Top Senior Analytics Jobs in Madison, WI

Lead credit underwriting, pricing, verification, and portfolio performance for personal loans. Use data and ML/AI to design automated decisioning, manage credit/fraud risk, forecast losses, and present strategies to leadership and investors. Collaborate cross-functionally and build vendor/alternative-data strategies to drive profitable growth while keeping losses within risk appetite.

Lead development and scaling of financial analytics: build data models, dashboards, and reporting; translate financial data into executive insights; embed predictive analytics; partner with Finance, IT, and business units to improve data quality, governance, forecasting, and automation.

Design, build, and support SAP Analytics Cloud dashboards, planning models, and executive reporting. Integrate SAC with SAP and non‑SAP sources, develop KPIs and forecasting models, run testing and UAT, troubleshoot issues, and support production deployments and stakeholder workshops to deliver actionable analytics for FP&A and operational teams.

Design, build, and deliver a global analytics platform: manage data collection and pipelines, develop visualizations and reports, create semantic models, tune performance, collaborate with IT and business stakeholders, and implement governance for analytics products.

Lead the analytics team for the third-party agent business, driving business strategy, product analytics, and AI-powered insights while fostering cross-functional partnerships.

Develop, enhance, and support Synchrony’s metadata management and cataloging platform (Ab Initio Metadata Hub). Collaborate with governance, engineering, and analytics stakeholders to ensure metadata quality, lineage, and discoverability; design solutions, implement platform changes, test, document runbooks, and enable cloud and AI/ML metadata use cases.

Client-facing Analytics Manager who architects large-scale digital analytics (GA4/Adobe), audits implementations, builds measurement frameworks, and leads GEO (Generative Engine Optimization) test & learn programs. Uses AI tools (Claude) to automate workflows, mentor teammates, manage projects, and translate data into strategic client recommendations.

Lead and scale companywide analytics for a DTC marketing business: own marketing measurement, financial forecasting, retention/LTV modeling, data architecture, reporting, and the analytics team. Set analytics strategy, oversee data engineering roadmap, build dashboards, apply statistical and ML methods, and translate insights into executive-level decisions.

Lead revenue forecasting, planning, and executive reporting across new, renewal, and expansion revenue. Manage and grow a revenue insights team while owning pre-sales analytics, forecast methodology, capacity and quota planning, and performance narratives. Build pipeline and forecast reporting, track conversion/velocity metrics, surface risks early, and apply AI to improve forecasting, anomaly detection, and automated insights.

Build and operate the core BI platform: own ingestion, integration, modular dbt pipelines, and enterprise data models. Establish data quality, testing, governance, observability, and CI/CD. Monitor platform health, certify pipelines and data products, and mentor analytics engineers to enable trusted, self-service analytics and AI-augmented workflows.

Lead analytics and BI for Digital Product Operations: define product and adoption metrics, build dashboards, ensure data governance and quality, measure ProdOps effectiveness, coach PM/POs, diagnose adoption gaps, and drive interventions that improve product planning and delivery.

Lead demand forecasting and performance analytics for eCommerce: build forecasting models, run scenario and promotional analyses, produce executive reporting, drive cross-functional predictive initiatives, and deliver market and competitive insights to inform planning and resource allocation.

Lead analytics for the Credit Card portfolio, partnering with Product, Engineering, Credit Risk, Fraud, and Marketing to deliver data-driven strategy. Perform ad-hoc and cohort analyses, design A/B tests, manage BI reports, drive portfolio growth and risk insights, and build scalable data solutions with Data Engineering. Influence executives through clear recommendations and leverage AI tools to accelerate analytics and self-service.

Serve as embedded BI lead for a divisional sales book, delivering actionable insights from syndicated and commercial data, leading opportunity analysis, training field teams, supporting Power BI reporting, and translating complex data into executive presentations to drive sales performance and planning.

Lead end-to-end category insights for strategic account chains using syndicated, retailer POS, and internal data. Develop analyses, reporting, and recommendations to drive sales and profit, partner with sales and BI, support category reviews and business planning, and present insights to stakeholders.

Serve as embedded BI partner for an assigned division, delivering actionable commercial insights from syndicated and internal data, leading opportunity analyses, training field teams, supporting Power BI reporting, and translating complex data into executive presentations to drive sales performance and program ROI.

Lead process mapping, analysis, and redesign to improve efficiency and outcomes. Partner with stakeholders to gather requirements, analyze data (SQL, Excel, Power BI), define KPIs, apply Lean/Six Sigma methods, and support automation and digital transformation initiatives to drive growth, profitability, and loss reduction.

Design, build, and maintain scalable Databricks-based data pipelines and ELT workflows; develop and document dbt models; define data modeling methodology; ensure data quality, reliability, and scalability; collaborate with clients and cross-functional teams; provide technical guidance and deliver improvements to clients' data infrastructure and analytics capabilities.

The Director of Product Analytics leads the product analytics strategy, develops a team of analysts, drives insights for product decisions, and oversees experimentation across products to enhance growth outcomes.

Lead product analytics efforts to define KPIs, build measurement frameworks, analyze customer behavior, and generate insights that influence product and engineering decisions. Partner with Product, Engineering, and Data teams to design event tracking, run experiments, quantify opportunity sizes, and communicate recommendations through clear narratives and visualizations to drive product adoption, retention, and business outcomes.

Lead BI developer who owns reporting infrastructure end-to-end: SQL-based data extraction from messy sources, LookML/BI model development, dashboard standardization, stakeholder partnership, and performance marketing optimization to drive actionable insights and business outcomes.

Lead a team to analyze sales data, build and maintain Power BI and Excel reporting, leverage AI and automation for pipeline and lead scoring, align cross-functional GTM strategies, ensure data quality, and deliver actionable, executive-ready insights to drive sales performance and quota attainment.

The Senior Consultant will lead the design and implementation of data solutions using Microsoft Fabric, Power BI, and Databricks to enhance analytics capabilities for clients. They will engage with stakeholders to define data strategies and deliver high-quality consulting services across multiple projects.

Build scalable GTM and product analytics to inform revenue, retention, and growth decisions. Partner with Sales, Customer Success, Finance, Product, and Data Engineering to deliver dashboards, models, and executive data stories; validate data quality; measure ROI; and enable self-service reporting across the lead-to-quote-to-cash journey.

Design, build, and operationalize scalable analytics engineering solutions on Databricks and modern cloud platforms. Implement metrics-as-code and semantic layers, create automated pipelines, enable product measurement and experimentation, define cohorts and KPIs, improve data quality and governance, and lead engineering best practices and stakeholder delivery for data products.