<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Propensity on Sergio A. Mora Pardo | AI/ML Technical Lead</title><link>https://sergiomorapardo.github.io/tags/propensity/</link><description>Recent content in Propensity 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/propensity/index.xml" rel="self" type="application/rss+xml"/><item><title>Causal ML Remarketing Engine</title><link>https://sergiomorapardo.github.io/work/causal-ml-remarketing/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://sergiomorapardo.github.io/work/causal-ml-remarketing/</guid><description>&lt;h2 id="the-problem"&gt;The problem&lt;/h2&gt;&#10;&lt;p&gt;High-volume remarketing can optimize clicks while still sending messages to users who would have purchased anyway. The product needed to decide &lt;strong&gt;who&lt;/strong&gt; should receive a message, &lt;strong&gt;when&lt;/strong&gt; it should arrive, and &lt;strong&gt;what&lt;/strong&gt; to recommend while measuring true incremental impact rather than correlation.&lt;/p&gt;&#10;&lt;h2 id="my-role"&gt;My role&lt;/h2&gt;&#10;&lt;p&gt;I built and led the ML remarketing engine for push and email across eight Latin American countries. The work combined causal modeling, personalization, serving architecture, and a sustained program of controlled experimentation.&lt;/p&gt;</description></item></channel></rss>