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Volunteer Data Scientist Machine Learning Jobs in Madison, WI

Urus

Dairy Data Scientist

Watertown, WI · On-site

Are you passionate about dairy science and interested in using data to improve herd performance ... Experience with machine learning or predictive modeling. * Experience with Power BI, Tableau, or ...

Wisconsin

Data Scientist II

Madison, WI · On-site +1

$80K/yr

The Data Scientist II will design, develop, and maintain data pipelines, data models ... machine learning, and data mining * Documents approaches to address research questions and ...

Varsity Tutors

Data Science Tutor

Madison, WI · Remote

$18 - $40/hr

What We Look For In a Data Science Tutor * Advanced Subject Mastery ... Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ...

Insperity

Artificial Intelligence Engineer III

Madison, WI · On-site

$58 - $77.75/hr

... paid volunteer hours and much more. We take care of our people so that you can do your best work ... This role applies advanced software, data science, machine learning, and LLM engineering expertise ...

Insperity

Artificial Intelligence Engineer III

Madison, WI

$58 - $77.75/hr

... paid volunteer hours and much more. We take care of our people so that you can do your best work ... This role applies advanced software, data science, machine learning, and LLM engineering expertise ...

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All JobsVolunteer Data Scientist Machine Learning JobsVolunteer Data Scientist Machine Learning Jobs in Madison, WI

Volunteer Data Scientist Machine Learning information

See Madison, WI salary details

$37.8K

$123.7K

$198K

How much do volunteer data scientist machine learning jobs pay per year?

As of Sep 3, 2026, the average yearly pay for volunteer data scientist machine learning 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 does a volunteer data scientist machine learning do?

A Volunteer Data Scientist in Machine Learning applies data analysis and machine learning techniques to help organizations solve problems, often for nonprofits or community projects. They may work on tasks such as cleaning and analyzing datasets, building predictive models, or creating data visualizations. Their work supports impactful decision-making and can help organizations operate more efficiently or achieve specific social goals. Volunteers often collaborate with teams to define project objectives and deliver actionable insights using their technical expertise.

What skills and qualifications are needed to thrive as a volunteer data scientist machine learning?

To thrive as a Volunteer Data Scientist (Machine Learning), you need proficiency in statistics, data analysis, programming (Python or R), and a foundational understanding of machine learning algorithms, often supported by a relevant degree or online certifications. Familiarity with tools like scikit-learn, TensorFlow, Jupyter Notebooks, and data visualization platforms is typically required. Strong problem-solving abilities, teamwork, and effective communication are crucial soft skills for translating complex data insights to non-technical stakeholders. These skills and qualities are essential to effectively contribute value, support decision-making, and drive impact in resource-limited volunteer environments.

How does a volunteer data scientist machine learning typically collaborate with other team members or departments?

As a Volunteer Data Scientist specializing in Machine Learning, you will often work closely with cross-functional teams such as project managers, software engineers, and subject matter experts. Effective collaboration is essential, as you may need to clarify project goals, source and preprocess data, or translate complex findings for non-technical stakeholders. Regular meetings and open communication help ensure that your machine learning solutions are aligned with the organization's mission and that your insights are actionable. This collaborative environment provides valuable experience working in diverse teams and often leads to impactful, real-world applications of your technical skills.

What is the difference between Volunteer Data Scientist Machine Learning vs Volunteer Data Analyst?

AspectVolunteer Data Scientist Machine LearningVolunteer Data Analyst
Required CredentialsKnowledge of machine learning algorithms, programming skills (Python, R), basic statisticsProficiency in data visualization, basic statistics, Excel, SQL
Work EnvironmentCollaborative projects, research-focused, often remote or nonprofit settingsData reporting, dashboard creation, data cleaning in nonprofit or community projects
Employer & Industry UsageTech nonprofits, research institutions, startupsCharities, educational organizations, community initiatives

Volunteer Data Scientist Machine Learning focuses on developing predictive models and advanced analytics, requiring programming and machine learning expertise. Volunteer Data Analyst emphasizes data interpretation, visualization, and reporting. Both roles support nonprofits but differ in technical complexity and focus areas.

What are the most commonly searched types of Data Scientist Machine Learning jobs in Madison, WI?

The most popular types of Data Scientist Machine Learning jobs in Madison, WI are:

Volunteer Data Scientist Machine Learning jobs near you

Data Scientist

State of Wisconsin Investment Board

Madison, WI • On-site

Full-time

Re-posted 3 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.

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