<?xml version="1.0" encoding="UTF-8" ?>
<?xml-stylesheet href="https://rss.buzzsprout.com/styles.xsl" type="text/xsl"?>
<rss version="2.0" xmlns:itunes="http://www.itunes.com/dtds/podcast-1.0.dtd" xmlns:podcast="https://podcastindex.org/namespace/1.0" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:psc="http://podlove.org/simple-chapters" xmlns:atom="http://www.w3.org/2005/Atom">
<channel>
  <atom:link href="https://rss.buzzsprout.com/2620097.rss" rel="self" type="application/rss+xml" />
  <atom:link href="https://pubsubhubbub.appspot.com/" rel="hub" xmlns="http://www.w3.org/2005/Atom" />
  <title>VARIANCE</title>

  <lastBuildDate>Wed, 27 May 2026 19:27:05 -0400</lastBuildDate>
  <link>https://podcasters.spotify.com/pod/show/pritul-patel1</link>
  <language>en</language>
  <copyright>© 2026 VARIANCE</copyright>
  <podcast:locked>yes</podcast:locked>
    <podcast:guid>3c1615b4-5512-5f44-b8c8-bf4d2f7b7b3e</podcast:guid>
  <itunes:author>Pritul Patel</itunes:author>
  <itunes:type>episodic</itunes:type>
  <itunes:explicit>false</itunes:explicit>
  <description><![CDATA[Variance explores the journeys of Data scientists, Product owners, Econometricians, Consultants specializing in the field of Experimentation and Observational Causal inference. Hosted by Pritul, a Data scientist who has spent 11 years scaling experimentation methods, infrastructure, and programs at scale at companies like Apple (Apple Podcasts), Peacock TV (streaming), ebay (ecommerce), and Yahoo (media), solving unique challenges of statistics and scalability.

Each episode is with a new guest where topics like debates in statistical methods, history, unique perspectives.]]></description>
  <generator>Buzzsprout (https://www.buzzsprout.com)</generator>
  <itunes:owner>
    <itunes:name>Pritul Patel</itunes:name>
  </itunes:owner>
  <image>
     <url>https://storage.buzzsprout.com/rz8ou7m4hzbrcxf08zt6zehtk3as?.jpg</url>
     <title>VARIANCE</title>
     <link>https://podcasters.spotify.com/pod/show/pritul-patel1</link>
  </image>
  <itunes:image href="https://storage.buzzsprout.com/rz8ou7m4hzbrcxf08zt6zehtk3as?.jpg" />
  <itunes:category text="Technology" />
  <item>
    <itunes:title>What Baseball Taught Us About Automating Judgment</itunes:title>
    <title>What Baseball Taught Us About Automating Judgment</title>
    <itunes:summary><![CDATA[MLB spent 7 years running a real-world experiment to automate the strike zone — and it nearly broke baseball. In this episode, we dig into what a Cornell research team found when they traced every design decision in the Automated Ball-Strike System: the strike zone was never what the rulebook said. What looks like a simple binary rule turned out to be 150 years of accumulated human judgment — and no sensor could measure that. If you build automated decision systems, this story is about you.  ...]]></itunes:summary>
    <description><![CDATA[<p>MLB spent 7 years running a real-world experiment to automate the strike zone — and it nearly broke baseball. In this episode, we dig into what a Cornell research team found when they traced every design decision in the Automated Ball-Strike System: the strike zone was never what the rulebook said. What looks like a simple binary rule turned out to be 150 years of accumulated human judgment — and no sensor could measure that. If you build automated decision systems, this story is about you.<br/><br/>Paper Reference: Inside Baseball: The Automated Ball-Strike System as an Object Lesson in Technological Rule Enforcement<br/><br/></p>]]></description>
    <content:encoded><![CDATA[<p>MLB spent 7 years running a real-world experiment to automate the strike zone — and it nearly broke baseball. In this episode, we dig into what a Cornell research team found when they traced every design decision in the Automated Ball-Strike System: the strike zone was never what the rulebook said. What looks like a simple binary rule turned out to be 150 years of accumulated human judgment — and no sensor could measure that. If you build automated decision systems, this story is about you.<br/><br/>Paper Reference: Inside Baseball: The Automated Ball-Strike System as an Object Lesson in Technological Rule Enforcement<br/><br/></p>]]></content:encoded>
