About

Full-stack data scientist, FinTech background.

[ FinTech ][ Experimentation Platforms ][ Strategic Analysis ][ Data Engineering ]

I'm a Staff Data Scientist at Intuit, working within Credit Karma, where I've spent my career at the intersection of consumer FinTech and rigorous measurement, figuring out not just what's happening in the product, but what would happen if we did something differently, and how confident we should be about it.

Most recently, I've focused on our experimentation platform: I'm the primary contributor to our internal experimentation codebase, building the features that streamline experiment analysis and reporting. When a team needs to run a test, size it, or make sense of a messy result, I'm usually the first call.

More recently I've started learning causal inference: methods like difference-in-differences, synthetic control, and instrumental variables, for the questions where running an A/B test isn't possible but the business still needs a real answer.

What I actually do

  • 01

    Strategic analysis & leadership

    Primary analyst for leadership, regulator, and external requests. Metrics I've defined now feed multiple years of company OKRs and planning.

  • 02

    Experimentation

    Built the platform's underlying logic and its alerting/guardrail program, and led the shift from frequentist to Bayesian methods for faster, more confident decisions.

  • 03

    Automation & tooling

    One of my primary charters: automating repetitive work and building the tooling (including AI-powered workflows) that streamlines analysis across the team.

  • 04

    Exploratory & predictive analysis

    Turning open questions into a first, testable read of the data, occasionally extending into prediction models built for analysis, not production.

  • 05

    Data engineering / ETL

    Building the pipelines that make the analysis possible in the first place.

  • 06

    Causal inference

    The newest addition to the toolkit: diff-in-diff, synthetic control, IV, for questions a randomized test can't answer.

The work I care most about sits at the intersection of experimentation rigor and clear metric ownership: measurement problems where the answer isn't obvious going in. That's held true across FinTech, and it's the throughline I look for in whatever comes next.