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Data Science Research Jobs in Madison, WI (NOW HIRING)

This position will work with CHDR scientists and Data Operations peers to optimize current processes related to a relational database used for scholarly research. The Research Data Engineer will join ...

... data, perform analyses, and write and publish USGS report products, in accordance with fundamental science practices and quality assurance standards. * Perform laboratory studies of fungal pathogens ...

This is an exciting opportunity for someone passionate about plant science and eager to contribute ... Collects data and monitors test results Department: The Wisconsin Crop Innovation Center (WCIC ...

New

You will work on a skilled team of passionate data scientists and meteorologists. Examples of ... Research, recommend, and implement statistical post process correction techniques using proprietary ...

Experience working with these type of data and/or research designs is highly desired. The ideal ... a sports science or high-performance athlete setting * Experience analyzing data using R, Matlab ...

The Enterprise Data Analyst is a firmwide shared-services role within the Firm's Data Science ... Engage in research and study to continuously improve the Firm's reporting and business intelligence ...

The Enterprise Data Analyst is a firmwide shared-services role within the Firm's Data Science ... Engage in research and study to continuously improve the Firm's reporting and business intelligence ...

As a Research Scientist, you'll bridge simulation and experiment to drive groundbreaking progress ... This work will include validation of computational models with experimental data from the currently ...

Showing results 21-40

Data Science Research information

See Madison, WI salary details

$37.8K

$123.7K

$198K

How much do data science research jobs pay per year?

As of Aug 20, 2026, the average yearly pay for data science research in Madison, WI is $123,675.00, according to ZipRecruiter salary data. Most workers in this role earn between $99,300.00 and $137,000.00 per year, depending on experience, location, and employer.

What is data science research?

Data science research involves using scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Researchers in this field work on developing new data analysis techniques, machine learning models, and data-driven solutions to solve complex problems. The work often includes designing experiments, analyzing large datasets, and publishing findings to advance the understanding of data science methodologies.

How does a data science researcher typically collaborate with other departments within an organization?

Data Science Researchers frequently work cross-functionally, collaborating with teams such as engineering, product management, and business analytics. They often translate complex research findings into actionable insights, guiding product development or business strategies. Regular meetings, joint project planning, and code reviews are common, ensuring that research outcomes align with organizational goals. Effective communication and teamwork are key to integrating advanced data solutions into real-world applications.

What are the key skills and qualifications needed to thrive as a data science researcher, and why are they important?

To thrive as a Data Science Researcher, you need strong analytical skills, expertise in statistics and machine learning, and an advanced degree in a quantitative field such as computer science, mathematics, or engineering. Proficiency with programming languages like Python or R, data visualization tools, and experience using platforms such as TensorFlow or PyTorch is typically required. Curiosity, creativity, and clear communication are essential soft skills for designing research questions, interpreting results, and sharing findings with diverse audiences. These skills and qualities are crucial for driving innovative solutions and impactful insights in data-driven environments.

What is the difference between Data Science Research vs Data Analyst?

AspectData Science ResearchData Analyst
CredentialsTypically requires advanced degrees (Master's or PhD) in Data Science, Statistics, or related fieldsOften requires a Bachelor's or Master's degree in Data Analysis, Statistics, or related areas
Work EnvironmentResearch labs, academic institutions, or R&D departments within companiesBusiness environments, corporate offices, or consulting firms
Employer & Industry UsageUniversities, research institutions, tech companies focusing on innovationRetail, finance, healthcare, and other industries focusing on data-driven decision making

Data Science Research focuses on developing new algorithms, models, and theories, often in academic or R&D settings. In contrast, Data Analysts primarily interpret existing data to generate reports and insights for business decisions. Both roles require strong analytical skills but differ in scope, goals, and work environment.

What do data science researchers do?

Data science researchers analyze large datasets to identify patterns, develop models, and generate insights that inform decision-making. They often use programming languages like Python or R, and tools such as machine learning algorithms and statistical methods. Their work typically involves experimentation, data cleaning, and collaboration with other teams to solve complex problems.
Infographic showing various Data Science Research job openings in Madison, WI as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $123,675 per year, or $59.5 per hour.