    <enclosure url="https://www.buzzsprout.com/2620097/episodes/19227680-what-baseball-taught-us-about-automating-judgment.mp3" length="5086654" type="audio/mpeg" />
    <itunes:author>Pritul Patel</itunes:author>
    <guid isPermaLink="false">Buzzsprout-19227680</guid>
    <pubDate>Sat, 23 May 2026 19:00:00 -0400</pubDate>
    <itunes:duration>421</itunes:duration>
    <itunes:keywords></itunes:keywords>
    <itunes:season>1</itunes:season>
    <itunes:episode>3</itunes:episode>
    <itunes:episodeType>full</itunes:episodeType>
    <itunes:explicit>false</itunes:explicit>
  </item>
  <item>
    <itunes:title>Statistical Pitfalls in Tech Experimentation - My Journey Through eBay, Apple and Yahoo</itunes:title>
    <title>Statistical Pitfalls in Tech Experimentation - My Journey Through eBay, Apple and Yahoo</title>
    <itunes:summary><![CDATA[In this special second episode, the roles reverse as I share some stories from my extensive experimentation background across tech giants like eBay, Apple, and Yahoo. This is a cut from my first episode with Ishan where Ishan asked me about my experience.  Discover the statistical challenges that plagued major experimentation platforms - from misaligned alpha thresholds and rampant false positives to the complexities of handling extremely skewed revenue metrics. Learn how I rebuilt trust in A...]]></itunes:summary>
    <description><![CDATA[<p>In this special second episode, the roles reverse as I share some stories from my extensive experimentation background across tech giants like eBay, Apple, and Yahoo. This is a cut from my first episode with Ishan where Ishan asked me about my experience. </p><p>Discover the statistical challenges that plagued major experimentation platforms - from misaligned alpha thresholds and rampant false positives to the complexities of handling extremely skewed revenue metrics. Learn how I rebuilt trust in A/A testing through blind studies, applied multiple comparison corrections, and navigated the challenges of KPI engineering for metrics with distributions spanning from $1 to $8,000. This candid behind-the-scenes look into real-world experimentation reveals why understanding statistical concepts is crucial for accurate decision-making in tech. Perfect for data scientists, analysts, and product managers working with experiments and causal inference at scale.</p><p><br/></p>
]]></description>
    <content:encoded><![CDATA[<p>In this special second episode, the roles reverse as I share some stories from my extensive experimentation background across tech giants like eBay, Apple, and Yahoo. This is a cut from my first episode with Ishan where Ishan asked me about my experience. </p><p>Discover the statistical challenges that plagued major experimentation platforms - from misaligned alpha thresholds and rampant false positives to the complexities of handling extremely skewed revenue metrics. Learn how I rebuilt trust in A/A testing through blind studies, applied multiple comparison corrections, and navigated the challenges of KPI engineering for metrics with distributions spanning from $1 to $8,000. This candid behind-the-scenes look into real-world experimentation reveals why understanding statistical concepts is crucial for accurate decision-making in tech. Perfect for data scientists, analysts, and product managers working with experiments and causal inference at scale.</p><p><br/></p>
]]></content:encoded>
    <enclosure url="https://www.buzzsprout.com/2620097/episodes/19227678-statistical-pitfalls-in-tech-experimentation-my-journey-through-ebay-apple-and-yahoo.mp3" length="15833722" type="audio/mpeg" />
