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Show Notes

• (2:19) Eugene got his Bachelor’s degree in Psychology and Organizational Behavior from Singapore Management University, in which he did a senior thesis titled “Competition Improves Performance.”
• (3:29) Eugene’s first role out of school is an Investment Analyst position at Singapore’s Ministry of Trade & Industry.
• (4:18) Eugene then moved to a Data Analyst role at IBM, working on projects such as supply-chain dashboards, social media analytics, and anti-money laundering detection.
• (5:55) Eugene transitioned to an internal Data Scientist role at IBM, working on job forecasting and job recommendations.
• (9:03) Eugene shared the story of how he became a Data Scientist at Lazada Group, which was a small e-commerce startup back in 2015.
• (12:08) Eugene explained his decision to go back to school and pursued an online Master’s degree in Computer Science at Georgia Tech.
• (19:14) Eugene shared his career milestones, as displayed in his blog post reflecting on his journey from getting a degree in Psychology to leading data science at Lazada.
• (22:17) Eugene discussed the unique data science challenges while working at uCare.ai - a startup that aims to make healthcare more efficient and reduce costs.
• (25:29) Eugene revealed three useful tips to deliver great data science talks (read his blog post “How to Give a Kick-Ass Data Science Talk” for the details).
• (28:29) Eugene talked about his transition to become an Applied Scientist at Amazon - working on Amazon Kindle.
• (30:43) Eugene unpacked his post “Commando, Soldier, Police, and Your Career Choices” that provides an interesting metaphor to help guide career decisions.
• (33:43) Eugene went meta onto his writing process (read here) and note-taking strategy (read here).
• (39:01) Eugene shared the lessons learned from taking on responsibilities in hiring, mentoring, and stakeholder engagement in his second year at Lazada (read his blog post on the first 100 days as a Data Science Lead).
• (44:20) Eugene went in-depth into the engineering and cultural challenges throughout Alibaba Group’s acquisition of Lazada Group.
• (47:51) Eugene explained Alibaba’s playbook for the technical integration of their acquisitions and the super-apps phenomenon in Asia (check out a summary of his talk on Asia’s Tech Giants).
• (53:52) Eugene unpacked the values and essential aspects of Lazada’s data science team culture, as detailed in his post “Building a Strong Data Science Team Culture.”
• (57:44) Eugene summarized his thoughts on the topic of data science and agile/scrum development (Read his 3-part blog series: Part 1, Part 2, and Part 3).
• (01:03:18) Eugene was heavily involved with the development of product ranking, product recommendations, and product classification models in his first year at Lazada (check out slides to his talk “How Lazada Ranks Products”).
• (01:09:08) Eugene helped mentor and empower teams on multiple machine learning systems while acting as VP of Data Science at Lazada (check out slides to his talk “Data Science Challenges at Lazada”).
• (01:12:07) Eugene shared the case study of how uCare.ai developed a machine learning system for Southeast Asia’s largest healthcare group that estimates a patient’s total bill at the point of pre-admission.
• (01:14:06) Eugene summarized his 2-part series that exposes the challenges after model deployment and yields a practical guide to maintaining models in production.
• (01:19:04) Eugene discussed his early-career Product Classification project that uses a public Amazon dataset and builds two APIs for image classification & image search.
• (01:22:29) Eugene discussed his 2-part series that implements seven different models on the same Amazon dataset, from matrix factorization to graphs and NLP.
• (01:24:42) Closing segment.

His Contact Info

• Website
• Twitter
• LinkedIn
• GitHub

His Recommended Resources

• Niklas Luhmann (well-known German sociologist)
• Roam Research (note-taking application)
• MLflow (A platform for ML lifecycle management)
• Amazon Product Review Dataset (big data in JSON format)
• Andrej Karpathy (Read “The Unreasonable Effectiveness of RNNs” and “A Recipe For Training Neural Networks”)
• Jeremy Howard (Read the “Universal Language Model Fine-tuning for Text Classification paper)
• Hamel Hussain (Check out GitHub Actions and fastpages)
• “Introduction to Statistical Learning” (by Trevor Hastie and Rob Tibshirani)
• “The Pragmatic Programmer” (by Andy Hunt and Dave Thomas)
• applied-ml repository
• ml-survey repository