Top Hybrid Data & Analytics Jobs in Boston, MA

Develop advanced statistical models and custom analyses to solve client business challenges. Use R, Tableau, SAS, Visual Basic, VBA, Excel, and related technologies to analyze data, guide decisions, and create client deliverables. Present findings, manage client relationships, provide project updates, implement solutions, and mentor Associates. The role requires strong quantitative education, programming or analytics experience, communication skills, teamwork, and availability for full-time work in 2026.

Leads applied AI and health data architecture initiatives for payer clients. Designs cloud-native platforms, pipelines, data models, integrations, and modernization roadmaps across AWS, Azure, Databricks, and Snowflake. Oversees data quality, governance, anonymization, performance optimization, and legacy migrations while advising stakeholders on AI-ready architectures. Manages multi-team delivery, resolves technical issues, presents solution tradeoffs, and coaches practitioners.

Leads strategy, architecture, governance, sourcing, and delivery of training and calibration data for data science and pricing teams. Oversees data engineering and pipeline modernization, vendor and warehouse integration, source-system changes, data lineage, and model-ready dataset delivery. Manages the Modeling Data Solutions organization, partners with enterprise data stakeholders, and guides teams supporting business-line data needs.

Designs and maintains reusable data ingestion and transformation components, automated quality and governance checks, reliable pipelines, monitoring, deployment automation, cloud integrations, containers, and infrastructure as code. Partners with data consumers on usable models and datasets, documents standards and runbooks, supports Agile improvement, and responds to critical IT incidents. Requires 5+ years of data engineering experience, orchestration, distributed processing, storage, scripting, automation, and version control expertise.

Analyze how customers use Hi Marley to guide product, customer success, and company decisions. Define feature success metrics, evaluate adoption funnels, measure pilots and utilization, present customer-facing insights, and establish trusted self-service reporting. Partner with Data Engineering to translate analyses into governed data models and communicate findings clearly to executives and customers.

Leads the Sales Analytics team supporting Sales and Customer Success across three markets. Oversees business intelligence tools, dashboards, sales data modeling, retention and revenue analysis, and go-to-market measurement. Modernizes data workflows using Snowflake, dbt, Git, and tested models; develops Gong transcript use cases with LLM-based extraction; stewards Salesforce data; and partners with Sales, Revenue Operations, Engineering, and leadership to drive data-informed decisions.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Responsibilities include data modeling, architecture, distributed processing, relational and NoSQL databases, data security, observability, and real-time workloads. The role collaborates with product and engineering teams, establishes reusable engineering patterns, communicates technical concepts, and mentors data professionals while delivering resilient solutions.

Own commercial analytics for direct-to-consumer and marketplace channels, analyzing pricing, promotions, conversion funnels, merchandising, product mix, customer behavior, and channel economics. Build scalable reporting tools, support forecasting and planning, and translate transactional and behavioral data into recommendations for digital commerce leadership. Partner with Marketing Analytics and Analytics Engineering to connect acquisition trends with commercial outcomes and ensure trusted data.

Develop market intelligence, forecasting, statistical models, scenario analyses, and automated reporting for supply chain decisions. Integrate internal and external datasets using Python, R, SQL, and other tools; build dashboards; support data governance; and translate analytical findings into recommendations for procurement, planning, and executive stakeholders.

Build AI agents and data acquisition systems, evaluate dataset quality, define quality KPIs, and improve data processes. Partner with product teams to deploy data into customer-facing products and acquire datasets from third parties, including coordinating vendors and contracts. The role requires organizing multiple projects, structuring analyses, communicating tradeoffs, and using data to support investment decisions and customer outcomes.