Top Hybrid Data Analysis Jobs in Boston, MA

Analyzes operational, customer survey, qualitative, and unconventional data using Snowflake and SAS to identify retention drivers and customer pain points. Synthesizes findings into recommendations, improves reporting, provides operational consulting, and presents insights to senior leadership. Requires strong statistical, analytical, communication, and cross-functional collaboration skills.

Analyzes complex regulatory data requests across insurance, underwriting, compliance, and business data sources. Translates ambiguous requirements into precise data rules, delivers defensible analysis, collaborates cross-functionally, and develops reporting for leadership. Documents data sources and business logic, improves request workflows through automation and AI, and leads projects from scoping through delivery.

Supports Wealth Management and the MMLIS Advisor Loan Program through financial analysis, deal and loan modeling, profitability and sensitivity analysis, dashboards, data visualization, and business insights. Uses SQL, Python, Excel, and BI tools to analyze complex datasets, automate workflows, identify trends, and develop KPIs. Partners with business and technology teams to improve analytics processes, support AI and automation initiatives, and communicate recommendations to leaders.

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.

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.

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.

Performs data analysis, cleans and validates data, identifies trends and anomalies, builds dashboards and reports, and supports KPI development. Collaborates with engineers and business stakeholders to improve data quality, pipelines, reporting requirements, and analytical processes. Leads data-driven change management, communicates insights, shares best practices, and may support urgent IT incidents.

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.

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.

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

Develops data and investment applications for a cloud-based analytics platform. Translates business requirements into technical solutions, prototypes, integrations, tests, reports, and system designs. Onboards and validates structured and unstructured datasets, investigates data exceptions, and collaborates with investment teams, engineers, vendors, QA, and IT. Creates project documentation and supports automated testing using SQL and Python, with a focus on private-market investment decision-making.