In this New Year’s episode, I reflect on one of my earliest data science projects. My son will “interview” me about my experiences during that time. The key project discussed in this episode is also featured in a LinkedIn article I wrote a few years ago (⁠https://www.linkedin.com/pulse/my-first-data-science-project-pan-wu/⁠⁠).

I’d love for you to check it out, leave a 5-star review, and subscribe to the channel! Thank you for your support, and I wish you all a fulfilling 2024 and a very Happy New Year in 2025!

In this episode, we will explore Netflix’s approach to content recommendation using contextual bandits and reward engineering. We will also discuss the important role of proxy reward functions and how Netflix leverages offline machine learning models to predict delayed customer feedback, enabling them to continuously improve their recommendation engine and deliver a more personalized viewing experience.

For more details, you can refer to their published tech blog, linked here for your reference: https://netflixtechblog.com/recommending-for-long-term-member-satisfaction-at-netflix-ac15cada49ef

In this episode, we will introduce the concept of paid marketing. We’ll explore how Instacart’s team developed an adaptive experimentation framework that continuously balances exploration and exploitation, ultimately maximizing marketing efficiency.

For more details, you can refer to their published tech blog, linked here for your reference:  https://tech.instacart.com/bandits-for-marketing-optimization-f5a63b9bfaa7

In this episode, we will introduce what CVS Health is and the importance of product recommendations for their business needs. We will delve into how their data science team leveraged advanced technologies, including Graph Neural Networks and generative AI models like GPT-4, to develop a prototype system to make product bundle recommendations.

For more details, you can refer to their published tech blog, linked here for your reference: 
https://medium.com/cvs-health-tech-blog/ai-in-cvs-front-store-e-commerce-building-a-complementary-product-bundle-recommender-dc6c71dee682

In this episode, we will introduce the issue of data imbalance and its impact on machine learning models, especially for text data. We will discuss a range of augmentation techniques and walked through how Walmart’s data science team built an automated augmentation module to apply these techniques consistently and effectively.

For more details, you can refer to their published tech blog, linked here for your reference:  https://medium.com/walmartglobaltech/augmentation-techniques-for-imbalanced-text-classification-f0d29c0f8ce1

In this episode, we will discuss what is Instamart, its business model, and the need to optimize operational efficiency continuously. We will explore how the team tackled picker assignment issues, tested a multi-order batching solution, and applied modeling techniques to enhance Instamart’s speed and service quality.

For more details, you can refer to their published tech blog, linked here for your reference: 
https://bytes.swiggy.com/optimizing-the-picking-process-to-enable-faster-deliveries-for-instamart-93de0fe9d819

In this episode, we introduce GovTech Singapore and its reasons for tackling the content moderation problem. We discuss their innovative approach to building the moderation classifier, which involves using a consensus voting mechanism with existing commercial LLMs to improve labeling in the training dataset, providing a strong foundation for developing the machine learning model.

For more details, you can refer to their published tech blog, linked here for your reference: 
https://medium.com/dsaid-govtech/building-lionguard-a-contextualised-moderation-classifier-to-tackle-local-unsafe-content-8f68c8f13179

In this episode, we will introduce Afresh, explore the concept of demand forecasting, and discuss the critical role of promotions in grocery forecasting. We will also examine Afresh’s technical solution, which combines deep learning with a carefully curated set of data inputs to create an accurate, promotion-aware demand forecasting system.

For more details, you can refer to their published tech blog, linked here for your reference: 

https://medium.com/afresh-engineering/buy-one-get-one-free-promotion-aware-demand-forecasting-for-groceries-857838c6871f

In this episode, we will explore the business model of Booking.com and the unique challenges it faces in preventing fraud. We will discuss how graph technology can enhance fraud detection by representing data through relationships and how combining this with machine learning enables Booking.com to detect and stop fraudulent activity in real-time.

For more details, you can refer to their published tech blog, linked here for your reference: 
https://medium.com/booking-com-development/leverage-graph-technology-for-real-time-fraud-detection-and-prevention-438336076ea5

In this episode, we will introduce a key marketing challenge—attribution—and explore how marketing mix modeling (MMM) can help solve it. We will also discuss how MMM works in practice and examined the trade-offs between using consultants for MMM development versus building an in-house solution.

For more details, you can refer to their published tech blog, linked here for your reference: 

https://medium.com/qonto-way/marketing-measurement-series-marketing-mix-modeling-at-qonto-337b8af11471