Data Scientist

• Frame ambiguous revenue-cycle problems as tractable modeling problems and own them end to end such as claim and invoice viability scoring, denial and underpayment prediction, revenue/ARR prediction, and work prioritization from hypothesis to a validated, monitored result in production
• Build and validate models with statistical rigor: thoughtful feature design, sound handling of missingness and class imbalance, calibration, honest evaluation against simple baselines, and a clear treatment of uncertainty
• Design, build, and maintain the data pipelines feeding these models integrating with source collections, billing, and EHR systems and internal data stores, and handling data collection, cleansing, field mapping, and payer/plan normalization with a bias toward reliability and data quality
• Establish and own MLOps practice: model monitoring and drift detection (e.g., population stability), calibration and threshold selection, benchmarking against simple baselines, explainability (e.g., SHAP and interpretable coefficients), experiment and version tracking, and model documentation such as model cards
• Partner with subject-matter experts to encode business-rule and heuristic logic alongside statistical models where that produces a more accurate or more defensible result
• Gather and document business requirements for revenue-cycle initiatives, including workflows, decision points, stakeholder objectives, operational constraints, success criteria, data availability, and underlying business assumptions, and maintain traceability as those requirements evolve.

Senior AI Data Scientist

• Design, develop, and maintain predictive models, algorithms, and analytical solutions supporting pricing, underwriting, claims, and other P&C insurance functions
• Apply advanced analytics and machine learning techniques to identify trends, risks, and business opportunities
• Translate business and strategic objectives into clear analytics, reporting, and data requirements
• Analyze large, complex datasets (structured and unstructured) using SQL, Python, Spark, or similar tools
• Document, present, and explain modeling methodologies and results for business partners and regulatory review
• Identify, evaluate, and integrate internal and external data sources to meet business needs
• Collaborate with cross-functional teams to deliver practical, production-ready analytical solutions
• Promote analytics best practices and provide consultation on data-driven initiatives
• Evaluate third-party data, models, and analytical tools in partnership with vendors

Senior Data Scientist

• Lead the development and implementation of a comprehensive biomedical research data governance program across SMPH, including best practices for research data stewardship and lifecycle management.
• Partner with investigators to develop and implement data management and sharing plans for grant applications, ensuring alignment with funding agency requirements and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles.
• Facilitate the discoverability, documentation, and dissemination of research datasets, including support for assigning persistent identifiers (e.g., DOIs), developing metadata standards, and depositing datasets into institutional and national repositories.
• Establish and oversee data quality, data curation, and data stewardship frameworks that promote the accuracy, consistency, completeness, and usability of research data assets.
• Lead the design and implementation of data integration, linkage and secure release methodologies spanning structured, semi-structured, textual, imaging, and other heterogeneous biomedical data sources.
• Develop processes, standards, and tools that improve interoperability and enable the secure integration of research, clinical, public health, and other data domains to support advanced research initiatives while protecting privacy of patients and participants in research studies.
• Guide the development and use of de-identified, limited, and synthetic datasets that support research access while maintaining appropriate safeguards for PHI and compliance with institutional policies.
• Develop and implement methodologies for creating research-ready data products that enable reproducible, secure, scalable, and efficient research workflows.
• Conduct and oversee data quality assessments, validation activities, and data fitness-for-use evaluations to ensure that research datasets meet established governance and scientific standards.
• Serve as a subject matter expert in biomedical data governance, advising investigators, departmental leaders, and research teams on data stewardship, data sharing, metadata standards, and responsible data use.
• Develop and deliver training, educational resources, and consultation services that promote best practices in research data management and governance across SMPH.
• Monitor emerging trends, technologies, and policies related to biomedical data governance, FAIR data practices, research data ecosystems, interoperability standards, and advanced data management methodologies.
• Directly and indirectly supervise staff supporting research data operations, data stewardship programs, data curation activities, and institutional data governance initiatives across SMPH.

Data Governance Program Manager

• Responsible for mining big data, interpreting and analyzing complex data sets
• Develops and deploys predictive models to analyze company performance and improve customer experiences
• Applies statistical techniques and machine learning algorithms to identify trends within Mazda data
• Collaborates closely with data engineers, analysts, and business stakeholders to integrate data-driven solutions

• Work in conjunction with our business development and product teams to develop and implement commercially viable model-based solutions to the healthcare, insurance, life sciences and adjacent markets
• Research, develop, deploy, and maintain traditional AI/ML models following industry best practices
• Work extensively with available GenAI models; construct exciting solutions to internal and external use cases across markets and enhance our internal capabilities through centralized internal tooling and thought leadership
• Coordinate with Product, Business Development, ML Engineering, and IT to bring new and exciting data science products to market, as well as support existing industry leading products
• Help drive best practices and continuous improvement on the data science team; influencing model design and experimentation strategy through planning, audit, peer review, and other coaching

Lead Data Scientist – IntelliScript

• Documents approaches to address research questions and contributes to the establishment of reproducible research methodologies and analysis workflows
• Composes and assembles reproducible workflows and reports to clearly articulate patterns to researchers and/or administrators
• Independently identifies and implements appropriate data science techniques to find data patterns and answer research questions chosen by the lead researcher including data visualization, statistical analysis, machine learning, and data mining
• Organizes and automates project steps for data preparation and analysis
• Prepares data sets for analysis including cleaning/quality assurance, transformations, restructuring, and integration of multiple data sources

