Top Senior Level Data & Analytics Jobs in Boston, MA

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.

Build and operate reliable data systems and products supporting clinical operations, drug evaluation, business development, analytics, machine learning, and AI agents. Responsibilities include designing pipelines, canonical data models, data contracts, warehouse transformations, quality and observability practices, governance for regulated data, and incident response. The role partners closely with Product Engineering and Data Science, uses AI-assisted development, contributes to architecture, and mentors other engineers.

Build and operate scalable healthcare data pipelines, dbt models, event-driven services, APIs, and Google Cloud infrastructure. Model and govern multimodal EHR, genomic, and imaging data for AI agents across federated hospital networks. Ensure data quality, lineage, observability, security, HIPAA compliance, and production reliability while contributing to TypeScript application services and agentic workflows.

Lead commercial analytics to diagnose sell-through, pricing, promotions, assortment, and retailer execution. Build causal and incrementality measurement, design test-and-learn, support elasticity and margin analyses, and deliver executive-ready insights and frameworks to optimize revenue, margin, and promotional efficiency across channels and retailers.

Design, build, and own the full data platform (ingestion, modeling, CI/CD, orchestration, observability, and BI). Implement streaming and batch pipelines for IoT data, enable governed self-service, enforce data governance/privacy/security, evolve AI access patterns, and provide technical leadership and mentorship to data engineers and data scientists to deliver production-ready analytics and ML capabilities.

Conduct quantitative research using surveys, experiments, and advanced statistical methods for healthcare and life sciences client engagements. Analyze data, develop insights and visualizations, support rigorous research design, and translate findings into actionable recommendations. Collaborate with consulting teams and global delivery groups while ensuring research quality and communicating complex concepts to technical and non-technical audiences.

Leads predictive media measurement across digital and traditional channels, building marketing mix, attribution, incrementality, and forecasting models. Owns KPIs, dashboards, signal detection, audience and creative effectiveness, brand health, and ambassador evaluation. Translates complex analytics into recommendations that optimize media allocation and marketing performance. Partners with agencies, marketing, brand, data, and engineering teams; mentors junior analysts and drives data-informed decisions across global markets.

Design and deliver scalable data and analytics solutions supporting the end-to-end sales lifecycle. Integrate SAP, Salesforce, and other enterprise sources into cloud data lakes, modernize BI platforms, ensure data quality and governance, drive automation and process improvements, and provide technical leadership and mentorship to teams while partnering with business and IT stakeholders.

Own and govern centralized data assets; design scalable dbt/Snowflake models; model Stripe subscription and payments data; set SQL standards, testing, monitoring and documentation; lead complex BI projects in Hex; partner with product, engineering, operations and GTM to deliver durable analytics solutions.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.