About
About Me
I'm a full-stack data scientist who works with early-stage companies, the kind that generate experimental or product data faster than they can make sense of it, and that need a data partner more than a vendor.
- ✓ M.Sc. Machine Learning
- ✓ 10 years as Data Analyst and Data Scientist
- ✓ Extensive technical toolkit
- ✓ Available for remote work worldwide
Most recently, I spent two and a half years at a battery materials startup, where I helped guide the data science work, supported the team, and built models that connected manufacturing parameters to product performance. Before that, I worked across product analytics at Adobe, data quality work at a healthcare data company, and consulting projects for Fortune 500 clients at a market research startup. The thread running through all of it: getting messy data into a usable state, building things the rest of the company actually uses, and translating between technical specialists and the people making decisions.
A few things that tend to matter to the companies I work with:
- I bridge engineering and business. I've worked inside a deeptech company, so I'm at home with noisy data, lab experiments, and the engineers and scientists making sense of them. I'm also used to taking what the data shows and explaining it clearly to the people making decisions and running the business, without losing nuance along the way.
- Strong coding background. A decade-plus with Python, which means I can work effectively when the data is messy and the questions are still half-formed. I don't need a tidy spec to start making progress.
- Practical over fancy. Most of what I do isn't cutting-edge modeling. It's getting the data into a state where straightforward analyses actually answer the question, and knowing when something needs more than that.
I'm available for freelance and contract work, remote worldwide. Engagements typically look like project work on a specific data problem, embedded consulting (joining your team for a defined period as an extra pair of experienced hands), or helping set up early data infrastructure for companies that don't have it yet.