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 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 VP of Agentic Systems will lead data science research and development, focusing on AI systems to enhance cybersecurity operations, requiring strong leadership and collaboration across technical teams.

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.

Lead Sprout's centralized Decision Analytics function to transform reporting into predictive, insight-driven decision-making. Set vision, operating model, and standards; manage managers, analysts, and data scientists across domain pods; partner with senior stakeholders; champion AI and ML integration; drive change management and build a high-impact analytics culture.

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.

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'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.

Lead data science strategy across complex customer engagements, turn ambiguous business problems into repeatable ML solutions, define modeling and evaluation standards, drive data readiness and MLOps practices, mentor teams, produce reusable IP, and represent Microsoft in executive and thought-leadership forums.

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.