Senior product designer in New York, building dashboards, design systems, and data-heavy enterprise tools that make complex work feel simple.
Currently Senior Product Designer, Freelance
Previously Thoughtworks / Digilence / ThinkEmpire
4 projects
Merchandisers plan and manage shelves at scale, but had hundreds of reports available with no way to know which ones existed or applied to their job. Nothing was tagged or curated, and search barely worked.
Interviews showed where the time actually went: not reading reports, but finding them and then doubting they had the right one.
Finding the right report was the whole problem. Everything else followed from that.
The homepage included a widget that flagged unusual numbers in a merchandiser's own reports. These users were experienced, process-driven, fluent in Excel, and skeptical of software that made recommendations for them. Compare both versions below.
Anomaly detected in SKU cluster 4471B
Confidence 0.87. Deviation of 2.4 sigma from the trailing 12 week baseline. Review recommended.
Why this fails. It is correct and nobody can use it. It asks a merchandiser to translate the model's vocabulary, gives them no way to check it, and sounds like an alarm during the busiest weeks of their year.
Sales on your top endcap fell for the third week running
Worth a look before Monday planning. Three stores account for most of the fall.
From: Weekly Endcap Performance
Three rules I designed to. Say it in plain words and say what to do. Name the report it came from, so people can check it rather than take it on faith. Ask for attention without sounding an alarm, because this kind of feature turns into a warning system very easily.
A replacement tool already existed, but nobody used it and nobody could explain why. I proposed a six-week pilot: one department, the new tool only, no fallback, with me supporting them throughout. The old tool had been in use for over twenty years.
Every user moved across. They were never resisting the product; they simply did not know it yet, and teaching them was enough.
A working rebuild of the shipped homepage: search, filter by business task, save a report, or select a few and combine them into one view.
8 reports ·
Select a report to preview it here.
Tax software is built for the person filing, not the accountant preparing it. Digilence is built for the accountant, whose work takes far too long with tools never designed for it. In tax season, that becomes 80-hour weeks.
Six types of users touch this product, and they have almost nothing in common. Designing the inbox for a tax admin and the dashboard for a CEO are opposite problems.
Discovery raised more questions than answers, and those questions shaped the information architecture. Choose one below.
The one that mattered most. Get it wrong and the inbox and the advisor end up competing for the same place in someone's head.
How the alerts work decides whether the inbox is a relief or just more noise for someone already at 80 hours.
Six kinds of user with opposite needs cannot share one set of onboarding screens or one empty state.
This decides whether the product stays inside the firm or goes out to clients, which changes almost everything after it.
A working rebuild of the two features I owned: the inbox, and a Digital Advisor that shows something different depending on who is looking at it.
6 items need attention
Notes from yesterday's check-in, plus a few action items for next week.
Acme Corporation's PBC list needs review before Friday's deadline.
Everything's uploaded to the portal and ready for your review.
A transaction over $10k was flagged for manual review.
Following up on the checklist items from last week's call.
Would love to discuss adding recurring invoice support.
Inbox clear. Nice work.
PBC checklist — Acme Corporation
0 of 4 documents received
Team performance
Firm metrics & system controls
Also on the admin roadmap
No clients found matching your search.
Client creation is view-only in this rebuild.
A founder needed everything from one contractor. I treated the project as a test of one question: how much of a real product can a single designer ship if AI handles production? The answer came down to how precisely each prompt was written. Compare the two below.
What comes back. Rounded cards, a sage green palette and a soft sans serif. Competent, ordinary, and impossible to keep consistent across twenty screens because there is nothing specific to hold on to.
What comes back. A direction specific enough to survive twenty screens without drifting, and specific enough that I can tell at a glance when something is wrong. You cannot review a result unless you set the terms first.
AI produces layouts faster than anyone can draw them by hand, but it cannot tell the difference between a layout that is correct and one that feels right. That judgment remained mine, and it is most of the job.
The same is true of the code. I did not write the animation logic by hand, but reviewing it required knowing the React Native Animated API well enough to catch mistakes. That is easier than writing it from scratch, but it still takes real knowledge to do well.
The old page was a brochure: sections in blocks of color, serif body copy, screenshots that did nothing, and no path to signing up. The new page leads with a search bar, because that is what the research showed people expected.
I tested with 12 people from inside the company, which was a mistake. Everyone already knew what the product was, and explaining that is the one job this page has to do. Next time, I would recruit people from outside who had never seen it.
“Major unlock for the business.”
“The thoughtfulness from this team is incredible.”
“Became the reference model for all future analytics product design at the client.”
I combine UX thinking with product strategy to design scalable solutions that solve real user needs, align with business goals, and are built to last, not just sit in Figma. No fluff. No over-design. Just thoughtful, tested solutions that work.
I don’t just ask questions, I ask the right questions. I dig into user psychology, business constraints, team dynamics, and market signals to uncover what’s really going on. I turn scattered insights, fuzzy ideas, and half-baked goals into clear, focused direction. If a brief doesn’t exist, I’ll help build it. If the strategy is murky, I’ll clarify it. That’s where the work really starts.
I move from strategy to systems with speed and intention. Every screen I design is grounded in business goals, user insights, and practical constraints. I annotate clearly and structure files so cross-functional teams can easily follow along. I believe in collaboration that’s honest and proactive, not just checking a box. You’ll always know why I made a design decision, not just what I did.
My work doesn’t have to stop at the handoff. I validate designs through experimentation, testing, and ongoing iteration. Whether it’s an A/B test, a usability study, or post-launch analysis, I believe in learning fast and improving faster. I’m not afraid to change direction when the data demands it. I make sure insights feed directly into the next round of work.
I work as a senior product designer and design quality analyst. Most of my career has been leading design end to end on analytics and fintech teams: owning the research, setting the system, and defending the decisions. The work I am proudest of is a hub that stopped a merchandising team from hunting through hundreds of reports every week.
Lately I have worked on AI from two directions: designing features people will act on without understanding how the model works, and using LLMs for real production work across design and code. In both cases, the job is knowing what a good result looks like and rejecting everything else.