Top Data Science Jobs in Madison, WI

Build and own measurement systems, MMM, incrementality, and experimentation frameworks to quantify marketing incrementality, optimize budget allocation, forecast LTV/CAC, and operationalize production-grade models and metrics to drive growth, retention, and long-term enterprise value.

The role involves leveraging advanced analytics and machine learning to assess and mitigate credit and fraud risks, developing scalable modeling solutions while collaborating with stakeholders.

The role involves designing, building, and deploying machine learning models, collaborating with teams to address business problems, and mentoring other data scientists.

Lead design and ownership of advanced fraud detection models and monitoring. Apply adversarial threat modeling to select architectures (tree-based, deep, graph), debug production failures, run experiments, mentor engineers, and build AI-assisted workflows that scale domain expertise while resisting manipulation.

The role involves preparing traffic data, developing and deploying machine learning models on Google Vertex AI, and collaborating with teams to improve project workflows.

The Data Scientist II will oversee data management, design study protocols, train staff, lead report development, and manage client activities using project frameworks.

As a Data Scientist II, you will analyze complex data, develop statistical models, and create data visualizations to support internal clients and enhance analytics capabilities. You will collaborate with technical leads and stakeholders, ensuring data quality and accuracy in deliverables.

As a Data Scientist, you'll build production-grade ML systems for HVAC efficiency, working on scaling models, database optimization, and real-time monitoring systems.

As a Data Scientist, you will develop advanced fraud detection models, collaborate with teams, analyze data for insights, optimize models, and ensure compliance with data privacy.

As a Business Intelligence Data Scientist, you will develop dashboards, build statistical models, and analyze product features to enhance user insights.

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.

Lead development of analytical pipelines and statistical models using R and Python for clinical data processing and reporting. Mentor peers and ensure compliance with regulatory standards.

The Staff Data Scientist will oversee Foodsmart's member growth analytics, focusing on marketing and product funnel optimization, experimentation, attribution modeling, and enhancing self-service analytic tools while collaborating closely with leadership and cross-functional teams.

This role involves analyzing EHR and insurance claims data to define clinical outcomes, developing AI models, and collaborating with technical experts for manuscript preparation.

The Data Scientist II will work with sports tracking data to build models and analyses for NHL and NBA organizations, focusing on data transformation and metrics development.

The Data Scientist will build and operate models to improve logistics operations, focus on driver behavior, and analyze data for better delivery efficiency.

The Data Scientist II will develop and improve machine learning models for document verification, engaging in model diagnostics and optimization, and collaborating with cross-functional teams to meet performance requirements.

The Applied Data Scientist III will develop scalable AI solutions, analyze data, and translate technical concepts for stakeholders while advocating for AI adoption.

The Data Scientist at GenLogs will build machine-learning systems, analyze large datasets, and collaborate with engineering and product teams to deliver advanced logistics intelligence solutions.

As the Staff Data Scientist, you will lead the data science function, driving strategy, mentoring peers, and shaping product direction through advanced analytics and experimentation.

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

Develop, deploy, and operationalize analytical and ML/AI models for cybersecurity use cases. Collaborate with product and engineering teams to enable and migrate production workflows, work with databases and data architectures, create visualizations and reports, adopt new cloud orchestration and data virtualization technologies, and mentor junior cyber data scientists.

Conduct original economic research using Stripe transaction and public datasets; apply econometric and causal methods; build reproducible data pipelines; create economic indicators; translate findings for product, policy, and external audiences; publish and present research; collaborate cross-functionally and with external partners.

The Data Scientist will work with the team to build and optimize models, interpret data, and support product development using massive data sets for advertising effectiveness.

This role involves partnering with sales to design data-driven solutions, engaging with customers, and supporting pre-sales engagements.