Eugene Yan · Tech & AI
TIER 4 2020-02-27
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<h1 class="title">My Journey from Psych Grad to Leading Data Science at Lazada</h1>
<p class="date">
<info datetime="2020-02-27 00:00:00 +0000">
<span class="no-italics">[
<a class='tag' href="/tag/career/">career</a>
<a class='tag' href="/tag/lazada/">lazada</a>
<a class='tag' href="/tag/🔥/">🔥</a>
]
</span> · 12 min read
</info>
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<div class="notebody">
<p>Many people are curious about how I got into the field of data science with a Psychology degree. They’re curious how I made the <em>first</em> step.</p>
<p>Then, they find out I was leading the data science team at Lazada and they’re interested in how that happened. (Spoiler alert: A lot hard work <em>and</em> some luck.)</p>
<p>In a world where almost every data science leader has a technical Ph.D. (or two), I’m unusual. An <em>anomaly</em>. I guess this is why people are curious about my story. There’s perhaps also a bit of “underdog” dust in it.</p>
<p>Enough people (10+) have asked to write this down—it’s just more scalable. I’ve included some key anecdotes and also distilled some lessons at the end.</p>
<p>TL; DR: (i) self-learning, (ii) delivering value, and (iii) effective communication.</p>
<blockquote>
<p>As much as I try not to appear boastful, my intent is to share what I’ve learned and help others get into and be successful in this field.</p>
</blockquote>
<iframe class="centered-iframe" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6640045181544562688" height="160" width="504" frameborder="0" allowfullscreen="" title="Embedded post"></iframe>
<p><br /></p>
<p class="image-caption">Update: Here's what my ex-boss at Lazada had to say about it</p>
<h2 id="before-i-got-into-data-science">Before I got into data science</h2>
<p>Having just graduated with my degree in Psychology and Organisation Behaviour, I was unsure of what to do next. I’m a Social Science graduate! I could do anything! Or I could do nothing.</p>
<p>I eventually got a role as an Investment Analyst for the government. I expected to use data to make better investment decisions. Which countries to invest in, which industries to focus on. However, it was mostly negotiation and legal work on Free Trade Agreements. I got bored.</p>
<p>Out of curiosity, I started taking <a href="/speaking/how-to-get-started-in-data-science-talk/" target="_blank">online courses</a> (Coursera and EdX are great!) Those early data courses very super interesting, and convinced me that I was made for something greater. Or at least different. I <em>had</em> to get a data-related job.</p>
<p>Luckily, IBM had a program to hire and train a cohort of mid-career professionals for data-related roles. It seemed like a long shot, but I decide to send in my resume anyway.</p>
<p>Surprisingly, I got invited for interviews. (I didn’t have the right education or experience; I wouldn’t have considered myself!) Some questions on statistics, SQL, R, most of which I picked up through online courses. It was a gift from above when they sent me an offer.</p>
<p>The 33% pay cut was tough to swallow. But I was pretty bored (read: miserable) as an Investment Analyst after two years, so I accepted.</p>
<h2 id="my-first-step-into-the-field">My first step into the field</h2>
<p>Day 1 was a shock. Of the 20 people hired, 18 had technical degrees, of which 10 were PhDs. The remaining two had a Psychology degree (me) and an economics degree. I was the <strong>most non-technical</strong> in the group.</p>
<p><img style="width: 300px" src="/assets/not-trained.webp" loading="lazy" alt="Image" /></p>
<p class="image-caption">How I felt on the first day (from: https://www.nathanwpyle.art/)</p>
<p>Why did IBM hire me? I’d like to think it was my charm, but my conscience says “no way!”. Thus, I offer three <em>more</em> rational reasons.</p>
<ul>
<li>I met the minimum technical bar (thanks to the free courses)</li>
<li>My government experience was useful in my consultant role</li>
<li>A bit of luck</li>
</ul>
<p>The first year saw me working as a junior consultant, building supply chain dashboards, doing social media monitoring, etc.</p>
<p>In year two I was internally transferred to the workforce analytics team, working to forecast job demand and build a job recommendation engine to move people within the organization.</p>
<p>On the side, I continued self-learning. I picked up Python (love it) and took classes in machine learning. Spark, a shiny (pun intended) big data framework was emerging and provided free courses on EdX—I devoured these too.</p>
