Snacks Weekly on Data Science

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

In this episode, we will explore Urban Company’s business needs and the role of causal machine learning in addressing them. We will delve into three key models—S-learner, T-learner, and X-learner—and used a simple example to illustrate how each one works. These models provide valuable insights by offering more accurate estimates of cause-and-effect relationships, helping companies make better data-driven decisions.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/uc-engineering/how-urban-company-leverages-causal-inference-to-power-data-driven-decisions-a339cfcaa0e2

In this episode, we will explore how Faire tackled the challenge of product categorization. They initially used the K-nearest neighbor algorithm with CLIP embeddings, which improved categorization but still required manual corrections. To further enhance accuracy, the team fine-tuned a vision-language model using their in-house dataset, increasing accuracy significantly. This solution showcases how advanced machine learning can drive business efficiency.

For more details, you can refer to their published tech blog, linked here for your reference: https://craft.faire.com/advancing-product-categorization-with-vision-language-models-the-power-of-fine-tuned-llava-2f4bf024a102

In this episode, we will discuss why Walmart relies on online experimentation to drive data-driven decisions and how reducing variance is a crucial challenge to making these experiments more efficient. We will also introduce the CUPED methodology, explaining how Walmart leverages it to speed up its experimentation process, enabling faster, more reliable insights for continuous improvement.

For more details, you can refer to their published tech blog, linked here for your reference: 
https://medium.com/walmartglobaltech/how-walmart-leverages-cuped-and-reduces-experimentation-lifecycle-eae2446c2ee4 

In this episode, we will explore the challenge Netflix faces in building machine learning models for video understanding. We will examine Netflix’s solution—a self-service system with active learning that empowers video experts to participate in creating and refining machine learning classifiers through a streamlined, three-step process.

For more details, you can refer to their published tech blog, linked here for your reference: 
https://netflixtechblog.com/video-annotator-building-video-classifiers-using-vision-language-models-and-active-learning-8ebdda0b2db4

In this episode, we explore the concept of incrementality in marketing, and how the data science team at Expedia leverages geo-testing to successfully measure their marketing campaign’s  incrementality 

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/expedia-group-tech/measuring-marketing-success-the-power-of-incrementality-and-geo-testing-1acd4291545d