Other

Re-posted 18 days ago


Job description

Sophisticated Work. In a Great City. Making a Difference.
The State of Wisconsin Investment Board (SWIB) manages more than $178 billion in assets, including those of the fully-funded Wisconsin Retirement System (WRS). SWIB operates at a level more often seen in top-tier global asset managers than in typical public pension funds. SWIB is a home for top talent. Approximately 61 percent of SWIB's investment professionals are Chartered Financial Analyst (CFA) charterholders.
The City of Madison, the state capitol and home of Wisconsin's flagship university, makes regular appearances on lists of best places to live, eat, and play. SWIB offers a modern workspace, hybrid work options, and competitive compensation and benefits.
Serving over 703,000 WRS beneficiaries, SWIB is driven by a clear mission: securing the financial future of those who serve Wisconsin. When you work at SWIB, you know your work matters.
Job Description:
About the Team
Data Services & Engineering Teams at SWIB supports, implements & develops industry-leading systems and platforms to support SWIB's diverse and complex set of investment portfolios and strategies. The team at SWIB strives to be a trusted advisor and partner to the business that is valued as a critical contributor to SWIB's continued growth and success. We effectively leverage technology to derive the maximum value from it and achieve SWIB's business goals. We keep technology aligned with SWIB's future direction and operate SWIB's technology according to industry standards.
Position Overview
Essential activities:
  • Lead the design, development, validation, and deployment of advanced analytics, AI, and machine learning solutions that enable data-driven investment decision-making.
  • Own the technical approach for analytics products end-to-end: problem framing, data requirements, modeling, evaluation, deployment, monitoring, and ongoing iteration.
  • Architect and deploy solutions using GitLab (merge requests, CI/CD pipelines, automated testing, release management) and Terraform (infrastructure as code), establishing strong engineering practices and reproducibility.
  • Design, evaluate, and deploy AI-enabled analytical solutions measuring output quality, detecting hallucinations, and ensuring reliability for decision-making.
  • Implement data quality, validation, and AI evaluation frameworks; define reliability metrics, testing protocols, and monitoring controls ensuring outputs are accurate, traceable, and explainable.
  • Design and develop analytics applications and internal tools, including lightweight front-end interfaces (Power BI, Streamlit, React, or similar tools) to communicate findings and drive adoption; apply UI/UX principles ensuring usability, clarity, and intuitive workflows; craft clear narratives about assumptions, limitations, and implications.
  • Deploy analytics solutions in cloud environments (Azure or AWS), partnering with engineering/security to ensure secure, scalable, cost-aware deployments.
  • Utilize data warehousing technologies (e.g., Snowflake) to support analytics initiatives; collaborate on data modeling and performant query patterns.
  • Communicate complex concepts clearly to technical and non-technical stakeholders; translate investment needs into analytical roadmaps and measurable outcomes.
  • Serve as a liaison across investment teams and partner functions (IT, Operations, Legal, HR, Strategic Planning, etc.) to support change management and adoption of analytics solutions.
  • Act as a senior team contributor: provide design input, conduct code and analysis reviews, share patterns and best practices, and coach junior staff through pairing, feedback, and knowledge sharing.

The ideal candidate:
  • Bachelor's degree required; advanced degree preferred in finance, business, engineering, computer science, computational economics, math, data science, or related discipline.
  • Experience in investment management, quantitative finance, and technology; progress toward or completion of the CFA designation is preferred.
  • 5+ years of experience in data science, analytics, quantitative research, or similar roles.
  • 2+ years of experience designing and deploying AI-enabled analytical solutions measuring output quality, detecting hallucinations, and ensuring reliability for decision-making.
  • Strong proficiency in Python and SQL for advanced analytics, data engineering, and model development in production contexts.
  • Proven experience deploying and operating production code using GitLab, including CI/CD, merge request workflows, automated testing, and release management.
  • Experience using Terraform to provision and manage cloud infrastructure as code.
  • Experience building and deploying ML models using modern techniques (regression, classification, clustering, time series/forecasting) with strong evaluation practices and sound statistical reasoning.
  • Experience implementing data quality frameworks, validation controls, and reliability metrics/processes for analytical outputs and reports.
  • Strong experience with cloud platforms (Azure or AWS) for data storage/processing and deploying analytics solutions; familiarity with security and operational considerations.
  • Experience with data warehousing platforms (e.g., Snowflake) to support scalable analytics initiatives.
  • Excellent communication skills with the ability to influence decisions through clear storytelling and stakeholder partnership.
  • Demonstrated ability to collaborate effectively, coach junior staff, and elevate team standards through reviews, reusable patterns, and documentation.
  • Strong work ethic, attention to detail, and commitment to disciplined delivery (documentation, Jira ticketing, and best practices).

SWIB Offers:
  • Competitive total cash compensation, based on AON (formerly McLagan) industry benchmarks
  • Comprehensive benefits package
  • Educational and training opportunities
  • Tuition reimbursement
  • Challenging work in a professional environment
  • Hybrid work environment

The position requires U.S. work authorization.
Pursuant to our Hybrid Remote Work Policy, all staff have the flexibility to work remotely, but are required to have a weekly presence in our offices, the frequency of which is dependent on their distance from office. Staff are not required to reside locally; however, we offer relocation reimbursement to the Dane County area per our policy.
All SWIB employees are subject to SWIB's Ethics Policy and Personal Trade Approvals Policy. These policies include restrictions on outside business activities and employment and have limits on personal trading. You may request copies of these policies from SWIB's talent acquisition team and any questions can be answered by SWIB's compliance team.