<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A/B Testing on Sergio A. Mora Pardo | AI/ML Technical Lead</title><link>https://sergiomorapardo.github.io/tags/a/b-testing/</link><description>Recent content in A/B Testing on Sergio A. Mora Pardo | AI/ML Technical Lead</description><generator>Hugo</generator><language>en-US</language><atom:link href="https://sergiomorapardo.github.io/tags/a/b-testing/index.xml" rel="self" type="application/rss+xml"/><item><title>Personalized Product Discovery &amp; Ranking</title><link>https://sergiomorapardo.github.io/work/personalized-product-discovery/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://sergiomorapardo.github.io/work/personalized-product-discovery/</guid><description>&lt;h2 id="the-problem"&gt;The problem&lt;/h2&gt;&#10;&lt;p&gt;Affiliate product discovery depended on a static, curated landing experience. That limited the number of products users could explore and left little room for relevance to adapt to each session.&lt;/p&gt;&#10;&lt;p&gt;The product opportunity was not simply to train a ranking model. It was to redesign the discovery system and prove, under controlled conditions, that personalization created incremental value across the funnel.&lt;/p&gt;&#10;&lt;h2 id="my-role"&gt;My role&lt;/h2&gt;&#10;&lt;p&gt;I led the ML design for ranking and discovery, partnering across product and engineering to move from a curated landing to a continuous personalized feed. My contribution covered problem framing, ranking signals, experiment design, technical review, and interpretation of the results.&lt;/p&gt;</description></item></channel></rss>