<h2 id="serendipity">Serendipity</h2>
<p>With my new-found skills in Python and machine learning, I was looking for an opportunity to practice. It was then I stumbled into my first Kaggle competition.</p>
<p>The <a href="https://www.kaggle.com/c/otto-group-product-classification-challenge" target="_blank">Otto Product Classification challenge</a> had participants building machine learning models to classify 200k products into 9 categories. I took part and eventually teamed up with a fellow competitor towards the end. Our final submission was an ensemble of gradient boosted trees and neural networks that did well enough to rank in the top 3% of 3500+ teams. Not bad for a first attempt.</p>
<p>I thought Kaggle was the best thing since the iPhone. It was a great place to practice, learn from the best in the world, and get immediate feedback. I was excited to share my experience and learnings. When an opportunity came along to present at a <a href="https://www.meetup.com/en-AU/DataScience-SG-Singapore/" target="_blank">data science meet-up group</a> for a <a href="https://www.meetup.com/en-AU/DataScience-SG-Singapore/events/223005406/" target="_blank">personal-project themed meetup</a>—I was happy to oblige.</p>
<p>80+ people showed up that <a href="/speaking/dssg-kaggle-top-3-percent-talk/" target="_blank">Saturday afternoon in June</a>. Mind-blown. Who are these people and don’t they have better things to do <strong>on a Saturday</strong>?! They were generous with their attention and feedback, and I learned as much from them as taking part in the competition.</p>
<p>Unbeknownst to me, <em>Serendipity</em> also attended the meet-up. And she brought along a <a href="https://sg.linkedin.com/in/thiakx" target="_blank">friend</a> in the audience.</p>
<blockquote>
<p>“You make your own luck” - Ernest Hemingway</p>
</blockquote>
<p>Word about my talk travelled. At another <a href="https://www.meetup.com/en-AU/forwardleague/events/222953207/" target="_blank">data meet-up</a> in June, I got acquainted with <a href="https://www.linkedin.com/in/jfxberns" target="_blank">the organizer</a>. He was starting the data team at Lazada and got to know about my sharing.</p>
<h2 id="opportunity-comes-knocking">Opportunity comes knocking</h2>
<p><a href="https://en.wikipedia.org/wiki/Lazada_Group" target="_blank">Lazada</a> was a start-up launched in 2012, backed by Germany’s Rocket Internet. It was bringing the convenience of e-commerce to Southeast Asia’s population. Achieving similar success to Amazon and Alibaba would have been nice too. (Acquired by Alibaba in 2016.)</p>
<p>They were struggling with accurate product categorization and had heard about my sharing on the Kaggle competition.</p>
<p>I was invited to Lazada for a “chat”. It started with a quick run-through of my meet-up presentation to the Head of Data Science (John Berns) and his boss, the CIO (Klemen Drole). The rest of the team (three people, <em>including one person who attended my meetup</em>) were also kind enough to take me out for lunch.</p>
<blockquote>
<p>I left wondering if I had been through an interview, and secretly hoped I did well.</p>
</blockquote>
<p>They called to offer a position the next day.</p>
<p>I was hesitant to leave a venerable, branded company like IBM (in hindsight, this is hilarious). Lazada was an unknown start-up with a data team of three people. My family and closest friends thought it was risky. However, deep down, I knew I would <a href="https://www.inc.com/jessica-stillman/jeff-bezos-this-is-how-to-avoid-regret.html" target="_blank">regret</a> not accepting the offer.</p>
<p>(Note: Speaking brings unexpected opportunities. Here’s <a href="/writing/how-to-give-a-kick-ass-data-science-talk/" target="_blank">how to give a kick-ass talk</a>.)</p>
<h2 id="boarding-the-lazada-rocket-ship">Boarding the Lazada rocket ship</h2>
<p>Joining Lazada was one of my best decisions ever.</p>
<p><img src="/assets/rocket.webp" loading="lazy" alt="Image" /></p>
<p class="image-caption">If you're offered a seat on a rocket, don't ask what seat. Just get on</p>
<p>I got the opportunity to work with talented data scientists and engineers. We solved challenging problems and delivered a ton of value to consumers in Southeast Asia—very fulfilling.</p>
<p>My first project was (naturally) building a product classifier, similar to the Kaggle competition, with a <em>few small differences</em>.</p>
