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Madison, Wisconsin, United States
Hybrid
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).