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Job Category:
Academic StaffEmployment Type:
RegularJob Profile:
Data Scientist IIIJob Summary:
The Research Data Scientist – Data Quality will serve as a hands-on technical contributor responsible for developing, implementing, and operationalizing data quality capabilities within the Wisconsin Health Data Hub (WHDH) platform. WHDH is a federally funded initiative developing a secure, cloud-native data ecosystem designed to support biomedical research, advanced analytics, and AI-driven discovery using real-world health data.
This role focuses on the practical application of data science, statistical, and computational methods to assess, improve, and monitor the quality of complex clinical, biomedical, and research data. The Specialist will design and implement scalable data quality processes that identify issues related to data accuracy, completeness, consistency, validity, timeliness, and provenance across large and varied datasets. The position will develop automated quality checks, metrics, monitoring capabilities, and analytical approaches that transform complex health data into trusted, research-ready data assets.
The position requires a strong data science and analytical mindset, combined with the ability to translate research and data governance requirements into reliable, scalable solutions operating within a secure research data environment. The Specialist will work closely with data engineers, data scientists, informaticians, solutions architects, security specialists, and research stakeholders to establish repeatable data quality practices that support both current research needs and the long-term growth of the WHDH platform.
Key Responsibilities
Data Quality Assessment & Data Science
- Design, implement, and maintain scalable data quality frameworks for clinical, biomedical, and research datasets.
- Perform data profiling and exploratory analysis to identify patterns, anomalies, missingness, inconsistencies, and other data quality issues.
- Develop statistical and computational methods to assess data accuracy, completeness, consistency, validity, timeliness, and uniqueness.
- Develop and implement data quality rules, thresholds, metrics, and validation criteria appropriate for different research data sources and use cases.
- Analyze data quality trends and identify underlying causes of recurring or systemic data quality issues.
- Apply data science and machine learning techniques, where appropriate, to detect anomalies, identify potential errors, and improve data quality monitoring.
Data Quality Engineering & Platform Integration
- Develop automated data validation and quality-control processes that can be integrated into WHDH data pipelines and workflows.
- Build reusable data quality components, scripts, services, and APIs to support consistent quality assessment across WHDH data assets.
- Integrate data quality checks and monitoring capabilities into cloud-based data processing and analytics environments.
- Collaborate with data engineers to embed quality controls throughout data ingestion, transformation, integration, and delivery processes.
- Develop scalable approaches for monitoring data quality across large, distributed datasets and evolving data pipelines.
- Support the implementation of automated reporting and dashboards that provide visibility into data quality metrics, trends, and remediation status.
Research Data Standardization & Data Readiness
- Collaborate with researchers, informaticians, and subject-matter experts to define data quality requirements and establish fit-for-purpose criteria for research datasets.
- Assess and improve the standardization, normalization, and harmonization of data from multiple clinical, biomedical, and research sources.
- Support the development and implementation of common data standards and controlled vocabularies to improve interoperability and consistency.
- Evaluate datasets for research readiness and identify limitations that may affect downstream statistical analysis, modeling, AI development, or other research applications.
- Support data lineage, provenance, metadata, and documentation practices that enable researchers to understand the origin, transformation, and quality characteristics of WHDH data.
- Develop reproducible methodologies and workflows for preparing high-quality datasets for research and analytical use.
Data Quality Governance & Collaboration
- Translate institutional, research, and stakeholder requirements into practical data quality standards, controls, and processes.
- Collaborate with data governance leadership to establish data quality policies, standards, definitions, and operating procedures.
- Work closely with security and compliance teams to ensure data quality processes appropriately protect sensitive healthcare and research data.
- Document data quality rules, methodologies, findings, limitations, and remediation processes to support transparency and reproducibility.
- Participate in the development of data quality governance frameworks that establish accountability for data quality across WHDH data products and sources.
- Communicate data quality findings and recommendations to both technical and non-technical stakeholders, including researchers and academic and industry partners.