<p>Firstly, we had a couple hundred <strong>million</strong> products from <strong>thousands</strong> of categories. Second, it had to be in the form of an API for internal users and external sellers, with strict <strong>latency requirements</strong>. Third, it required <strong>continuous re-training and validation</strong> to ensure high accuracy.</p>
<p>Yeap, <em>just</em> like Kaggle.</p>
<blockquote>
<p>The learning curve was steep. Pushed me 2x beyond my existing abilities. Exhilarating.</p>
</blockquote>
<p>Thankfully, awesome data engineers taught me how to scale and make systems more robust. With a bit of (read: 10+ hours weekly) self-tinkering on weekends, I delivered in a couple of months.</p>
<p>This <strong>earned trust</strong> from the team, my boss, and our stakeholders.</p>
<h2 id="volunteering-is-unlucky">Volunteering is (un)lucky</h2>
<p>As I wrapped up my product classifier, two senior data scientists were working on email recommendations. However, they had a cold-start problem. A big portion of customers didn’t have enough activity to provide meaningful recommendations.</p>
<p>I volunteered to help by <strong>ranking products</strong> based on historical performance (e.g., clicks, purchases) and metadata (e.g., price, ratings, etc.). The top products would be recommended to these cold-start customers. Simple and manageable.</p>
<p>We presented our plan to senior management and it went well. Too well.</p>
<blockquote>
<p>“That’s a great idea! Why not extend product ranking to our entire website?, they asked.”</p>
</blockquote>
<p><em>Gulp.</em></p>
<p>My early efforts using Python and pandas were futile. Processing a single day’s data took 2 hours—a month’s data would take 2.5 days. “Welcome to big data”, a data engineer chuckled.</p>
<p>Thankfully, I had picked up Spark from online courses (thank you DataBricks and EdX). It provided a good foundation and helped me ramp up faster than most.</p>
<p>After a couple of months, it was ready for AB testing. I started the AB test, casually mentioned it to my boss over drinks (<strong>big mistake</strong> on hindsight), and slept like a baby the next couple of days.</p>
<h2 id="how-i-lost-millions-on-my-first-ab-test">How I lost millions on my first AB test</h2>
<p><img src="/assets/throwing-money.webp" loading="lazy" alt="Image" /></p>
<p class="image-caption">Nope, I wasn't as happy as this guy looks</p>
<blockquote>
<p>“What’s this AB test you launched and why did it lose millions in revenue last week?”, my boss asked over Slack.</p>
</blockquote>
<p>I found out later he was getting hammered at a meeting with senior management.</p>
<p>Panicked, I starting triaging across the AB testing tool, Airflow, validation metrics, etc. Within a couple of hours, I came up with a few hypotheses and how to validate them, and a plan for the next iteration of experiments.</p>
<p>After incorporating feedback, I started working on it day and night. In about two weeks, we launched another AB test. This time, I did not sleep as peacefully.</p>
<p>Thankfully, we achieved positive results over the next rounds of AB testing. Improving the timeliness of data (e.g., behavioral logs, transaction data) and simplifying the model to reduce overfitting did the trick.</p>
<blockquote>
<p>The failure and embarrassment was very public. But so was the recovery and success.</p>
</blockquote>
<p>This helped <strong>improve the data science team’s standing within the organization</strong>.</p>
<h2 id="plateauing-and-a-new-focus">Plateauing and a new focus</h2>
<p>After a few projects, things were getting less scary and more comfortable. Easy even. <em>This meant my growth was slowing.</em></p>
<p><img src="/assets/plateau.webp" loading="lazy" alt="Image" /></p>
<p class="image-caption">Progress is not linear, but a series of spikes and plateaus</p>
<p>I tried various ways to figure out “what” and “how” to continue learning. This included reaching out to experts I admired. They were generally 2 - 3 levels above me. Then, I sent a casual email offering to buy them coffee, or politely requesting a Skype chat, indicating my willingness to learn from their experience. Unexpectedly, everyone I reached out to replied positively.</p>
<p>I knew it was important to be good at programming, math, statistics, machine learning, business, etc. <em>“Which should I prioritize?”</em>, I asked.</p>
<blockquote>
<p>“Those skills are important. But you’re missing the most essential (and transferable) skill—communication”, they responded.</p>
</blockquote>
<p>You’re joking, right? Talking is the most important? (How naive I was back then.) Nonetheless, I decided to take their advice and deliberately focused on improving my communication.</p>
