Top Remote Senior Level Data & Analytics Jobs in Boston, MA

The Senior Product Data Analyst will lead product analytics, conduct deep-dive analyses, drive product decisions, communicate insights, and enhance analytics best practices at Mixbook.

The role involves building large, scalable analytics pipelines, defining data models, integrating data, and collaborating on data architecture and strategies.

The Sr. Operations Analytics Manager will enhance billing and account efficiency, analyze complex data, and provide actionable insights to improve financial performance and customer service.

Lead and grow a casino analytics team to measure, diagnose, and optimize Casino revenue performance. Provide strategic analytics support to Revenue Operations, run deep-dive customer and promotion analyses, build scalable monitoring/alerting frameworks, translate insights into actionable recommendations, and manage an analytics roadmap prioritizing short-term wins and long-term AI-enabled strategies.

As a Senior Data Analyst, you will analyze marketing performance, implement multi-touch attribution, and provide insights for strategic decisions.

Lead analytics implementations using Google Analytics and Tag Manager for clients, providing mentorship and managing projects with a focus on enhancing clients' digital strategies.

The Senior Data Engineer II will design and maintain scalable data pipelines, collaborate with teams on machine learning workflows, and ensure data accessibility across systems.

The Market Risk Analyst will monitor SBL lines of credit, assess market risks, manage collateral, develop risk models, and prepare reports.

Lead Pager Health’s enterprise data and reporting strategy, build a centralized data and reporting shared services function, own internal dashboards and client-facing analytics, partner with Product and Engineering on data architecture and governance, mentor data teams, prioritize and deliver data initiatives, and drive continuous improvement in tooling, automation, and data storytelling to support growth, efficiency, and client outcomes.

Lead end-to-end data science initiatives: design, deploy, and monitor production ML models for credit risk, pricing, collections, and fraud. Partner cross-functionally to integrate models, set development standards, mentor team members, and maintain reliable, scalable machine learning pipelines and monitoring systems.