Snacks Weekly on Data Science

In this episode, we explore how OLX improved discovery by combining keyword search and vector search instead of forcing a choice between the two. Keyword systems remain excellent for precision, while vector systems add semantic understanding. Together, they create a smarter and more user-friendly marketplace experience. For more details, you can refer to their published tech blog, linked here for...

In this episode, we explore how Udemy built a multilingual AI platform to bring its generative AI features to learners around the world. The team approached localization across three levels: a translation-first approach for broad and fast coverage, a fully native multilingual system for markets where fluency and cultural precision are essential, and a hybrid solution in between that intelligently...

In this episode, we explore how Meta uses the “Ladder of Evidence” framework to evaluate the effectiveness of new product features. Instead of relying on a single analytical method, this framework helps teams choose the right type of evidence based on real-world constraints, leading to better and more informed product decisions. For more details, you can refer to their published tech blog, linked...

In this episode, we explore how Vimeo built a customized AI system for subtitle translation—one that goes beyond basic text translation to tackle the much more challenging problem of synchronizing language with timing. We discuss how the team designed a split-brain architecture to separate translation quality from timing constraints, and how they implemented fallback mechanisms to ensure the syste...

In this episode, we explore how the New York Times engineering team used AI agents to scale unit test coverage across their News site. They accomplished this by building a custom coverage measurement tool, designing a two-loop human–AI workflow, and investing heavily in prompt engineering, including strict guardrails to prevent the agent from cheating or drifting. The key takeaway is that AI works...

In this episode, we explore how Shopify evolved its product classification system across three major stages: from a traditional logistic regression model with TF-IDF features, to a multi-modal approach combining text and images, and finally to Vision Language Models built on top of a standardized and evolving product taxonomy. We also look at how architectural design and inference optimization are...

In this episode, we explore how Faire built its ads system from scratch. On the business side, we discuss why ads matter for a growing marketplace: enabling brand discovery, creating a new revenue stream, and strengthening the overall ecosystem. On the technical side, we break down the three core components—Ads Delivery, Ads Manager, and Ads Foundation—and examine key considerations such as optimi...

In this episode, we explore how Agoda tackled a costly engineering bottleneck by integrating GPT into their CI/CD pipeline to analyze failing SQL stored procedures automatically and suggest optimizations — complete with rewritten queries, index recommendations, and side-by-side performance comparisons. The result is a human-in-the-loop system where AI handles the heavy lifting and engineers make t...

In this episode, we explore how LinkedIn is reimagining job search with AI and large language models — evolving from rigid, keyword-based systems to flexible, intent-aware experiences that feel more conversational and personalized. For more details, you can refer to their published tech blog, linked here for your reference: https://www.linkedin.com/blog/engineering/ai/building-the-next-generation-...

In this episode, we explore how Uber tackled the challenge of personalizing CRM communications at scale through contextual bandit strategies enhanced with generative AI embeddings, lightweight and powerful models like LinUCB and XGBoost, and smart decision augmentation with SquareCB. This work shows how data science can take a core business need—delivering relevant user communications—and build sy...

In this episode, we explore how seemingly perfect-looking SQL generated by AI agents can be “lying” when essential logic is missing. The Thomson Reuters Labs team highlights the need for deeper evaluation beyond simple syntax checks, and shows how tools like TruLens and AgentBench help expose hidden errors and better align agent outputs with real business intent. For more details, you can refer to...

In this episode, we explore how Airbnb measures Listing Lifetime Value by separating it into baseline LTV, incremental LTV, and marketing-induced incremental LTV, and how this framework helps address challenges like measuring true incrementality and handling uncertainty about the future. For more details, you can refer to their published tech blog, linked here for your reference: https://medium.co...

In this episode, we explore how OLX evolved its ranking algorithms—from the pairwise logic of RankNet to the metric-optimized power of LambdaRank—to ensure users find exactly what they’re looking for. We discuss how moving from simple classification to "Learning to Rank" helps businesses prioritize user attention where it matters most. For more details, you can refer to their published t...

In this episode, we explore how Udemy tackled the tricky challenge of understanding learner intent in their AI Assistant — from a simple similarity-based embedding model, through experiments with larger models and fine-tuning, to a hybrid system that intelligently leverages both embeddings and large language model classification. This evolution demonstrates how real-world ML systems often require...

In this episode, we explore how Meta’s data scientists approach product strategy using a structured framework that adapts to different data and problem scenarios. We walk through the distinct analytical approaches used across different problem spaces, defined by whether data availability is high or low and whether problem clarity is broad or concrete. Each scenario requires a different mix of thin...

In this episode, we explore how PayPal estimates incremental lift in customer value using synthetic control methods. This causal inference–based approach provides a principled way to construct a counterfactual and isolate causal effects when traditional experiments aren’t sufficient, helping teams measure true impact in a complex, noisy, real-world environment and make more informed decisions. For...

In this episode, we explore how Netflix tackles the challenge of predicting user session intent by extending the capabilities of its foundation model with a hierarchical multi-task learning architecture. This approach helps Netflix better understand what users want in the moment and personalize the experience in real time, ultimately improving its recommendation system at scale. For more details,...

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...