Research Data Scientist - Data Quality

Universities of Wisconsin•Madison, WI

1d•Remote

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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.
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