Top Remote Senior Level Data Science Jobs in Boston, MA

The Senior Data Scientist will design and implement AI-enabled products using clinical trial data, mentor team members, and collaborate with stakeholders.

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 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.

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.

The Senior Manager, Data Science & Analytics will lead the analytics strategy for client engagement, focusing on data-driven insights and marketing performance to assist in customer acquisition efforts.

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 build models and analyses, design experiments, integrate datasets, and leverage AI for improved workflows and insights in data science.

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

Embed within a core business area to define problems, build metrics and analyses, run experiments, generate insights from data, and drive decisions end-to-end to improve product, growth, AI/automation, or partnerships.

Design and deploy scalable statistical and machine learning models on large health datasets (claims, EHR, pharmacy). Prototype NLP search, resolve identity/merge anomalies, collaborate with product, engineering and analytics to productionalize models, monitor performance, and drive data-driven product improvements.