Top Data Science Jobs in Boston, MA

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

Use advanced analytics, statistical methods, and machine learning to transform large datasets into actionable insights. Build data solutions and pipelines, perform exploratory analysis, create visualizations, validate models (including generative AI and NLP), collaborate with clients, mentor juniors, and maintain data quality to inform strategic decisions.

Lead data science initiatives within Data and Analytics Engineering to build predictive models, maintain data pipelines and lakes, apply ML/DL and generative AI, ensure data quality, coach teams, manage project timelines, and collaborate with stakeholders to drive data-driven business growth.

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

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

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.

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

Design and deploy predictive models and AI-driven solutions to solve specialty pharmacy and healthcare operational challenges. Perform exploratory and statistical analyses, build dashboards and reports, and translate findings for business and executive audiences. Partner cross-functionally to identify opportunities, support AI initiatives (including AI agents and automation), and drive measurable operational impact.

The Senior Data Scientist will engage with business partners to solve problems using data science, lead model development, and mentor others, focusing on supply chain and causal modeling.

Analyze large, diverse datasets (claims, hazard, geospatial) and apply advanced data science, ML, and AI to model catastrophe, hazard, exposure, and man-made risks. Evaluate vendor models, develop internal risk views and tools, collaborate with domain experts, and support underwriting decision-making and account-specific risk analyses.

Hands-on data scientist owning the full lifecycle of risk models for credit, accident, and fraud. Build, calibrate, deploy, and monitor predictive models and risk scores; manage model orchestration and vendor integrations; detect drift and recommend mitigations. Collaborate with Risk, Finance, Product, and Engineering to set thresholds and translate model outputs into actionable decisions.

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.

The Data Scientist will build predictive models, drive forecasting, design experiments, and translate findings for stakeholders, optimizing healthcare operations.

Perform statistical analysis, predictive modeling, and data visualization on workforce and training data. Build dashboards, produce ad hoc analyses and decision-support products, advise on data governance/privacy, and collaborate with stakeholders to define metrics, deliverables, and reproducible analytic workflows.

Use statistical and causal inference methods to build, implement, and monitor marketing models (Bayesian MMM, multi-touch attribution), analyze A/B and uplift tests, partner with marketing to test and drive data-informed decisions from large web-product datasets.

Lead and grow a 4–6 person data science team, set the roadmap, deliver marketplace forecasting and production ML (matching, scoring), embed data science cross-functionally, and establish modeling, experimentation, and monitoring best practices.

Embedded on the central Data Science team, you will define the right problems, build metrics, run analyses and experiments, create reports, and drive data-informed recommendations end-to-end across growth, product, AI/automation, or partnerships.

Embed with a business unit to define problems, run analyses, design experiments, build reports, surface insights, and drive data-informed decisions across growth, product, AI, or partnerships.