Chris Moody

I'm an experienced people manager with a history of growing and leading diverse teams of machine learning engineers and scientists that create algorithmic products and recommender systems.

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I’m building Popgot to compare unit prices (per oz/sheet/lb) across Costco, Walmart, Target, and Amazon. We normalize fuzzy sizes (“family,” “mega,” multipacks) so you see the actually cheapest option for staples.
We recently launched a deep research mode that, on demand, crawls thousands of product pages and uses visual LLMs to read label photos (ingredients, counts, square footage) when the text is messy. First run takes ~60–90s, then it’s cached.

Highlights

At Stitch Fix, I envisioned and created a massively engaging “Tinder for Clothes” app -- Style Shuffle. This key vehicle for data collection strengthened our algorithms and allowed us to enhance our personalized recommendations. As the product took off, I founded and scaled a complementary ML team.

  • Increased engagement by ~30x by shifting 50% of monthly active users to daily active users

  • Increased company revenues by hundreds of millions of dollars via improvements in recommendations, stylist matching, email targeting and other algorithms (see an earnings call)

  • Collected ~10x more data per user which led to more personalized algorithms via "latent style embeddings"

  • Hired and grew a diverse AI team of data scientists and ML engineers from zero to fourteen, eventually including managing managers. I am happy to provide references from managers, reports, and partners.

  • Scaled our production machine learning infrastruture to millions of users and tens of billions of ratings

  • Interviewed (along with our team) for articles in the popular press: WIRED, FastCompany, and Quartz.

  • Developed Style Tyles -- another recsys app I built around dynamic explore & exploit.

As an individual, I have created many side projects, all revolving around creating strong data-driven feedback loops. To get there, I've built engaging full-stack apps with components like engaging game-like frontends, scalable data backends, and model deployment and logging infrastructure.

  • Corner Champ. An app for learning crowd-sourced home valuations. Zillow & competitors are usually off by ~5%, and home buyers placing offers need better home price estimates

  • Hype the Like A Shopify-compatible app that was "Tinder for Product Analytics." It's a free tool for merchants to do zero-risk product exploration, effectively showing images items before stocking.

I've built research- and production-grade machine learning algorithms.

I have spoken at many machine learning conferences, including an invited KDD 2018 (the largest machine learning conference) tutorial on building deep-learning based recommenders in PyTorch