Top Hybrid Data & Analytics Jobs in Boston, MA

Analyzes business performance, trends, competitor filings, and product-line data for personal lines property and specialty insurance. Develops advanced queries and analytical tools, conducts state and countrywide reviews, manages projects and workstreams, and recommends strategic and tactical improvements to products, pricing, technology, and business initiatives. Presents findings to product managers and serves as a subject matter expert on assigned states and products.

Conduct exploratory and hypothesis-driven analysis across disparate data sources to identify business opportunities, risks, and causal relationships. Build metrics, reports, and dashboards to monitor performance and experiments, while partnering with decision-makers to communicate insights and improve data-driven decisions. Contribute to data structure modernization, modeling, and ETL initiatives while managing multiple analytical projects.

As a Decision Analytics Associate Consultant, you will develop statistical models, apply analytic techniques, and communicate results to clients. You will also design custom analyses and guide team projects while mentoring Associates and maintaining client relationships.

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.

Leads a team of analytics professionals and owns client and product analytics strategy, dashboards, reporting, metrics governance, and self-service capabilities. Partners with Product, Engineering, Client Success, and account teams to define trustworthy KPIs, improve data quality, translate complex data into actionable insights, and prioritize analytics initiatives. The role combines people leadership with hands-on BI solution design and supports client-facing analytics offerings.

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.

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.

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.