Continuous Improvement & Technology Transfer
- Evaluate and implement emerging data science, data quality, and analytical technologies that improve the reliability and usability of WHDH data assets.
- Develop innovative approaches for automated data quality assessment, monitoring, anomaly detection, and issue resolution.
- Establish reusable and scalable data quality methodologies that can be applied across research projects, data domains, and partner organizations.
- Contribute to the development of best practices for reproducible and sustainable research data management.
- Support the technology transfer goals of the WHDH initiative by developing documented, modular, and deployable data quality tools and processes.
- Help establish operational practices that enable WHDH data quality capabilities to be sustained and adopted across academic, healthcare, and industry research environments.
It is anticipated that this position will be remote and requires work be performed at an offsite, non-campus work location. The selected candidate for this position must reside within the State of Wisconsin or relocate to the State within a reasonable time frame from the start date of the position.
Key Job Responsibilities:
- Develops and implements informatics pipelines for the processing, integration, and harmonization of heterogeneous data sources
- Develops predictive models using retrospective real-world data to estimate disease risk, progression, and treatment effectiveness, while addressing bias and fairness. Designs and executes rigorous hypothesis testing on observational datasets to validate research findings
- Serves as an institutional subject matter expert and liaison to key internal and external stakeholders regarding data science best practices and methodologies and represents the interests of data science
- Prepares data sets for analysis including cleaning/quality assurance, transformations, restructuring, and integration of multiple data sources
- Identifies and implements or guides others in implementing 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
- Documents approaches to address research questions and contributes to the establishment of reproducible research methodologies and analysis workflows
- Organizes and automates project steps for data preparation and analysis
- Composes and assembles reproducible workflows and reports to clearly articulate patterns to researchers and/or administrators
Department:
School of Medicine and Public Health, Office of Informatics, Wisconsin Health Data Hub.
The Wisconsin Health Data Hub (WHDH) is a grant-funded initiative within the Office of Informatics Division at the University of Wisconsin–Madison School of Medicine and Public Health. WHDH brings together a multidisciplinary team of technologists responsible for designing, implementing, and operating a secure data enclave that supports the responsible use of real-world health data for biomedical research.
The WHDH team develops and manages a scalable data platform that enables researchers to efficiently access, integrate, and analyze large-scale health datasets from participating health systems. By providing advanced data services, governance frameworks, and analytical capabilities, WHDH accelerates the research lifecycle—from project conception and data acquisition to analysis and discovery—while ensuring compliance with applicable regulatory, privacy, and security requirements.
Compensation:
The starting salary for the position is $90,000 annually; but is negotiable based on experience and qualifications.
Employees in this position can expect to receive benefits such as generous vacation, holidays, and sick leave; competitive insurances and savings accounts; retirement benefits. For more information, refer to the campus benefits webpage.
SMPH Faculty /Academic Staff Benefits Flyer 2026
Required Qualifications:
- 3 to 5 years’ of professional experience in data science, research data management, data quality, data analytics, or a related technical field.
- Strong programming experience in Python, R, SQL with demonstrated ability to analyze and transform complex datasets.
- Experience developing and implementing data quality processes, including data profiling, validation, cleansing, standardization, anomaly detection, and quality monitoring.
- Experience working with large-scale datasets and data processing pipelines, including data integration across multiple structured and/or unstructured data sources.
- Demonstrated ability to develop automated data quality rules, metrics, validation frameworks, and monitoring processes.
- Strong analytical and problem-solving skills, with the ability to identify patterns, inconsistencies, anomalies, and root causes of data quality issues.
- Experience communicating complex data quality findings and recommendations to technical and non-technical stakeholders.
Preferred Qualifications:
- Experience working with clinical, healthcare, or research data in an academic, healthcare, government, or industry environment.
- Familiarity with healthcare data models and standards such as OMOP, FHIR, UMLS, ICD, SNOMED CT, LOINC, or RxNorm.
