Four things I built — what the problem was, what we shipped, and what changed.

1. 01 · Northwestern Mutual Investment Management Co.

   Director (Oct 2024 -- present) / Associate (Jul 2021 -- Oct 2024) · Milwaukee, WI

   2021 -- present

   An in-house analytics platform for a $130B Public Investments department

   Problem

   The Public Investments department needed shared analytics infrastructure — one stack the desk could trust for relative-value, exposure, rebalancing, and risk analytics. Tooling was scattered across notebooks, spreadsheets, and one-off dashboards; the same questions were being answered five different ways. There was no platform, no shared vocabulary, no governance, and no team to own any of it.

   What I built

   I defined the data strategy and set the platform roadmap. Brought dbt to the firm — the first dbt deployment in the department, now load-bearing infrastructure across the analytics stack. Designed an in-house Streamlit visualization framework that turns Snowflake models into governed, interactive decision-support tools used daily on the desk. Built the analytics & ML practice from a single seat — recruited, hired, and trained the rock-star team that ships it. Established federated analytics architecture, governance, lineage, and Investment Committee reporting. I sit on the firm's AI committees defining how the rest of NM uses applied AI for investments — and I'm still close enough to the work to write the dbt models and review the Streamlit code.

   Who used it

   70+ portfolio managers, traders, analysts, and research professionals using the platform daily — and the Investment Committee consuming the reporting that comes out of it.

   Outcomes

   • ✓ Defined the data strategy and platform roadmap for the $130B Public Investments department
   • ✓ Brought dbt to the firm — first deployment in the department, now load-bearing across the stack
   • ✓ Designed and shipped an in-house Streamlit visualization framework adopted across the desk
   • ✓ Built the analytics & ML practice from a single seat — recruited, hired, trained a rock-star team
   • ✓ 70+ portfolio managers, traders & analysts using the platform daily
   • ✓ Established federated analytics architecture, governance, lineage & Investment Committee reporting
   • ✓ Sit on internal AI committees defining the firm's posture on applied AI for investments
   • ✓ Live ML decision-support models in production supporting real investment decisions

   SnowflakedbtPythonStreamlitscikit-learnFederated analyticsSQLTableau

2. 02 · State of Wisconsin Investment Board

   Risk & Analytics IT Development Manager, Data Science Team · Madison, WI

   2019 -- 2021

   Modernizing a $100B+ fund's investment risk model

   Problem

   SWIB's custom fund-level risk model — the one used in Investment Committee reporting on the $100B+ fund — was the right idea on the wrong stack. It was inflexible, slow to change, and ran far less often than the team needed.

   What I built

   Led a multi-year modernization that replaced legacy batch reporting with a high-frequency analytics platform on SQL Machine Learning, Python, and Tableau. In parallel, served as product owner, designer, and SME for the firm-wide Snowflake security-level risk and exposure analytical data store — defined the federated data model, governance, lineage, and quality controls supporting multi-billion-dollar portfolios. Drove data literacy across the investment organization, educating colleagues and developing training materials for investment services staff.

   Who used it

   The Investment Committee, the Risk team, portfolio managers across asset classes, and the analysts who lived in the exposure dashboards every day.

   Outcomes

   • ✓ $100B+ fund risk model rebuilt — high-frequency analytics platform replacing legacy batch reporting
   • ✓ Output used directly in Investment Committee reporting
   • ✓ Firm-wide Snowflake security-level risk & exposure store stood up as the cross-asset backbone
   • ✓ Federated data model, governance, lineage & quality controls established for multi-billion-dollar portfolios
   • ✓ Drove data literacy across the investment organization with training materials and direct mentorship

   SnowflakeSQL MLPythonTableauFederated analyticsData product ownership

3. 03 · State of Wisconsin Investment Board

   Senior Quantitative Analyst, Asset & Risk Allocation · Madison, WI

   2013 -- 2019

   Original quantitative strategies and a $50M automation win

   Problem

   Two problems, one team. Strategy R&D needed faster, more flexible infrastructure to take ideas from research through back-test to live trading. And a long tail of manual reporting workflows was leaking value — slow, error-prone, and a tax on every other decision.

   What I built

   Pioneered original quantitative investment strategies — from research and back-testing through to live trading — using custom algorithms, large historical datasets, self-built databases, and a lot of carefully-written R. In parallel, persistent automation of the manual reporting workflows the rest of the desk relied on. Authored the firm's rebalance playbook research paper. Founding member of the SWIB AI team — studied and experimented with early AI tools and software libraries, building internal expertise in applied machine learning for investment workflows.

   Who used it

   The Asset & Risk Allocation division, portfolio managers across the fund, and the rest of the firm via the rebalance playbook and the early AI working group.

   Outcomes

   • ✓ $50M added to the fund through automation, better data, and faster reporting
   • ✓ Rebalance playbook research paper adopted firm-wide — increased fund Sharpe ratio
   • ✓ Original quant strategies taken from research through back-test to live trading
   • ✓ Founding member of the SWIB AI team — built internal expertise in applied ML for investments

   RQuantitative strategy R&DTime seriesRisk modelingApplied ML

4. 04 · T. Rowe Price Group, Inc.

   Associate Analyst — Equity Research, Technology Sector · Baltimore, MD

   2007 -- 2010

   Equity research tooling for the technology sector team

   Problem

   The technology sector team was producing world-class fundamental research, but doing it on top of disparate tools and a lot of manual data manipulation. The friction between an analyst and the question they were trying to answer was bigger than it needed to be.

   What I built

   New internal processes for obtaining and manipulating data via Excel macros — the kind of unglamorous infrastructure that becomes load-bearing. Worked directly with the head of the technology team to design and lead the rollout of a firm-wide system for sharing critical stock and portfolio information.

   Who used it

   T. Rowe Price's technology sector analysts, the broader equity research function, and ultimately the portfolio managers consuming the research.

   Outcomes

   • ✓ Proprietary financial models built and maintained across the public software sector
   • ✓ Excel macro pipelines became part of the firm's standard investment workflow
   • ✓ Firm-wide stock & portfolio information distribution system designed and rolled out

   Equity researchFinancial modelingCloud / SaaS sector coverage

The full chronological record lives in the two-page resume.