Top Data & Analytics Jobs in Boston, MA

Conduct healthcare product analysis, build dashboards and recurring reports, monitor KPIs, support A/B testing, validate data, and identify trends affecting member engagement and health outcomes. Partner with product, engineering, design, data science, and marketing teams to deliver actionable insights, improve data quality, and support healthcare product strategy. The role also contributes to documentation, segmentation analysis, and repeatable analytics processes.

Analyze AML and sanctions data to identify trends, exceptions, and patterns. Build dashboards, recurring reports, and visualizations; clean and reconcile data; automate tasks using Python and analytics tools; translate business questions into analytical outputs; document methodologies; and collaborate with project teams supporting banking clients and regulatory standards.

Build predictive models forecasting customer behavior, analyze retention and gameplay trends, and create automated tools, analyses, and dashboards. Translate model outputs into actionable recommendations that guide marketing investments, promotional strategies, customer valuation, and retention. Partner with Marketing, Finance, Data Science, and Data Engineering teams while using Databricks, Airflow, Bitbucket, SQL, Snowflake, and dashboarding tools to support revenue growth and customer insights.

Leads Liberty Mutual’s Global Legal & Compliance Data Office and owns its data and AI strategy, governance, quality, architecture, enablement, and investment roadmap. The role integrates product management, data science, knowledge management, and information services while ensuring regulatory compliance, data readiness, and alignment with enterprise strategy. It drives data maturity, change leadership, cross-functional collaboration, and adoption of data-driven practices across legal and compliance teams.

Leads Customer Success analytics strategy, executive business reviews, customer experience and voice-of-customer insights, enablement impact measurement, and data foundation development. Manages two analysts, partners with data engineering and data science, applies statistical and experimental methods, analyzes structured and unstructured customer data, and uses AI tooling to improve self-service and recurring analytics. Translates ambiguous business questions into actionable recommendations for senior leaders.

Own high-impact revenue strategy analyses from problem framing through recommendations and decision support. Analyze marketplace performance, lead economics, monetization, retention, product adoption, and operating models using SQL, Python, financial modeling, and cross-functional data. Advise senior leaders, create executive- and board-ready materials, improve metric reliability, build reusable analytical frameworks, and influence decisions across Revenue, Product, Finance, and Operations.

Designs, builds, and maintains secure data pipelines, ETL processes, data warehouses, and quality controls. Collects, validates, cleanses, and analyzes data; develops SQL queries, Tableau dashboards, and AWS-based solutions; investigates data integrity issues; documents standards; and partners with stakeholders to deliver technical solutions using Agile and DevOps practices.

The Data Scientist II will lead and execute health economic outcomes research projects, collaborate with stakeholders, and build analytical infrastructure while adhering to regulations.

Manage the development, implementation, governance, and adoption of enterprise ontologies and optimized data models. Partner with Finance, Technology, Product, Machine Learning, and enterprise teams to support ERP replacement, semantic technology integration, data standardization, and data harmonization. Own the ontology roadmap, establish standards using W3C technologies, communicate strategic vision, manage and mentor teams, and advocate for semantic technologies across the organization.

Designs conceptual, logical, and physical data models for R&D research and clinical domains. Translates scientific and business requirements into reusable, scalable, AI-ready data structures that support analytics, machine learning, data integration, and standardized workflows. Partners with domain experts, data engineers, architects, data scientists, and governance teams to apply enterprise standards, schemas, ontologies, metadata, quality rules, and lineage. Promotes shared data assets and ensures models are implementable, interoperable, and aligned with evolving R&D needs.