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About This Role
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.
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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At State of Wisconsin Investment Board, 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 $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
State of Wisconsin Investment Board AI Hiring
State of Wisconsin Investment Board has 1 open AI role right now. They're hiring across Data Scientist. Based in Madison, WI, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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