<p>At meetings with business stakeholders, I avoided data science jargon such as “area-under-curve (AUC)”, “recall”, “mean squared error”. Instead, I used their terms: “instances of fraud identified and savings”, “increase in conversion and revenue”, “reduction in late deliveries and complaints”.</p>
<p>I needed practice too. The data team wanted to launch an internal newsletter to spread awareness—I volunteered. Someone had to visit overseas markets to do a data science roadshow—I volunteered. We wanted to present our work at conferences—<a href="/speaking/shared-about-my-work-in-lazada-at-strata-hadoop-singapore-2016-talk/" target="_blank">I volunteered</a>.</p>
<p>Practice, practice, practice.</p>
<h2 id="metamorphosis">Metamorphosis</h2>
<p>Another year flew by. I focused on delivering small but impactful projects and improving my communication.</p>
<blockquote>
<p>Around that time, I was <strong>promoted twice in a year</strong>.</p>
</blockquote>
<p>The first—to senior data scientist—was probably some time after the product ranking success and a few other wins.</p>
<p>Then, I moved up to VP of Data Science, leading the team of 12+ data scientists.</p>
<p><img src="/assets/level-up.gif" loading="lazy" alt="Image" /></p>
<p class="image-caption">It takes a while, but eventually you'll level up</p>
<h2 id="how-did-i-achieve-this">How did I achieve this?</h2>
<p><strong>Continuous self-learning</strong> played a big role, especially in <em>getting into the field</em>. In almost all my roles and projects, I had to learn on my own—outside my job scope—to be barely qualified. This opened up increasingly challenging opportunities.</p>
<blockquote>
<p>But once in the field, there were people with better educational qualifications and more experience—why me?</p>
</blockquote>
<p>It can’t be <em>just</em> luck. I couldn’t figure it out and decided to ask my boss and other senior leaders. Here’s what I gathered:</p>
<p>I was hungry and <strong>got shit done</strong>. The measurable value I created was 3x that of an average data scientist. This was how they justified it to senior leaders and HR.</p>
<p>In addition, <strong>non-technical</strong> stakeholders could <strong>communicate</strong> with me. Often, after stakeholders had a meeting with a data scientist, they (stakeholders) left not knowing what the data scientist was saying, or where the project was headed. (Sad, but true).</p>
<p>I was promoted to be a role model and to mentor the team to deliver and communicate better.</p>
<h2 id="that-was-a-long-readcould-you-summarise-please">That was a long read—could you summarise, please?</h2>
<p>Okay, so here’s what I did:</p>
<ul>
<li>Self-learning and practice, which led to…</li>
<li>My first data role, and more learning, which led to…</li>
<li>Taking part in a Kaggle competition, which led to…</li>
<li>Volunteering to share at a meet-up, which led to…</li>
<li>Being invited for a chat and lunch, which led to…</li>
<li>Joining a startup, learning lots, and mentors, which led to…</li>
<li>Focusing on communication, which led to…</li>
<li>Greater effectiveness and growth</li>
</ul>
<h2 id="parting-words">Parting words</h2>
<p>I started this post to share my journey. The aim was to clear up the misconception that a technical degree was required to enter the field, much less achieve success in it. Hopefully, you’re convinced.</p>
<p>The post ended with a reflection on three keys for success as a data scientist—based on my experience—namely: (i) continuous self-learning, (ii) get shit done, and (iii) emphatic communication.</p>
<div class="note-twitter">
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">My journey from psych degree into data science (thread):<br /><br />My first job was as an investment analyst for the govt.<br /><br />Took online courses out of curiosity and fell in love with data.<br /><br />Applied for entry-levels roles—it was a long shot.<br /><br />Got invited to interview with IBM. (1/6)👇</p>— Eugene Yan (@eugeneyan) <a href="https://twitter.com/eugeneyan/status/1254552466235854849?ref_src=twsrc%5Etfw">April 26, 2020</a></blockquote> <script async="" src="https://platform.twitter.com/widgets.js" charset="utf-8" type="7150d404e72c919b025cf725-text/javascript"></script>
</div>
<p><br />
<strong>Thanks</strong> to Xinyi Yang, Gabriel Chuan, and Chng Yee Siang for reading drafts of this.</p>
<br>
<p>If you found this useful, please cite this write-up as:</p>
<blockquote class="blockquote-citation">
<p>Yan, Ziyou. (Feb 2020). My Journey from Psych Grad to Leading Data Science at Lazada. eugeneyan.com.
