Top Data Science Jobs in Boston, MA

Lead analytics initiatives to deliver strategic insights for consumer markets. Develop advanced analytics and BI solutions, manage teams, engage stakeholders, implement cloud analytics, and coach diverse teams to improve performance and decision-making.

Own analytics for Bevi digital experiences and websites, measure incremental impact of new flavors using experiments and quasi-experimental methods, build a flavor recommendation engine, create dashboards, and partner with Product, Design, Engineering, and Data Engineering to improve tracking, data quality, and product decisions.

Build and productionize end-to-end fraud and financial risk models: data acquisition, feature engineering, labeling, training, experimentation, deployment, and monitoring. Research new fraud types, drive product development, support data quality and access, and present analyses to inform product, operations, sales, and marketing.

Lead product analytics efforts by designing and evaluating experiments, building dashboards, and producing statistical insights. Partner with product, design, and engineering to measure product success, identify optimization opportunities, and embed ML-driven personalization into the product.

Build predictive models for churn, expansion, and look-alikes; run marketing mix and incrementality analyses; create leading indicators and cohort reporting; partner with Sales, Marketing, and RevOps to translate analytics into actionable recommendations and inform data infrastructure needs.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Lead and manage data science teams to build ML and deep learning models, maintain data pipelines and lakes, ensure data quality, deliver actionable insights, and manage project timelines and client expectations.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

Lead data science projects to optimize drug development, develop AI models for clinical trials, and collaborate with teams on biomedical workflows.

Embed with a business unit to define analytic problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end using SQL/Python.

Embedded in a core business area, own end-to-end analytics: define problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed decisions to improve product, growth, AI/automation, or partnerships.

Embedded data scientist owning analytics end-to-end for a business unit: define problems and metrics, pull and analyze data, design experiments, build reports, surface insights, and drive recommendations that influence product, growth, and strategic decisions.

Embedded within a core business area, define analytics problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end with stakeholders.

Embed within a core business area to define problems, build metrics, run analyses and experiments, create reports, surface insights, and drive data-informed product, growth, AI, or partnership outcomes end-to-end.

Embed on a core business team to define problems, build metrics/reports, run analyses and experiments, and drive data-informed recommendations end-to-end to improve growth, product, AI/automation, or partnerships.

Senior-level data scientist responsible for developing, maintaining, and enhancing AML/CTF risk models and GenAI-enabled analytics. Collaborates with business and engineering partners to translate business problems into scalable data science solutions, monitors model performance, and communicates insights to stakeholders. Provides technical leadership, mentors teammates, and ensures compliance with regulatory and governance requirements.

Develop, validate, and monitor financial crime risk models using statistical and machine learning techniques. Collect, wrangle, and visualize data, translate business problems into data science solutions, collaborate with engineers and analysts to build scalable analytics for transaction monitoring and AML risk assessment.

The Senior Data Scientist will implement and test ML models for international products, collaborating with cross-functional teams to drive insights and deploy solutions.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

As a Senior Data Scientist, you will develop technologies for signals exploitation, perform data analysis, and implement algorithms while collaborating with project teams.

Lead Data Scientist will define goals and metrics for algorithm Test & Evaluation, research T&E methodology, lead vendor coordination, present analyses and results to stakeholders, and transition algorithms into software and real-time embedded systems.

As a Data Scientist, you'll build production-grade ML systems for HVAC efficiency, working on scaling models, database optimization, and real-time monitoring systems.

The Data Scientist supports customer use of the Epistemix platform through data integration, modeling, and visualization, improving decision-making and customer satisfaction.

Conduct data science research on experimental methodologies and causal modeling, develop tools to analyze experimental data, prototype improvements to platform features, and communicate results to internal teams and external stakeholders to drive product and research decisions.

End-to-end data scientist who analyzes messy SEO/AEO data, designs and runs experiments, implements fixes using internal tooling, ships improvements, and communicates decision-ready reports to clients while improving tools and processes.