In this episode, we talk about why Lyft needed to understand which rider experiences drive long-term retention. We discuss how raw observational data can produce misleading conclusions because of confounders such as geography, and how Lyft's Rider Experience Score addresses this challenge using Augmented Inverse Propensity Weighting. This doubly robust causal inference method adjusts for those confounders and produces experiment-quality estimates without running an experiment.

For more details, you can refer to their published tech blog, linked here for your reference: https://eng.lyft.com/my-starter-project-on-the-lyft-rider-data-science-team-86a60dddd935

In this episode, we explore how Flipkart rethought ranking in its "Similar Products" widget. Instead of relying on fixed ad slots, the team built a unified ranking system that enables sponsored and organic products to compete on the same playing field. By combining a unified ranking model with a score normalization layer, the system ranks content based on contextual merit rather than predefined positions. The result: higher organic orders, increased ad revenue, and a better overall marketplace experience.

For more details, you can refer to their published tech blog, linked here for your reference: https://blog.flipkart.tech/the-science-of-unified-ranking-integrating-ads-and-organic-recommendations-8cc24113ef21

In this episode, we explore how Pinterest rethought its ads serving infrastructure to support more expressive ranking models beyond the traditional two-tower architecture. Their solution was not simply to build a better model, but to redesign the serving stack so that ranking logic and model inference work more closely together, allowing lightweight ranking models to capture richer signals while operating efficiently at massive scale.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/pinterest-engineering/beyond-two-towers-re-architecting-the-serving-stack-for-next-gen-ads-lightweight-ranking-models-1992f2b76cbb

In this episode, we explore how Meta addressed a fundamental limitation of applying LLMs to enterprise analytics. While modern models are highly capable of generating code and SQL, they still fall short when it comes to organizational context and deep domain understanding — both of which are essential for reliable, real-world analytical work. Meta’s approach focuses on closing this gap through shared memory systems, iterative reasoning loops, transparent execution, and a layered organizational knowledge framework built around Cookbooks, Recipes, and Ingredients.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/@AnalyticsAtMeta/inside-metas-home-grown-ai-analytics-agent-4ea6779acfb3

In this episode, we explore how CVS Health tackled a classic large-scale engineering operations problem: keeping application runbooks accurate, complete, and continuously compliant across hundreds of internal systems. To solve this, the team built an LLM-based automated auditing pipeline. The result is a lightweight but effective system that turns documentation compliance from a periodic manual effort into a continuous and scalable operational workflow.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/cvs-health-tech-blog/automated-documentation-auditing-leveraging-llms-for-compliance-verification-13fa80b90912 

In this episode, we explore how Uber tackled one of the most operationally challenging parts of its marketplace: airport pickups. Unlike normal city rides, airport demand is highly bursty, queue-driven, and heavily influenced by flight schedules, delays, and driver positioning decisions. To solve this, Uber built a coordinated forecasting system composed of three specialized models: Estimated Time to Request (ETR) to predict queue wait times, Earnings Per Hour (EPH) to estimate airport profitability versus city driving, and Driver Deficit Forecasting to proactively reposition supply before shortages occur. This allows the platform to reduce uncertainty, improve driver behavior, and stabilize airport marketplace dynamics in real time.

For more details, you can refer to their published tech blog, linked here for your reference: https://www.uber.com/blog/forecasting-models-to-improve-availability-at-airports

In this episode, we discuss Expedia’s need to evaluate many ranking ideas quickly so that A/B testing would not become a bottleneck. We explore their three-stage experimentation funnel: backtesting to remove weak candidates, interleaving to rapidly compare promising ones, and A/B testing to validate final business impact. What made this design approach effective was using the right evaluation tool at the right step instead of forcing A/B testing to do everything.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/expedia-group-tech/interleaving-for-accelerated-testing-75adc644027b

In this episode, we look at how companies deal with large volumes of unstructured text and why traditional clustering methods often fall short at scale. We explore two LLM-powered approaches shared by data scientists from Microsoft: a bottom-up pipeline that builds structure from data using embeddings and clustering, and a top-down pipeline that starts with LLM-generated categories and refines them recursively into a hierarchy.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/data-science-at-microsoft/from-chaos-to-clarity-building-taxonomies-from-unstructured-text-using-large-language-models-c1303db3adb1

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