About
About me
My work spans conversational AI, advertising, streaming, commerce, and global payments. At Amazon, I shape advertiser and customer experiences across Ads and Prime Video. Before that, I led content design for Mastercard’s global payment products.
I came to content design through sociology and writing, and I’m as interested in how systems shape behavior as I am in the words themselves. I’m curious, direct, and happiest in the middle of a complicated problem—asking questions, finding the structure, and making the experience easier to understand.
My work has taken me through enterprise software, global payments, streaming, advertising, and conversational AI. I tend to get involved early, while teams are still deciding what a product is and how it should behave, then stay with the work through research, review, and launch.
I’ve worked as both a manager and an individual contributor, leading workshops and critiques, mentoring designers, and building practices other teams could use. I’m now exploring hands-on Staff and Principal roles where I can help shape the product and stay close to the writing.
- Focus
- Conversational AI, content systems, information architecture, taxonomy, UX writing, and product strategy
- Domains
- Advertising, creator marketplaces, streaming, commerce, payments, and enterprise software
- Looking for
- Hands-on Staff and Principal content design roles
How I work
Four habits-
Understand the product and the decision
Before I write, I make sure I understand what the product does, what the customer is trying to accomplish, and what information they need.
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Design for the actual context
I account for how and where someone will use the product—whether they’re navigating with a remote, watching a live game, building an ad campaign, or trying to complete a payment.
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Bring the right people in early
I bring in product, design, research, engineering, science, legal, and privacy early enough for the findings to change the product.
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Test, measure, and make it reusable
I use research and experiment data to challenge assumptions, then turn what works into guidance and patterns other teams can reuse.