Snacks Weekly on Data Science

In this episode, we discuss a classic scaling problem in fraud and risk operations: too much manual review, inconsistent judgments, and growing complexity. We explore the team’s solution, Bumblebee, a multi-agent AI architecture that separates planning, evidence gathering, and analysis into specialized roles, enabling a robust and scalable system to solve the problem.For more details, you can refer to their published tech blog, linked here for your reference: https://engineering.razorpay.com/meet-bumblebee-the-multi-agent-ai-architecture-that-changed-fraud-detection-at-razorpay-c2b6d5704f51

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 your reference: https://tech.olx.com/hybrid-search-where-keywords-meet-vectors-enabling-classifieds-discovery-b7c383fe4fc4

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 routes between the two depending on the situationFor more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/udemy-engineering/from-zero-to-hero-localization-led-generative-ai-at-udemy-a422e4f968d4

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 here for your reference: https://medium.com/@AnalyticsAtMeta/ladder-of-evidence-in-understanding-effectiveness-of-new-products-part-i-ad8dee70906c

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 system remains reliable in real-world scenarios.For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/vimeo-engineering-blog/how-we-built-ai-powered-subtitles-at-vimeo-ff11f1d64b2a