Product designer on Payment Intelligence at Stripe, connecting the dots across complex systems so they feel simple. Previously Stripe Terminal, Uber, and a founding designer at a creative agency.
Be the risk team people don’t have.
01 · Radar
Radar was powerful but fragmented. Seven surfaces each held part of the risk story, and merchants had to connect them on their own. As Radar grew from transaction fraud into other kinds of risk and abuse, every new risk type multiplied the product’s complexity.
Decision 1
Detect, investigate, act, prove. Every risk type runs through the same four steps, with the same signals, explanations, and outcomes, so merchants learn Radar once.
Decision 2
A system that lets people detect, investigate, and act on fraud, anywhere in Stripe.
Most merchants don’t have a fraud team. They have one person, or no one. So Radar does more of the work itself: it spots patterns, recommends the right control with its trade-offs, automates low-risk decisions, and keeps a record of why.

Decision 3
New risk types extend shared surfaces like reviews, insights, rules, and controls instead of shipping parallel tools. Each one tells the risk story in the same order: what’s happening, what’s the impact, what’s the trend, what’s the evidence, and what you can do about it. The org changed with it: one shared risk journey, and platform ownership for the experiences every team relies on.

Outcome
1 to many
Fraud types supported across Stripe.
Every insight should end in an action.
02 · Radar
Merchants wanted Stripe to give them advice: which fraud patterns mattered, what to do about them, which controls to adopt, which rules to write. Interviews and a journey map showed where it broke down: in the middle of an attack, when the answers mattered most. So we designed the path from noticing something to acting on it, Radar’s core diagnostic loop.
Decision 1
Insight components in the Radar overview watch for fraud patterns, customer risk, and abuse before a merchant goes looking. Each one says what changed, why it matters, and what to do next.

Decision 2
Risk scores on transactions and customers connect to risk controls that act on their own, and to the numbers that show whether they worked. A risk and response module keeps the three together, so automation never runs blind.

Decision 3
Rules used to be written in a query syntax, and rule quality, satisfaction, and time to write were all poor. An if, this, then builder replaced it, organized around what a merchant wants to protect, with a backtest before anything goes live. Rules got better, faster to write, and easier to trust.

Outcome
2–3 to 10
Automated defenses in about a year. Each square is one defense; orange ones are new.
One reader, then many.
03 · Terminal
When I started, Terminal was a basic Android reader on a single device, with very little dashboard behind it. Over three years, as its only designer, I helped it become a system: readers platforms could build on, phones that take payments, and fleets for the largest enterprises, across a growing family of devices.
Decision 1
First, the reader itself: tipping, forms captured on the reader, conversion controls, theming, and the system screens around them. Platforms building on Terminal could finally offer their own merchants a complete checkout.

Decision 2
Tap to Pay on Android was the first of its kind: taking payments on a phone, with no other hardware. Every phone is a different shape and size, so the flow was shaped through rounds of usability studies across form factors.

Decision 3
Then it got hard. Enterprises run multinational setups with sub-brands, regions, and their own ways of configuring devices. Fleet management covered a device’s whole life: logistics and returns, setup and configuration by region, observability, and software rollouts across first- and third-party hardware. Meanwhile the portfolio grew beyond the WisePOS E into many sizes, and I shaped the payment experience to hold together across all of them.

Outcome
+XX%
Share of processing volume.
Every order is a promise with a clock on it.
04 · Uber
Eats was moving from restaurant delivery to getting anything, but its grocery landing pages were really just search results. I led a design sprint to answer one question: how do people find, reorder, and discover groceries they love?
Decision 1
Grocery shopping is a chore, and Eats was known for speed, not groceries. The first job of the landing page is trial: lead with what makes on-demand worth it, from fast stores and fulfillment to selection and deals.


Decision 2
Staying is a routine, so we experimented with what brings people back: returning orders and order again, communications timed to their week, recipes that turn into a cart, and deals on the things they already buy.


Decision 3
Beyond grocery came convenience, alcohol, and verticals that didn’t exist yet. Every module was designed to change with its context and move between a vertical’s landing page and the main feed.


Outcome
Multi-year
A roadmap and a strategy that continued long after the two-day sprint.
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