Hi, I'm Alison.

I'm a statistician who leads data science teams. My work is the modeling at the center of a product (Bayesian, spatial, causal), and then defending that modeling to the people who have to trust it: buyers, auditors, investors, and standards bodies.

Right now I’m the technical lead for the carbon program at Funga, an Austin company that restores the forest microbiome so trees grow faster and store more carbon. My models underpin the carbon credits we sell, and I own the quantitative framework of the Verra methodology they’ll be credited under.

$24M+in carbon credit sales underpinned by models I built

60k+project acres forecast and monitored

1Verra methodology whose analytics I rebuilt and carried through review

720×speedup I engineered for large-scale Bayesian inference

15 yrsof modeling for decision-makers

What I do

Lead

Set the data science strategy and roadmap. Run model governance, technical review, and hiring for a team of six data scientists and data engineers. Explain the model to the exec team, the board, and the auditor.

Model

Hierarchical Bayesian, spatial and spatiotemporal, survival, causal inference and experimental design, forecasting with honest uncertainty. Most of it ends up in front of a reviewer, and I build it with that in mind.

Ship

Production Python and R pipelines, PostgreSQL, Databricks, MLflow, CI, and a spec-driven workflow for AI-assisted analysis. I care a lot that other people can run what I build.

How I work

Curiosity first. I got into this field because I like learning things, and every question I’ve worked on has led to more questions. That is still what I like about the work.
Simple beats clever. I’d rather ship a model the whole team understands than a cleverer one only I can run. Validation, reproducibility, and code review are how I make that stick.
Uncertainty is the deliverable. Every number I hand over comes with an interval and a plain-language reason for its width. In my experience the interval is what gets asked about.
I don’t mind being reviewed. Public comment, VVB review, buyer diligence, investor questions. Most of my work goes through at least one of these, and it comes out better for it.
I use AI agents, and I review every line. Written specs, one branch and PR per change, CI checks, and my statistical sign-off on every merge. I hold agent-written code to the same standard as hand-written code.
I was a freelance photographer for ten years before I was a statistician. I ran a studio in Brooklyn and interned for Annie Leibovitz. The long version is on the resume page.
Few statisticians get to fly in helicopters over the tundra. I did, counting elk in Rocky Mountain National Park for my PhD. In Wisconsin I occasionally got to hug deer.

What I’m looking for

Head of Data Science, Lead Data Scientist, or senior quantitative roles where modeling is central to what the company sells, not a reporting function. Climate, carbon, nature, energy, agriculture, or anything else with real-world uncertainty and real-world stakes. I’m based in Austin, TX and happy to work remote.

Download my resume Email me LinkedIn