    <itunes:image href="https://storage.buzzsprout.com/ptrk44jtqu6ixfkhr3p7rd390k4r?.jpg" />
    <itunes:author>Pritul Patel</itunes:author>
    <guid isPermaLink="false">06a0a86b-8d7d-493b-89b9-37973e81e273</guid>
    <pubDate>Mon, 03 Mar 2025 10:13:55 -0500</pubDate>
    <itunes:duration>1317</itunes:duration>
    <itunes:keywords></itunes:keywords>
    <itunes:season>1</itunes:season>
    <itunes:episode>2</itunes:episode>
    <itunes:episodeType></itunes:episodeType>
    <itunes:explicit>false</itunes:explicit>
  </item>
  <item>
    <itunes:title>Ishan Goel - From Computer Science to Data Science Research</itunes:title>
    <title>Ishan Goel - From Computer Science to Data Science Research</title>
    <itunes:summary><![CDATA[Welcome to the first episode of Variance, a podcast where I explore experimentation, basic statistics, and data science career through conversations with industry experts. In this episode, I sit down with Ishan Goel, former Associate Head of Data Science at VWO (Visual Website Optimizer), where he led data science research and helped build VWO's new stats engine. Topics covered include: 🟣 Transition from computer science to data science 🟣 The role of experimentation in modern businesses 🟣 Sta...]]></itunes:summary>
    <description><![CDATA[<p>Welcome to the first episode of Variance, a podcast where I explore experimentation, basic statistics, and data science career through conversations with industry experts. In this episode, I sit down with Ishan Goel, former Associate Head of Data Science at VWO (Visual Website Optimizer), where he led data science research and helped build VWO&apos;s new stats engine.</p><p>Topics covered include:</p><p>🟣 Transition from computer science to data science</p><p>🟣 The role of experimentation in modern businesses</p><p>🟣 Statistical concepts and methodologies</p><p>🟣 Career advice for aspiring data scientists</p><p>🟣 Common challenges in A/B testing</p><p><br/></p><p>Connect with Ishan:LinkedIn: https://www.linkedin.com/in/i-goel/Weekly Experimentation Webinars: Every TuesdayUpcoming Course on Experimentation Leadership (stay tuned)Connect with Pritul:LinkedIn: https://www.linkedin.com/in/pritul-patel</p><p><br/></p>
]]></description>
    <content:encoded><![CDATA[<p>Welcome to the first episode of Variance, a podcast where I explore experimentation, basic statistics, and data science career through conversations with industry experts. In this episode, I sit down with Ishan Goel, former Associate Head of Data Science at VWO (Visual Website Optimizer), where he led data science research and helped build VWO&apos;s new stats engine.</p><p>Topics covered include:</p><p>🟣 Transition from computer science to data science</p><p>🟣 The role of experimentation in modern businesses</p><p>🟣 Statistical concepts and methodologies</p><p>🟣 Career advice for aspiring data scientists</p><p>🟣 Common challenges in A/B testing</p><p><br/></p><p>Connect with Ishan:LinkedIn: https://www.linkedin.com/in/i-goel/Weekly Experimentation Webinars: Every TuesdayUpcoming Course on Experimentation Leadership (stay tuned)Connect with Pritul:LinkedIn: https://www.linkedin.com/in/pritul-patel</p><p><br/></p>
]]></content:encoded>
    <enclosure url="https://www.buzzsprout.com/2620097/episodes/19227679-ishan-goel-from-computer-science-to-data-science-research.mp3" length="39991184" type="audio/mpeg" />
    <itunes:image href="https://storage.buzzsprout.com/qptxu8xgdfvtx8diyd52ihwqjlnj?.jpg" />
    <itunes:author>Pritul Patel</itunes:author>
    <guid isPermaLink="false">26560146-8806-4105-905a-9491c159a356</guid>
    <pubDate>Fri, 07 Feb 2025 14:08:07 -0500</pubDate>
    <itunes:duration>3330</itunes:duration>
    <itunes:keywords></itunes:keywords>
    <itunes:season>1</itunes:season>
    <itunes:episode>1</itunes:episode>
    <itunes:episodeType></itunes:episodeType>
    <itunes:explicit>false</itunes:explicit>
  </item>
</channel>
</rss>
