Top Artificial Intelligence Data & Analytics Jobs in Boston, MA

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.

The Data Platform Engineer II role involves automating data infrastructure, scaling analytics platforms, and ensuring system reliability and security. Key responsibilities include managing CI/CD pipelines, configuring secure networking, and collaborating with Data & Analytics teams to optimize performance.

As a Digital Innovation Co-Op, you'll work on projects involving AI and machine learning to enhance supply chain operations, collaborating with engineers and data scientists to automate workflows and analyze data.

Analyze auction data to identify performance drivers and risks, collaborate with commercial teams, and provide actionable insights to optimize DSP outcomes.

Lead client-facing data solutioning and pre-sales efforts for Consumer verticals, design executable data architectures, support pursuit to close, and provide ~50% hands-on delivery and architectural oversight while ensuring information security and data privacy compliance.

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 Data Specialist at VIA is responsible for building data pipelines, enhancing AI capabilities in data products, and ensuring data quality and integrity.

The Lead Data Analyst will partner with the General Manager to drive data initiatives, lead investigations into metrics, and design analytical frameworks to evaluate product growth, including A/B testing and financial storytelling.

Lead and scale Scribd's Data & Analytics organization across Product Analytics, Data Science, and Analytics Engineering. Build operating model, data governance, experimentation frameworks, AI-native workflows, and infrastructure to accelerate insight delivery. Develop director-level leaders, recruit talent, and embed data-driven decision-making at the executive level to drive growth, retention, and portfolio-wide strategy.

Lead development and deployment of standardized, scalable manufacturing analytics and dashboards (capacity, throughput, WIP, cycle time, yield, OTS). Integrate ERP, SPC, LIMS, and cloud data to enable daily operations cockpits, predictive analysis, and cross-site performance visibility. Partner with Operations, Engineering, Planning, and Digital teams to drive adoption and continuous improvement.