Top Remote Senior Level Data Science Jobs in Boston, MA

The Director of Data Science leads data science and machine learning initiatives for Commerce Media, translating complex problems into scalable solutions, mentoring teams, and communicating insights effectively across functions.

The Senior Data Scientist will analyze healthcare data to identify care gaps and segment key healthcare professionals, guiding strategic medical activities and mentoring other data scientists.

The role involves leading data science initiatives to drive business outcomes, mentoring junior scientists, performing analyses for decision-making, and using advanced technologies for optimization and causal analyses.

The Senior Data Scientist will design and implement data-driven initiatives, optimize product offerings using AI/ML, and collaborate with engineers on scalable solutions.

As a Senior Data Scientist, you will analyze data to produce insights, guide team decisions, improve systems, and influence stakeholders. You'll conduct rigorous experimentation and causal analysis to solve complex business challenges.

The Senior Data Scientist will influence product roadmaps, drive AI/ML capabilities, and perform deep-dive analysis to shape data-informed strategies.

The Staff Data Engineer designs and develops data solutions, builds data pipelines, optimizes AI workflows, and ensures data quality and compliance. They collaborate with cross-functional teams to enhance marketing and analytics capabilities.

The Lead Analyst will shape data-driven talent decisions by conducting predictive analyses, developing dashboards, and mentoring other analysts, impacting workforce strategies across the organization.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

Lead development of a greenfield data product by partnering with customers and product teams to build analytics and models (simulation, Bayesian networks, optimization, ML). Maintain existing analytics, improve predictive churn models, and translate customer needs into production-grade SQL/dbt pipelines, dashboards, and data-driven product features.