About
I like turning messy problems into something we can actually solve.
I'm someone who enjoys working with data, building models, and figuring out why things behave the way they do. My background is in economics and computer science, which means I tend to approach problems from both a quantitative and computational perspective.
I've worked on problems across industry and research — from understanding customer behavior and measuring the impact of interventions to building predictive models and working with financial and economic data. Depending on the problem, that might mean statistics, machine learning, causal inference, or simply finding a clever way to work with the data.
What I enjoy most is the process: starting with a question that isn't particularly well-defined, breaking it down, testing ideas, and eventually getting to an answer that's both rigorous and useful.
Lately, I've been spending a lot of time exploring machine learning, AI, quantitative methods, and how they can be applied to real-world problems.
Outside of work, I'm usually learning something new, reading about an idea I came across, or building something just because I wanted to see if it would work.
Approach
- —I like starting with why before jumping into the data.
- —Keep it simple when simple works.
- —Question assumptions and let the data surprise you.
- —Build things that are useful, not just technically interesting.
What I Work On
- —Predictive modeling, forecasting, and machine learning.
- —Causal inference, experimentation, and impact measurement.
- —Customer behavior, growth, and decision analytics.
- —Financial, economic, and quantitative modeling.