Data Scientist II

• Advise clients on end-to-end solutions across data strategy, data management, and data quality.
• Support business development, methodology development, team leadership, and delivery execution for data governance assessments and implementations.
• Design and develop data governance technology solutions across data quality, data dictionaries, business glossaries, data standards, and data policies.
• Define organizational design, governance processes, policies, roles, responsibilities, success metrics, and operating models for client data programs.
• Build roadmaps covering technology, culture, governance, processes, skills, competencies, data quality, analytics, and data management maturity.
• Lead and collaborate with data governance teams to create business cases, resource plans, implementation roadmaps, and enterprise data system assessments.
• Assess current-state environments, identify client pain points and requirements, and help define a practical vision and strategy for becoming a data-driven organization.
• Advise clients as a trusted partner and provide thought leadership on data governance, data management, cloud transformation, AI readiness, and related best practices.
• Manage resources, budgets, project delivery, team development, training, mentoring, and performance assessment.

Data Strategy and Governance Manager

• Own HR data management at Rakuten
• Manage the data governance framework for all Workday employee data
• Lead, develop, and retain a team of HR data management professionals
• Own the HR data workstream for recurring high-stakes programs
• Champion automation and AI adoption within HR Data Management
• Serve as a strategic partner to HR, Payroll, Global Total Rewards, Finance, Legal, IT, and HRIS

Manager, HR Data Management

• Design, develop, and deploy ML solutions ranging from traditional ML applications (classification, clustering, recommendations) to LLM-based systems, including document parsing, data extraction, RAG pipelines, and LLM agents
• Write clean, maintainable, production-quality Python code that integrates smoothly with existing engineering and deployment infrastructure
• Work with large datasets to clean, preprocess, and analyze data, ensuring data quality and integrity
• Implement and optimize algorithms using best practices in machine learning, deep learning, and statistical analysis
• Collaborate with business stakeholders to understand requirements and deliver data-driven solutions that provide actionable insights
• Develop and maintain scalable pipelines and infrastructure for data processing and model training, versioning, deployment, and monitoring
• Evaluate the performance of machine learning models, including LLM-specific evaluation approaches, and tune models for optimal performance
• Communicate findings, insights, and model performance to both technical and non-technical audiences
• Continuously stay updated on the latest trends, technologies, and best practices

Machine Learning Engineer, Data Scientist

• Collaborate closely with Data Engineers to implement strategic engineering proof of concepts and contribute to technology strategy and engineering roadmaps
• Collaborate with clients and internal partners throughout the organization to identify and execute upon opportunities for demonstrating marketing data to drive business solutions
• Audit existing datasets for accuracy, reliability and consistency
• Advise senior team members to identify data models and algorithms to apply to unique client problems
• Increase and optimize user experiences, revenue generation, ad targeting and other business outcomes using predictive modeling and other advanced analysis techniques
• Coordinate with internal and client data engineering teams to implement models and supervise outcomes
• Support digital analytics implementations with broad understanding of tag management, HTML and JavaScript on websites
• Develop and test applications (visualization, routing data, cleaning data, access to data, interacting with data) for analytics pipelines
• Other duties as assigned

• Identifies research problems, designs research methodologies, performs research, prepares data sets and analyses them using data science techniques, presents results for presentation to professional organizations or for scholarly publications
• Collects and analyzes complex research data, conducts experiments and interviews, and documents results according to established policies and procedures under general supervision
• Serves as a unit subject matter expert and liaison to internal and external stakeholders providing advanced level information and representing the interests of a specialized research area
• Identifies research problems and develops complex research methodologies and procedures
• May supervise the day-to-day activities of a research unit as needed
• Writes or assists in developing grant applications and proposals to secure research funding
• Attends and assists with the facilitation of scholarly events and presentations in support of continued professional development and the dissemination of research information
• Conducts literature reviews, prepares reports and materials, and disseminates information to appropriate entities
• Independently identifies and implements appropriate data science techniques to answer research questions including data set preparation, data visualization, statistical analysis, machine learning, reproducible workflows
• Publishes and presents results to help advance research
• Monitors program budget and approves unit expenditures

Clinical Data Scientist

• Analyze and model operational, economic, financial, and organizational data
• Help quantify competitive performance
• Evaluate the impact of operational changes
• Develop data-driven strategies and solutions to complex business problems
• Build modeling frameworks and predictive models for new datasets using advanced analytics
• Explore data to test hypotheses, identify important causes, and answer business questions
• Develop and run statistical programs/tests to analyze complex relationships
• Incorporate industry/competitor insights, stay current on best practices, and propose innovative solutions
• Research and maintain awareness of insurance industry best practices and our strategies

Data Scientist jobs in Wisconsin

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Senior AI Data Scientist

Senior Data Scientist

Data Governance Program Manager

Data Scientist

Lead Data Scientist – IntelliScript

Data Scientist II

Data Strategy and Governance Manager

Manager, HR Data Management

Machine Learning Engineer, Data Scientist

Data Scientist

Clinical Data Scientist

Senior Actuarial Data Scientist – Hybrid