Top Hybrid Data & Analytics Jobs in Boston, MA

Supports PwC Deals diligence analytics projects through data gathering, analysis, research, visualization, data mining, algorithm development, and preparation of reports and presentations. The intern will use Alteryx and Power BI, integrate data from multiple sources, develop data-driven insights for clients, and learn from advisory professionals while contributing to cloud, data architecture, and AI-related initiatives.

Conduct quantitative and qualitative research, including data cleaning, statistical analysis, subgroup comparisons, regression, clustering, and significance testing. Manage online research communities for teens, parents, and counselors, overseeing recruitment, engagement, incentives, research programming, and member experience. Translate findings into dashboards, visualizations, reports, and presentations for nontechnical audiences. Collaborate cross-functionally to shape research agendas, deliver rapid insights, and advance data-driven initiatives.

Leads data stewardship and transformation for the Personal Lines Product, Pricing, and Underwriting domain. Develops data roadmaps, translates strategy into actionable epics, guides modernization to a unified insurance platform, governs data policies and compliance, and coordinates stakeholders across business and technical teams. The role requires expertise in insurance data, data modeling, warehousing, quote and policy lifecycles, premium and loss data, and technologies including Kafka, Guidewire PolicyCenter, Hadoop, and NoSQL.

Own and improve e-commerce fulfillment analytics capabilities, including solution design, coding, deployment, data pipelines, validation, modeling, reporting, and visualization. Analyze internal and external data to generate insights for network strategy, capacity, investment, sales capture, and cost management. Partner with data, fulfillment, brand, and executive teams while managing roadmaps, vendors, documentation, testing, and continuous improvement.

Leads WHOOP’s data governance function, defining strategy, operating models, ownership, data contracts, event taxonomy, cataloging, access policies, quality standards, and privacy compliance. Builds and coaches a senior team while partnering across engineering, analytics, product, legal, GRC, and InfoSec. Establishes governance frameworks, automated quality monitoring, access controls, compliance practices, and metrics, while embedding standards into technical workflows and advancing AI-powered governance.

Develop, implement, govern, and integrate enterprise ontologies and data models. Harmonize linked data, establish semantic and data-standardization guidelines, support knowledge organization and machine-learning integrations, and advocate semantic technology across Finance and Enterprise teams. Use W3C ontology standards and specialized tools to test, troubleshoot, and maintain models while collaborating with Technology, Product, and Machine Learning stakeholders.

Analyze and visualize large customer datasets, build production-grade data models and scalable data products, automate manual analytical workflows, and create AI-assisted, conversational, and self-service analytics experiences. Translate ambiguous stakeholder needs into actionable solutions while collaborating across teams and experimenting with generative AI and modern data technologies.

Design and execute advanced statistical and data analyses for client projects, using tools such as R, Tableau, SAS, VBA, and Excel. Translate data into actionable insights, communicate findings to clients and internal teams, collaborate on solution implementation, and take ownership of project deliverables.

Leads the data engineering team, including hiring, coaching, technical direction, platform architecture, data pipelines, Snowflake warehousing, governance, quality, observability, and incident support. Partners with Data Science, Analytics, Clinical Operations, Biostatistics, and Business Development to deliver trusted data products for clinical operations, asset evaluation, reporting, and machine learning. Oversees budgeting, resourcing, documentation, technical programs, and responsible use of AI engineering tools.

Design, build, and operate high-throughput data pipelines supporting billing and cost attribution. Drive Spark optimization, latency reduction, sharding, cross-datacenter reliability, and new billing capabilities. Investigate billing accuracy issues with Finance and Product teams, participate in on-call rotation, and improve engineering standards and operational excellence.