Matt Deland Email & Phone Number

I am currently a data scientist at Stripe, helping the company make rigorous decisions using all of Stripes' rich data. I work across Payments, Product, Finance, and Risk - helping both understand the current state of the business and predicting the future state to enable growth for the millions of businesses that rely on Stripe.From 2018-2020: I led the "Payments Data Science" Team, growing it from our humble beginnings of 4 people to over 30 today. In 2021: I took over as the Interim Head of Data Science.In 2022: I lead the Product + Technology Data Science team - leading the Payments group, all of our Product Data Scientists (working with e.g. Radar, Payouts, Connect, Billing+Invoicing, Capital, and Issuing) as well as our Technology Science teams (Security, Reliability, Efficiency, Data).In 2023: I took over as Head of Data Science working with all the above and our Risk, Finance, Treasury Data Science Teams. I'm fortunate to work with such a talented and experienced group of analysts and scientists on the team - 120 strong.We're always hiring (as of writing, my team has more than 10 open roles!) and you can see them all [on our jobs page](https://stripe.com/jobs/search?teams=Data+%26+Data+Science)Prior to Stripe I was a ML engineer at Box, building data driven features into the product. Box acquired Wagon - where I was a cofounder - a team focused on making the process of data analysis visual, repeatable, and collaborative. Previously, I was a data scientist and mathematician at Groupon, working to connect merchants to customers, and customers to merchants. I developed an internal, predictive, sales optimization pipeline to inform the company's sales organization.Prior to Groupon, I was the lead data scientist at Hyperpublic, where our mission was to collect, index, and distribute structured, rich, local data. Prior to Hyperpublic I was an academic mathematician (Columbia University PhD) actively researching in algebraic geometry. I was employed by SUNY Stony Brook as a Simons assistant professor and at the University of Michigan as a Hildebrandt assistant professor.Specialties: Mathematics, data mining, statistical learning and analysis, predictive model building and evaluation.

Listed skills include Teaching, Big Data, Research, Python, and 11 others.