, conversation analysis, human evaluation, and LLM-based evaluation. Partner with Product, Engineering, Finance, and executives, build governed metrics and scalable analytics, influence product and pricing decisions, and mentor data scientists.

Lead end-to-end revenue forecasting, developing production statistical and machine learning models, setting evaluation and governance standards, investigating performance drivers, and building scalable self-service analytics. Partner with Product, Finance, Engineering, and Go-to-Market leaders to support strategic decisions, mentor data scientists, guide technical direction, and communicate complex findings to executive and non-technical audiences.

Leads data science for Capital One’s US Card business, developing and deploying machine learning models across marketing, acquisitions, underwriting, and fraud prevention. Partners with data scientists, engineers, and product managers; analyzes large-scale numeric and textual data using cloud and open-source technologies; translates complex insights into business outcomes; and develops talent while guiding technical innovation.

Lead data science for Capital One’s US Card organization by building and deploying machine learning models across the credit card lifecycle. Partner with software engineers and product managers, analyze large-scale customer data using Python, cloud, and distributed computing technologies, and translate statistical insights into business outcomes. The role also emphasizes innovation, stakeholder communication, technical leadership, talent development, and managing complex data science initiatives.

Lead the development of deep learning and computer vision models for MRI, X-ray, CT, and PET medical imaging in clinical trials. Build image processing pipelines, analyze disease progression and treatment response, develop imaging biomarkers, integrate AI into trial workflows, support FDA-compliant endpoints, and present findings to clinical, regulatory, executive, and external stakeholders.

Lead data science initiatives supporting drug development, including patient stratification, biomarker identification, therapeutic hypothesis evaluation, clinical trial optimization, and AI agent development. The role applies machine learning, computational biology, and advanced analytics to biomedical data while collaborating with clinical, research, and engineering teams. Responsibilities also include presenting complex findings to senior stakeholders and contributing to production-oriented AI solutions.

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive recommendations and decisions with cross-functional stakeholders. The role requires independent analytical problem-solving, strong communication, and ownership of ambiguous initiatives that materially improve business outcomes.

Own end-to-end data science for a business area such as growth, product, AI, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires independently structuring ambiguous problems, using SQL or Python, communicating findings clearly, and driving measurable business outcomes.

Develop and refine machine learning and LLM-powered models for clinical use cases, from prototyping through production. Conduct rigorous evaluation and error analysis, identify failure modes, implement targeted improvements, and ship frequent model updates. Apply prompting, structured outputs, tool use, and evaluation frameworks while contributing to performance metrics, reporting, and dashboards. Collaborate closely with the ML team in a fast-paced, high-ownership environment.

Own end-to-end data science for growth, product, automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires strong SQL or Python skills, independent analytical problem-solving, clear communication, and ownership of ambiguous initiatives.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; communicate insights and recommendations; and drive measurable outcomes with cross-functional stakeholders. The role requires strong analytical thinking, independent data work using SQL or Python, ownership of ambiguous problems, and persuasive communication with leadership.

Develops and enhances AML/CTF risk models and AI solutions for financial crime compliance. Applies statistics, machine learning, anomaly detection, Python, SQL, and GenAI techniques to complex data. Partners with business, engineering, and analytics teams to translate business problems into scalable solutions, communicate insights, monitor model performance, address risks, and provide technical leadership across enterprise initiatives.

Conduct econometric, causal inference, cost-benefit, and financial analyses evaluating policies and economic mobility programs. Acquire and manage large datasets, develop impact and investment models, create calculators, dashboards, and visualizations, and translate technical findings into policy insights. The role also produces technical and public-facing reports, supports data science education, mentors junior researchers, and presents findings to policymakers and other stakeholders.

Develop forecasts, machine learning models, and optimization algorithms for renewable energy and battery storage assets. Analyze large energy-market datasets, build simulations, deploy production-grade data science workflows, create dashboards, and establish asset-optimization metrics. The role collaborates with engineering, operations, and energy markets teams to support real-time operations, market strategies, and business development.

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Conduct bioinformatics and data science analyses to identify and prioritize vaccine targets. Develop comparative genomics, phylogeny, functional prediction, and omics analysis methods; build pipelines, databases, and dashboards; interpret findings; and communicate results to computational and experimental teams. Coordinate cross-functional Vaccines R&D projects and contribute to scientific publications, reports, and presentations.

Leads organization-wide data science strategy, architecture, and AI/ML standards across multiple teams. Drives multi-year initiatives, platform and tooling decisions, scalable production systems, and measurable improvements. Provides technical leadership in generative AI, agentic systems, LLMs, deep learning, and advanced modeling. Influences executives and cross-functional roadmaps, mentors senior contributors, supports hiring, and promotes responsible AI, data governance, and innovation.

Develop and validate clinical heuristics and algorithms using real-world healthcare data. Build Python profiling pipelines, automated data-quality checks, and longitudinal patient cohorts. Analyze complex clinical databases and ETL pipelines, evaluate AI, NLP, and heuristic outputs, and collaborate with clinical, labeling, engineering, and data science teams. Maintain documentation, configuration, metrics, and validation standards for transparent, high-quality clinical analytics.

Leads health economics and outcomes research and real-world evidence projects for pharmaceutical, biotech, academic, and other partners. Designs and analyzes pharmacoepidemiologic studies, treatment patterns, healthcare utilization, causal inference, and survival analyses. Builds reusable analytical infrastructure, collaborates with scientists, engineers, and product teams, interprets results for stakeholders, supports publications and regulatory work, and ensures compliance with data governance and applicable regulations.

Develop and deploy data science solutions for pharmacy benefits and member experience, including predictive models, generative AI pipelines, and rules-based logic. Extract and analyze large-scale data with Python and SQL, manage model lifecycles, monitor production performance, and communicate insights to clinical and business leaders. Collaborate with data scientists, engineers, and operational stakeholders to solve complex healthcare and PBM problems.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.