Hi, I'm Jay

Product leader and AI builder

About me

I'm passionate about creating great products that solve hard problems.

I believe the best outcomes result from open collaboration — between product and engineering, of course, but also via frequent interactions with clients and prospects, go-to-market teams, the C-suite, and anyone else with curiosity and a drive to help create best-in-class software solutions. Particularly important is the ability to translate in-the-weeds concepts into layman's terms for non-technical audiences, and vice versa.

My career has spanned everything from Series A-stage tech startups to sprawling, 150,000-person organizations. The startups I've joined were acquired (twice!) for over $1.2 billion collectively. My expertise is concentrated in launching large-scale technical products. As the head of brand safety product management, I led an international team of PMs to build best-in-class contextual data products that generated millions in revenue within their first three years. These included API-first viewability and fraud prevention integrations with the largest buying platforms in the digital advertising ecosystem, as well as highly bespoke and first-in-market measurement integrations with marquee social platforms including Facebook, Instagram, Snapchat, and Pinterest.

I like getting my hands dirty: I've leveraged my technical curiosity to teach myself multiple languages -- Ruby on Rails, Python, and JavaScript -- and launched a wide variety of apps (see below projects), and I'm comfortable using a broad variety of cloud services, tools, and frameworks (AWS, Hugging Face, Streamlit, and Retool, for example) in a wide swathe of applications ranging from audio analysis to model fine-tuning. Lately I've been building and open-sourcing LLM-powered tools to summarize podcasts and analyze transcript accuracy.

I'm always up for a chat!

Recent Substack posts

Move fast and break things
The trouble with 'everything-bagel' Abundance is the gaping hole right in the center of it.

When political polling and betting odds diverge
I've created a new tool to correlate pairs of time-series datasets. So now you can measure just how much Thursday's debacle split the presidential polling average from the PredictIt odds.

A smarter way to measure transcript accuracy
TLDR: I've built a Transcript Accuracy Analyzer app to evaluate accuracy in speech-to-text ASR models. (Warning: this gets pretty in the weeds.)

Get in touch

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