Snacks Weekly on Data Science

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 he...

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 you...

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,...

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 b...

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...

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://m...

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...

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 ho...

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 mor...

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...

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-t...

In this episode, we explore the concept of out-of-app marketing and its importance for businesses like Uber. We'll discuss the challenges involved in personalizing out-of-app marketing messages, the essential components of their recommendation architecture, and how the team created customized solutions to improve personalization and relevance in their recommendations. For more details, you can...

In this episode, we will discuss the importance of ETA for ridesharing apps and the challenges of providing a reliable ETA to users upfront. We delved into the practices of the machine learning team at Lyft, examining how they developed a solution to address this unique challenge using a lightweight machine learning model. For more details, you can refer to their published tech blog, linked here f...

In this episode, we will explore how Yelp navigates the challenges of incorporating video reviews into its platform. We will discuss the use of machine learning to detect inappropriate content and the strategies to maintain the quality and integrity of its platform. For more details, you can refer to their published tech blog, linked here for your reference: https://engineeringblog.yelp.com/2024/0...

In this episode, we will introduce what a brand is and why measuring brand perception is crucial. We will discuss how social media data can be used to achieve this and look at how Airbnb leverages deep learning methods and word embedding technologies to quantify brand perception more effectively. For more details, you can refer to their published tech blog, linked here for your reference: https://...

In this episode, we will explore the concept of quasi-experimentation and its role in Instacart's decision-making process. We will take a closer look at one specific methodology, regression discontinuity design, explaining its key concepts and demonstrating how it works through an interesting example. For more details, you can refer to their published tech blog, linked here for your reference:...

In this episode, we will discuss the machine learning architecture built by LinkedIn for their premium product recommendation. We will explore the machine learning architecture, which includes a two-towered neural network and reinforcement learning as key components. For more details, you can refer to their published tech blog, linked here for your reference: https://www.linkedin.com/blog/engineer...

In this episode, we will discuss why search relevance is important for Faire, how their data team quantifies semantic relevance in search, and how they leverage large language models (LLMs) to measure it efficiently. For more details, you can refer to their published tech blog, linked here for your reference: https://craft.faire.com/fine-tuning-llama3-to-measure-semantic-relevance-in-search-86a7b1...

In this episode, we will touch on the importance of A/B testing in the product decision-making process. We will share the four types of metrics and the decision-making framework used by the Data Science team at Spotify, as well as the necessary statistical adjustments that need to be incorporated into experimentation to ensure a solid statistical foundation. For more details, you can refer to thei...

In this episode, we will discuss the importance of Estimated Time of Arrival (ETA) for DoorDash and how the company enhanced its machine learning model through three key directions: upgrading from a tree-based model to a deep-learning architecture, adopting a multi-task modeling approach, and leveraging probabilistic models. For more details, you can refer to their published tech blog, linked here...

In this episode, we will discuss the intricacy of balancing both the art and science aspects in forecasting. We will explore this through two key aspects: validation of forecasting and integrating product impact into forecasting, where combining the art and science can be crucial to enhancing forecasting performance. For more details, you can refer to their published tech blog, linked here for you...

In this episode, we will discuss what is feature selection and how JustEatTakeaway.com leverages generic algorithms as one practice to to optimize their feature selection. For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/justeattakeaway-tech/optimising-feature-selection-with-genetic-algorithms-an-easy-to-use-python-script-dde44cc9c053...

In this episode, we will discuss how to build an effective monitoring mechanism for a recommendation system. We will cover the basic components of various recommendation processes and explore how the Tubi TV Engineering team developed their monitoring flow. For more details, you can refer to their published tech blog, linked here for your reference: https://code.tubitv.com/how-to-monitor-a-recomme...

In this episode, we will introduce the concept of causal inference, and discuss how the data scientist team from Walmart determines Causal Effects when A/B Tests are Infeasible through Adopter Analysis. Based on their published tech blog, with the link provided here for your reference:  https://medium.com/walmartglobaltech/how-to-determine-causal-effects-when-a-b-tests-are-infeasible-through-adopt...

In this episode, we will discuss the importance of metrics for the business. We will share the journey of how Indeed started with a single metric to measure client-side performance, and eventually converged into a composite metric serving as a comprehensive measure Based on their published tech blog, with the link provided here for your reference:  https://engineering.indeedblog.com/blog/2024/01/c...