Snacks Weekly on Data Science

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’s faster, more accurate, and far easier to maintain than traditional rule-based automation.For more details, you can refer to their published tech blog, linked here for your reference: https://www.uber.com/blog/advancing-invoice-document-processing-using-genai

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 more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/walmartglobaltech/walmart-journey-to-optimize-web-performance-and-drive-business-growth-c3bec8d7780b

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, statistics, and engineering into an actionable workflow, empowering teams to respond faster and with greater confidence.For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/pinterest-engineering/the-quest-to-understand-metric-movements-8ab12ae97cda

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 real business impact — improving bookings, enhancing customer satisfaction, and increasing overall platform efficiency.For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/airbnb-engineering/improving-search-ranking-for-maps-13b03f2c2cca

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 recommendations. Together, they power a system that makes replacements more accurate, relevant, and efficient at scale.For more details, you can refer to their published tech blog, linked here for your reference: https://tech.instacart.com/how-instacart-uses-machine-learning-to-suggest-replacements-for-out-of-stock-products-8f80d03bb5af