https://eugeneyan.com/writing/psych-grad-to-data-science-lead/.</p>
</blockquote>
<p>or</p>
<div class="citation"><pre><code>@article{yan2020psych,
title = {My Journey from Psych Grad to Leading Data Science at Lazada},
author = {Yan, Ziyou},
journal = {eugeneyan.com},
year = {2020},
month = {Feb},
url = {https://eugeneyan.com/writing/psych-grad-to-data-science-lead/}
}</code></pre>
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border: 1px solid color-mix(in srgb, var(--c-background) 85%, var(--c-text) 15%); /* Theme-aware light grey border */
padding: 0; /* Remove overall card padding, will be handled by elements */
text-align: left; /* Or 'center' if you prefer */
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/* Styling for images within recommendation items */
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display: block; /* Can help remove extra space below image */
width: calc(100% - 4px); /* Full width minus 2px L/R margins */
max-width: 100%; /* Ensures image does not exceed container if intrinsically smaller */
/* height: auto; -- Controlled by inline style's max-height and object-fit */
object-fit: cover; /* Ensure image covers the area, also in inline style */
margin: 2px; /* 2px margin on top, left, right. Bottom is overridden by inline style. */
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font-size: 0.75em;
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line-clamp: 2; /* Standard property */
-webkit-box-orient: vertical;
overflow: hidden;
text-overflow: ellipsis;
padding: 0 7px 7px 7px; /* 0 top, 7px L/R/B for text area */
line-height: 1.5; /* Adjust for better readability */
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color: var(--c-interactive); /* Use theme's interactive color */
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/* This container will wrap the image and score, taking the original image's layout space. */
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display: block; /* Matches original image display and ensures proper block layout */
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margin: 2px; /* Adopts margin from original image styling */
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height: auto; /* Maintain aspect ratio by default */
max-height: 12em; /* Constrain image height (adjust as needed) */
object-fit: cover; /* Ensures image covers the allocated space, cropping if necessary */
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position: absolute;
bottom: 3px; /* Padding from the bottom edge of the container */
right: 3px; /* Padding from the right edge of the container */
background-color: color-mix(in srgb, var(--c-background) 85%, var(--green) 15%); /* Theme-aware light green */
color: var(--green); /* Theme's green color for text */
padding: 3px 6px; /* Slightly adjusted padding */
font-family: 'Raleway', Helvetica, sans-serif;
font-size: 0.75em;
font-weight: bold;
border-radius: 10px; /* More rounded corners like the example */
border: 1px solid var(--green); /* Theme's green color for border */
line-height: 1; /* Critical for small text in a small box */
z-index: 10; /* Ensure it's above the image */
box-shadow: 0 1px 2px rgba(0,0,0,0.15); /* Softer shadow */
display: flex; /* To align icon and text nicely */
align-items: center; /* Vertically center icon and text */
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/* Styling for the SVG icon within the score box */
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width: 0.9em; /* Scale with score's font size */
height: 0.9em;
vertical-align: -0.1em; /* Fine-tune vertical alignment */
margin-right: 4px; /* Space between icon and score number */
fill: var(--green); /* Theme's green color for icon */
}
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.then(() => {
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const recSearchClient = algoliasearch(
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const relatedSearch = instantsearch({
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searchClient: recSearchClient,
clickAnalytics: true,
insights: true, // Enable insights for click tracking on recommendations
});
relatedSearch.addWidgets([
instantsearch.widgets.relatedProducts({
container: '#algolia-related-products',
objectIDs: ['/writing/psych-grad-to-data-science-lead/'],
limit: 3,
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attributesToHighlight: [], // Disable highlighting
attributesToSnippet: [] // Disable snippeting
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title: '', // Custom title is in _layouts/post.html
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templates: {
header() { // Removed unused results, html parameters
// Return a PLAIN string for the header
return '<h4 class="algolia-recs-section-header">You Might Also Like</h4>';
},
item: function(hit, { html, sendEvent }) { // Added sendEvent to params
const itemUrl = `${hit.url || '#'}`;
const indexName = 'eugeneyan.com'; // Get index name for insights
let imageUrl;
// Ensure hit.image is not null, undefined, or an empty/whitespace string before using it.
if (hit.image && typeof hit.image === 'string' && hit.image.trim() !== '') {
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let scoreValue = null;
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const scoreElement = scoreValue ? `<div class="recommendation-score"><svg viewbox="0 0 24 24" class="recommendation-score-icon" xmlns="http://www.w3.org/2000/svg"><path d="M16 6l2.29 2.29-4.88 4.88-4-4L2 16.59 3.41 18l6-6 4 4 6.3-6.29L22 12V6h-6z"></path></svg>${scoreValue}</div>` : '';
const imageAndScoreTag = `
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const title = hit.title || 'Untitled Post';
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containerElement.style.display = 'none';
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}
})
]);
relatedSearch.start();
}
document.addEventListener('DOMContentLoaded', function() {
let recsLoaded = false;
function checkLoad() {
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initAlgoliaRecommendations();
}
}
window.addEventListener('scroll', checkLoad, { passive: true });
checkLoad();
});
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</div> -->
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<p>I'm a Member of Technical Staff at Anthropic. I work to bridge the field and the frontier, and help build safe, reliable AI systems that scale. I've led ML/AI teams at Amazon, Alibaba, Lazada, and a Healthtech Series A, and write about LLMs, RecSys, and engineering at <a href="https://eugeneyan.com/" target="_blank">eugeneyan.com</a>.</p>
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