Top Data Science Jobs in Boston, MA

Lead and manage data science initiatives: develop ML/DL models, oversee data pipelines and lakes, validate data quality, coach team members, liaise with stakeholders, and ensure projects meet timelines, budgets, and client expectations while promoting innovation.

Lead teams to apply 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, secure project delivery, mentor staff, present findings to senior stakeholders, and help develop innovative analytics products for payer strategies.

Develop and apply statistical analyses, machine learning, and ML/DL/NLP techniques to extract insights from large datasets, build predictive models, design data solutions and visualizations, ensure data pipeline integrity, collaborate with clients, and mentor junior team members in PwC's Assurance practice.

Embed with product teams to design and analyze experiments, build measurement frameworks and behavioral models, create scalable reporting, and translate analyses into actionable product recommendations to improve member outcomes.

As a Data Scientist, you will design and operationalize advanced analytics, develop scalable models, and collaborate with teams to enhance product capabilities in healthcare.

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.

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

As a Data Scientist at SentiLink, you'll develop fraud detection models using machine learning, engage in product development, and support analysis across teams. This role emphasizes hands-on technical work and end-to-end project ownership in a fast-paced environment.

The Data Scientist II will lead RWE studies, derive insights from real-world clinical data, and contribute to methodologies using advanced statistical techniques and AI tools, collaborating closely with pharmaceutical partners.

Lead and scale enterprise data science capabilities for operations-focused use cases in pharma. Build and deploy production ML/AI and optimization solutions, design reusable ML platforms, engage stakeholders to translate problems into high-impact analytics, and mentor teams while driving adoption and lifecycle management of digital products.

Support health plan quality and affordability initiatives by analyzing diverse data sources, mapping clinical journeys, identifying drivers of quality variation, building dashboards, testing hypotheses, and presenting actionable insights to improve population health, member experience, and Medicare/Commercial quality measures.

Perform quantitative real estate and urban economic analysis, applying econometric and spatial methods to synthesize property, macroeconomic, and geospatial data. Build and maintain automated data pipelines, ensure data integrity, document dataflows, and prototype ML/AI methods. Collaborate with research, GIS, and engineering teams to produce reproducible insights and analytical products for stakeholders.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.

Design and implement quantitative behavior metrics for autonomous vehicles, build and maintain data pipelines and dashboards, analyze large-scale vehicle logs and simulation data to identify regressions, set metric thresholds to support geofence expansion, and communicate findings to technical and non-technical stakeholders.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

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

Embedded within a core business area, own end-to-end analytics: define problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-driven recommendations to influence product, growth, AI, or partnerships.

Embedded on a business unit, define problems and metrics, run analyses and experiments, build reports, surface insights, and drive data-informed decisions across growth, product, AI, and partnerships.

Embed with a business unit to define problems, build metrics, run analyses and experiments, generate insights, and drive data-informed decisions end-to-end to improve growth, product, AI automation, or partnerships.

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

Embedded on a business unit (growth, product, AI, or partnerships) to define problems, set metrics, build reports, run analyses and experiments, surface insights, make recommendations, and drive end-to-end execution to improve key business metrics.

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

Support enterprise cardiovascular quality improvement and clinical research by analyzing clinical datasets, developing dashboards and reports, performing cohort development and statistical analyses, ensuring regulatory and data governance compliance, and communicating findings to clinical and operational stakeholders to inform quality and research initiatives.

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.