Technologist / Team Builder / Product Leader
Paul Kauders
I'm a technologist who builds products and leads the teams behind them. Over eighteen years at Northwestern Mutual, the State of Wisconsin Investment Board, and T. Rowe Price I've taken data and AI platforms from blank slate to daily habit. I run the whole loop: discovery with users, product definition, architecture, hiring, and shipping.
What I bring
01
Products, end to end
I run the whole loop: discovery with the people who'll use it, product definition, architecture, and shipping. The platforms I've built went from scattered notebooks and spreadsheets to governed products that 75+ professionals open every morning. Adoption is my metric, not delivery.
02
Teams from scratch
I built Northwestern Mutual's analytics and ML practice from a single seat: recruited, hired, trained, and set the strategy. I stay close enough to the work to write the dbt models and review the code, and I never ask of my team what I won't do myself.
03
Both sides of the table
Most technology leaders are either domain experts who can't build or engineers who don't know the business. I'm a CFA with an MS in Computational Finance who architects and ships. I build the platform and understand the users, because I was the user.
Experience
All case studies →Director, Quantitative Research & Analytics
Northwestern Mutual
2021–present
Built the $130B analytics platform and lead the team behind it. 75+ investment professionals use it daily. Sit on the firm's AI committees.
Analyst to Risk & Analytics IT Development Manager
State of Wisconsin Investment Board
2012–2021
Nine years, three roles. Modernized the $100B+ fund risk model, pioneered quant strategies through live trading, $50M automation win.
Associate Analyst, Equity Research
T. Rowe Price
2007–2010
Technology sector equity research, firm-wide tooling.
Writing
All posts →Adoption is the metric
Internal tools don't fail at launch. They fail three weeks later, quietly.
Driving AI inside an investment firm
The committee, the platform, and the gap between vendor demos and reality.
Snowflake + Streamlit vs. Power BI and Tableau
The case for the data product mindset in investment analytics.
Working together
Building something ambitious with data and AI?
I'm at my best taking a platform and a team from zero to load-bearing: finding out what your people actually need, building the thing they'll rely on daily, and hiring the team that keeps it getting better. If that's the problem on your desk, I'd love to hear about it.