Data Scientist

Company Overview

The State of Wisconsin Investment Board (SWIB) manages more than $178billion in assets, including those of the fully-funded Wisconsin Retirement System (WRS). It operates at a level often seen in top-tier global asset managers, and 61% of its investment professionals hold a Chartered Financial Analyst (CFA) charter. SWIB serves over 703,000 WRS beneficiaries and is committed to securing the financial future of those who serve Wisconsin.

The organization offers a modern workspace, hybrid work options, competitive compensation, and a range of benefits.

The Data Services & Engineering team builds, implements, and maintains industry‑leading systems and platforms that support SWIB’s diverse investment portfolios and strategies. The team acts as a trusted advisor and partner to the business, leveraging technology to deliver maximum value, align with future direction, and operate according to industry standards.

Lead the design, development, validation, and deployment of advanced analytics, AI, and machine‑learning solutions that support data‑driven investment decision‑making. Own the technical lifecycle for analytics products from problem framing through data requirements, modeling, evaluation, deployment, monitoring, and ongoing iteration.

• Architect and deploy solutions using Git Lab (merge requests, CI/CD pipelines, automated testing, release management) and Terraform, establishing strong engineering practices and reproducibility.
• Design, evaluate, and deploy AI‑enabled analytical solutions that measure output quality, detect hallucinations, and ensure reliability for decision‑making.
• Implement data‑quality, validation, and AI‑evaluation frameworks; define reliability metrics, testing protocols, and monitoring controls to guarantee accurate, traceable, and explainable outputs.
• Develop analytics applications and lightweight front‑end interfaces (PowerBI, Streamlit, React, or similar tools) to communicate findings and drive adoption, applying UI/UX principles for usability and clarity.
• Deploy solutions in cloud environments (Azure or AWS), coordinating with engineering and security to ensure secure, scalable, and cost‑aware deployments.
• Utilize data‑warehousing technologies such as Snowflake to support analytics initiatives, collaborating 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 by providing design input, conducting code and analysis reviews, sharing patterns and best practices, and coaching junior staff through pairing, feedback, and knowledge sharing.

• Bachelor’s degree required; advanced degree preferred in finance, business, engineering, computer science, computational economics, mathematics, data science, or related discipline.
• Progress toward or completion of the CFA designation 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 that measure output quality, detect hallucinations, and ensure 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 Git Lab, including CI/CD, merge‑request workflows, automated testing, and release management.
• Experience with Terraform to provision and manage cloud infrastructure as code.
• Experience building and deploying machine‑learning models using modern techniques (regression, classification, clustering, time‑series/forecasting) with robust evaluation practices and sound statistical reasoning.
• Experience implementing data‑quality frameworks, validation controls, and reliability metrics/processes for analytical outputs and reports.
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