Snacks Weekly on Data Science

In this episode, we explore how CVS Health builds its product recommendation system to deliver relevant, timely suggestions across millions of customers and thousands of products. We look at the business motivation behind personalization at CVS, and then walk through how the team uses Word2Vec, Euclidean distance, LLM-generated product summaries, and iterative refinement to improve the system step...

In this episode, we explore how Airtable built AI Agents—a system that lets users automate workflows using natural language. We examine the business motivation behind making automation more accessible and break down the technical architecture that ensures these agents are safe, reliable, and tightly integrated into Airtable’s platform. For more details, you can refer to their published tech blog,...

In this episode, I share a few key observations and reflections drawn from the tech blogs I read throughout 2025. The themes include the rise of real-world LLM applications, a move toward deeply customized machine learning solutions, and the evolving skill sets in data and AI, with continuous learning becoming more important than ever. I’d also like to express my sincere appreciation to everyone w...

In this episode, we explore how Lyft identified the right algorithmic approach for building a real-time spatial-temporal forecasting system. The team evaluated two major model families for this task: classical time-series models and deep neural networks. This study highlights the balance between accuracy and practicality—and serves as a valuable guide for choosing machine learning solutions that t...

In this episode, we explore how Uber tackled the challenge of processing an enormous volume of invoices that vary widely in layout, language, and quality. We break down how generative AI plays a central role in helping them build a more flexible and scalable document-processing system. By combining OCR, LLM-based extraction, and a thoughtful human-in-the-loop workflow, Uber created a platform that...

In this episode, we will explore how Walmart's Engineering team tackled the challenge of optimizing web performance at scale: they set top-line targets, moved from server-centric metrics to user-centric ones like Core Web Vitals, integrated these measures into their experimentation framework, and ultimately drove measurable business impact through improved engagement and organic traffic.  For...

In this episode, we explore how Pinterest tackled one of the toughest challenges in large-scale analytics — understanding why metrics move. We discuss how their engineering team built a root cause analysis platform that combines Slice and Dice, General Similarity, and Experiment Effects, with each component addressing a different part of the problem. This system brings together analytics, statisti...

In this episode, we explore how Airbnb improved search ranking for its map interface — a challenge that sits at the intersection of user behavior, design, and data science. From assuming uniform attention to modeling tiered and spatial attention, Airbnb’s team systematically refined how users interact with map results. This work shows how aligning user attention with booking likelihood can drive r...

In this episode, we explore how Instacart leverages machine learning to suggest smart replacements for out-of-stock products — a challenge that’s central to the grocery delivery experience. We dive into Instacart’s two-model approach, where a deep learning model uncovers general product relationships across the catalog, and an engagement model learns from customer behavior to personalize those rec...

In this episode, we explore the importance of covariate selection in causal inference and how different types of variables can influence the results. The discussion highlights why careful covariate selection is essential for generating reliable insights and enabling smarter, evidence-based business decisions. For more details, you can refer to their published tech blog, linked here for your refere...

In this episode, we explore how Klaviyo used counterfactual learning and uplift modeling to move beyond the question of which treatment works — to the deeper question of for whom it works. We’ll see how the team combined randomized experiments, causal inference techniques, and uplift modeling to power a product that helps marketers deliver smarter, more personalized messages. For more details, you...

In this Halloween special episode, we explore some fun facts and surprising data behind these festive favorites: Did you know Illinois is the top pumpkin-producing state, harvesting nearly 40% of all pumpkins in the U.S.? Or that Reese’s Peanut Butter Cups consistently rank as America’s most popular Halloween candy? And that over — or at least — 20% of pet owners now dress up their pets for Hallow...

In this episode, we explore how Whatnot improved its feed ranking system by moving from batch predictions to online inference—enabling the platform to scale effectively while capturing real-time marketplace dynamics. This evolution reflects a broader shift in recommendation systems toward more adaptive, real-time personalization. For more details, check out the full tech blog from the Whatnot engi...

In this episode, we explore Tripadvisor’s self-serve experimentation platform for marketing. On the business side, the challenge was measuring campaign effectiveness in a messy, external environment where clean randomization isn’t always possible. On the technical side, the TripAdvisor team developed a system that applies causal inference techniques—particularly the difference-in-differences metho...

In this episode, we look at how Meta addressed the challenge of feature selection at scale through Global Feature Importance—a system that aggregates insights across models to surface the most valuable features. This approach not only streamlines model development but also enables machine learning engineers to iterate more effectively and build models that deliver stronger business impact. For mor...

In this episode, we explore how to evaluate the retrieval-augmented generation (RAG) capabilities of small language models. On the business side, we discuss why RAG, long context windows, and small language models are critical for building scalable and reliable AI systems. On the technical side, we walk through the Needle-in-a-Haystack methodology and discuss key findings about retrieval performan...

In this episode, we explore how Netflix enhanced recommendation personalization using foundation models. These models can process massive user histories through tokenization and attention mechanisms, while also addressing the cold-start problem with hybrid embeddings. The work highlights how principles from large language models can be adapted to build more effective recommendation systems at scal...

In this episode, we explore how Vanguard evaluated standard A/B testing against multi-armed bandits for digital experimentation. Their simulated study showed that A/B testing is often the better choice when dealing with a small number of variations, while bandit strategies, such as Thompson Sampling, become more effective as the number of variations increases. The broader lesson is that experiment...

In this episode, we explore how Instacart tackled the challenge of extracting accurate product attributes at scale. We discuss different solutions—starting with SQL rules, moving to text-based ML models, and finally, Instacart’s multi-modal LLM platform, PARSE. By blending text and image data and enabling rapid configuration, PARSE demonstrates how modern AI tools can streamline data pipelines, re...

In this episode, we explore how Airbnb developed a structured framework that combines unsupervised clustering and supervised modeling to classify listings into meaningful supply personas based on availability patterns. This data-driven approach helps Airbnb enhance personalization, improve experimentation, and gain deeper insights into its global supply base. For more details, you can refer to the...

In this episode, we explore the Bayesian Structural Time Series model as a causal inference methodology and walk through a real-world example of how Walmart leveraged it to measure the impact of a simple yet meaningful product taxonomy change. For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/walmartglobaltech/decoding-causal-increment...

In this episode, we delve into how Pinterest has enhanced its embedding-based retrieval system to provide a more personalized, relevant, and dynamic Homefeed experience. By scaling their models with richer feature interactions, refreshing the content corpus with trending Pins, and leveraging cutting-edge machine learning techniques, Pinterest is able to serve better content—faster and more accurat...

In this episode, we dive into what it’s like to be a data scientist at Meta. Grounded in product leadership, data scientists at Meta apply deep analytical expertise to drive measurement, navigate complex product ecosystems, and shape key decisions—ultimately delivering meaningful impact on product outcomes. For more details, you can refer to their published tech blog, linked here for your referenc...

In this episode, we explore how Revolut is reimagining risk management. By developing a modular, scientifically grounded, and explainable platform, the team has enabled faster, more accurate, and more transparent risk decisions—spanning diverse products and global markets. For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/revolut/reinv...

In this episode, we explore how Lyft tackles interference bias in marketplace experiments using Marketplace Marginal Values (MMVs). We break down why interference is a natural challenge in two-sided platforms like Lyft, and how their team uses optimization, simulation, and advanced metrics to measure causal effects more reliably. For more details, check out the original tech blog linked here: http...