- Experience developing data quality solutions within cloud environments such as AWS, Microsoft Azure Platform.
- Experience with large-scale data processing technologies and platforms such as Azure Synapse, MS Fabric or ADF.
- Experience developing data quality dashboards, reporting tools, or visualization solutions to communicate data quality metrics and trends.
- Experience working with electronic health record (EHR) data (Epic Clarity & Caboodle), or other complex health data environments.
- Experience establishing reproducible data science workflows and documenting data transformations, quality rules, methodologies, and limitations.
Education:
PhD or terminal degree preferred; Focus in Data Science, Computer Science, Statistics, Biostatistics, Biomedical Informatics, Information Systems, or a related technical field preferred.
How to Apply:
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To apply for this position, select either “I am a current employee” or “I am not a current employee” under Apply Now. You will then be prompted to upload your application materials.
Important: The application has only one attachment field. Upload the following documents in that field, either as a single combined file or as multiple files in the same upload area.
• Cover letter (required)
• Resume (required)
Your cover letter should address how your training and experience aligns with the required and preferred qualifications listed above. Application reviewers will rely on these written materials to determine which applicants move forward in the process. References will be requested from final candidates. All applicants will be notified once the search concludes and a candidate is selected
University sponsorship is not available for this position, including transfers of sponsorship and TN visas. The selected applicant will be responsible for ensuring their continuous eligibility to work in the United States (i.e. a citizen or national of the United States, a lawful permanent resident, a foreign national authorized to work in the United States without the need of an employer sponsorship) on or before the effective date of appointment. This position is an ongoing position that will require continuous work eligibility. If you are selected for this position you must provide proof of work authorization and eligibility to work.
The department will not be able to support a request for a J-1 waiver. If you choose to pursue a waiver and apply for our position, neither the UW nor UWMF will reimburse you for your legal or waiver fees.
Contact Information:
Cody Roekle, croekle@wisc.edu, 608-263-7676
Relay Access (WTRS): 7-1-1. See RELAY_SERVICE for further information.
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Diversity is a source of strength, creativity, and innovation for UW-Madison. We value the contributions of each person and respect the profound ways their identity, culture, background, experience, status, abilities, and opinion enrich the university community. We commit ourselves to the pursuit of excellence in teaching, research, outreach, and diversity as inextricably linked goals.
The University of Wisconsin-Madison fulfills its public mission by creating a welcoming and inclusive community for people from every background - people who as students, faculty, and staff serve Wisconsin and the world.
The University of Wisconsin-Madison is an Equal Opportunity Employer.
Qualified applicants will receive consideration for employment without regard to, including but not limited to, race, color, religion, sex, sexual orientation, national origin, age, pregnancy, disability, or status as a protected veteran and other bases as defined by federal regulations and UW System policies. We promote excellence by acknowledging skills and expertise from all backgrounds and encourage all qualified individuals to apply. For more information regarding applicant and employee rights and to view federal and state required postings, visit the Human Resources Workplace Poster website.
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3-5 years professional experience in data science or related field; strong Python, R, SQL; experience with data quality processes, large-scale datasets, and automated validation; ability to communicate findings; must reside in Wisconsin.
Key Responsibilities
- developing data quality frameworks
- performing data profiling
- building automated validation processes
Skills & Tools
Python, R, SQL, AWS, Microsoft Azure, Azure Synapse, MS Fabric, ADF, Epic Clarity, Caboodle, OMOP, FHIR, UMLS, ICD, SNOMED CT, LOINC, RxNorm
Job Details
- Category: Data and Analytics
- Seniority: Mid Level
- Commitment: Full Time
- Workplace: Remote — Wisconsin, United States
- Salary: USD 90,000 – 90,000 / year
- Languages: English
Benefits
- Generous paid time off
- Retirement plan
About University of Wisconsin-Madison
A public research university operating a secure data hub for biomedical research. — Industry: Education