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About This Role
- JR10015399
- Remote
- Madison, Wisconsin
- Research
- Full time
- Closing at: Oct 25 2026 - 11:00pm CDT
Job Category:
Academic Staff Employment Type:
Regular Job Profile:
Data Scientist III Job 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.
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:
For the best experience completing your application, we recommend using Chrome or Firefox as your web browser.
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, [email protected], 608-263-7676
Relay Access (WTRS): 7-1-1. See RELAY_SERVICE for further information.
Institutional Statement on Diversity:
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.
To request a disability or pregnancy-related accommodation for any step in the hiring process (e.g., application, interview, pre-employment testing, etc.), please contact the Divisional Disability Representative (DDR) in the division you are applying to. Please make your request as soon as possible to help the university respond most effectively to you.
Employment may require a criminal background check. It may also require your references to answer questions regarding misconduct, including sexual violence and sexual harassment.
The University of Wisconsin System will not reveal the identities of applicants who request confidentiality in writing, except that the identity of the successful candidate will be released. See Wis. Stat. sec. 19.36(7).
The Annual Security and Fire Safety Report contains current campus safety and disciplinary policies, crime statistics for the previous 3 calendar years, and on-campus student housing fire safety policies and fire statistics for the previous 3 calendar years. UW-Madison will provide a paper copy upon request; please contact the University of Wisconsin Police Department.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At University of Wisconsin–Madison, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,503 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
University of Wisconsin–Madison AI Hiring
University of Wisconsin–Madison has 1 open AI role right now. They're hiring across Data Scientist. Based in Madison, WI, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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