Top Remote Data Science Jobs in Madison, WI

Analyze large-scale educational data to inform product design and efficacy. Develop, evaluate, and maintain statistical and AI models, collaborate with cross-functional teams, communicate findings to stakeholders, and leverage AI tools for analysis and feature engineering while following software development practices.

Develop and deploy machine learning models for demand forecasting and inventory optimization; conduct advanced time-series analysis; collaborate with analytics engineers and data analysts to build end-to-end data products; translate analysis into actionable recommendations for supply chain; promote data science best practices and mentor the team.

Drive product and business analyses across Quora: design and evaluate experiments, build models and visualizations, analyze cross-product data to surface root causes, and partner with cross-functional teams to implement data-informed solutions and frameworks.

Lead the technical development and automation of enrollment forecasting models, collaborating with various departments to enhance decision-making and forecast accuracy.

Lead applied AI projects end-to-end: prototype, validate, and help deploy ML and GenAI solutions (malicious behavior, anomaly/fraud detection, developer experiences). Act as internal AI consultant, design experiments and evaluations, bridge research and production, mentor peers, and ensure data governance and privacy.

Design and deploy ML systems for enterprise search, recommendations, retrieval, and AI agents. Build scalable data/ML pipelines, evaluation frameworks, and metrics. Apply statistics, experimentation, and causal inference to guide product decisions. Collaborate cross-functionally, prototype LLM-based capabilities (embeddings, RAG), and mentor junior team members.

Lead design and deployment of ML systems for enterprise search, retrieval, recommendations, and AI agents. Build scalable data/ML pipelines, evaluation frameworks, and KPIs. Apply statistics, causal inference, experimentation, and analysis to guide product strategy. Prototype and productionize LLM-based features, mentor junior staff, and collaborate cross-functionally.

Design and implement data-centric cybersecurity solutions: develop ML algorithms and automation for security tools; discover, parse, map, and transform legacy RMF artifacts into OSCAL; migrate and analyze security data; reproduce dashboards/workflows in eGRC; collaborate with Professional Services; produce migration assessments and reports.

Contract data scientist to remotely gather, extract, and analyze technical information about AI models, benchmarks, data centers, and companies. Tasks include reading ML papers, tracking leaderboards, processing satellite images/permits, compiling infrastructure details, and producing data insights for research and policy. Part-time (10–30 hrs/week), flexible schedule with occasional Pacific Time meetings.

Deploy and validate NEXUS in customer environments, perform end-to-end data and feature engineering, benchmark against baselines (XGBoost/LightGBM), optimize models for business impact, handle last-mile integrations (VPC/on‑prem/air-gapped), and translate field insights into product improvements while liaising with stakeholders.

As a Senior Data Scientist, you'll optimize search relevance, retrieve content, analyze data, and collaborate with teams to enhance product strategies.

Build, monitor, and maintain production ML models and LLM/agent systems. Analyze complex datasets, develop proofs of concept, collaborate with stakeholders, and align work to sustainability business needs.

Lead design and production deployment of ML models focused on pricing, availability, forecasting, and optimization. Build data infrastructure, governance, testing/monitoring, and dashboards. Partner with executives and cross-functional teams, mentor others, and establish scalable data-science practices and experimentation frameworks.

Build and deploy machine learning models for credit and fraud risk, analyze portfolio performance, monitor models in production, develop risk strategies, collaborate with engineering and cross-functional partners, and conduct high-impact ad-hoc analyses to inform business decisions.

Build and deploy end-to-end ML models for renter acquisition, supply optimization, ranking, personalization, and marketplace optimization. Lead technical projects from problem framing through experimentation, launch, monitoring, and iteration. Apply statistical methods, causal inference, and rigorous A/B testing. Partner cross-functionally, mentor other data scientists, and raise modeling and experimentation standards.

The role involves deploying data science solutions in cybersecurity, collaborating with customers and teams to operationalize models and enhance security insights.

Design, develop, and deploy NLP and LLM-powered models to extract intent, outcomes, and sentiment from calls, chats, and digital interactions. Translate unstructured interactions into actionable insights, build evaluation frameworks, debug and improve model performance, and hand off production-ready artifacts to data engineering to improve call center efficiency and customer experience.

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

Design, validate, and deploy scalable statistical and distributional models for enterprise consumer analytics. Implement rigorous methodologies, convert ad-hoc analyses into version-controlled analytical libraries, and partner with Data Engineering to optimize performance and scale compute workloads (including cloud/Databricks). Deliver reproducible analytics powering dashboards and LLM-driven applications, with a focus on panel and transaction-level consumer data.

Senior individual contributor responsible for leading pet/P&C insurance pricing model development (GLMs and ML), owning end-to-end projects, performing EDA and root-cause analysis, communicating results to stakeholders, and mentoring junior team members.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

Lead a 5–8 person Data Science team to develop and operationalize FWA detection models using SAS and Python on multi‑payer healthcare claims. Oversee model development, data pipelines, quality reviews, stakeholder presentations, and collaboration with SMEs and BI developers. Split role ~40% management, 60% hands-on coding/analysis.

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.

Lead end-to-end development of fraud and risk ML systems: define problems, engineer features, build and evaluate models, collaborate with product and engineering, deploy with ML engineers, and monitor production performance to detect drift and trigger retraining.