About

Most people don't end up in Revenue Operations by way of a statistics thesis on climate model extremes. I did.

I hold a M.S. in Statistics from UC Santa Cruz, where my thesis compared and assessed the extremes of different types of climate model simulations, and a M.S. in Statistics from Brigham Young University, where I modeled mass spectrometry experimental data using Gaussian processes. Both degrees were exercises in the same underlying skill: taking messy, high-volume data and building a rigorous model of what's actually happening underneath it.

That's the same skill I use now — just applied to ARR movement instead of climate extremes, and to Salesforce data instead of spectrometry output. I started as a Data Analyst building machine learning models to predict churn and customer longevity, then moved into operations and revenue systems, where I found that the biggest lever for a growing company usually isn't a smarter model — it's whether the underlying data and systems are trustworthy enough to act on. That's what pulled me toward Revenue Operations: it's the role where statistical rigor, systems ownership, and business judgment all have to work together.

Most RevOps candidates come from sales operations or customer success. I come from a formal statistics and machine learning background, which shows up in how I approach the work: I default to asking what the data actually supports before I trust a metric, and I'd rather build the pipeline right once than patch a report every month.