Snacks Weekly on Data Science

In this episode, we explore how Airbnb built a destination recommendation model to predict the places users are most likely to travel to next. The model predicts travel intent using sequences of user actions, carefully designed training examples for both active and dormant users, and multi-task learning to capture geographic relationships. Together, these design choices led to more relevant recommendations and measurable improvements across multiple recommendation products.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/airbnb-engineering/recommending-travel-destinations-to-help-users-explore-5fa7a81654fb

In this episode, we discuss how Meta built an AI system with persistent memory, structured organization, secure access to enterprise tools, and reusable workflows. The result was an AI assistant that could participate in ongoing work rather than isolated chat sessions. By carefully organizing knowledge and retrieving only the context relevant to the current task, the system enabled the assistant to deliver more relevant answers and ultimately helped employees work more productively.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/@AnalyticsAtMeta/how-we-built-an-ai-second-brain-for-60k-knowledge-workers-78c507dd795b

In this episode, we explore how Booking.com scaled experimentation quality across the organization. Instead of relying on strict enforcement, the team chose to optimize for flexibility while building the supporting processes to foster a strong experimentation culture, defining a clear framework for measuring experimentation quality, and integrating those practices directly into the experimentation platform so that good behavior became the default. The combination of improved tooling and sustained community education created a positive feedback loop, leading to more product decisions being backed by sound statistical practices.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/booking-com-data-science/scaling-experimentation-quality-at-booking-com-726152ee4ef0

In this episode, we discuss how Netflix built an LLM-based evaluation framework for creative writing quality without losing the nuance that makes expert review valuable. They achieved this by creating a golden dataset calibrated by expert writers, splitting evaluation into specialized judges for each quality criterion, and applying targeted techniques—tiered reasoning, consensus sampling, and multi-agent verification—tailored to the unique needs of each criterion.

For more details, you can refer to their published tech blog, linked here for your reference: https://netflixtechblog.com/evaluating-netflix-show-synopses-with-llm-as-a-judge-6269251e6f28

In this episode, we explore how Glassdoor customized the Two-Tower model for its specific production environment. The gap between generic semantic embeddings and behavior-driven embeddings proved significant, and closing it required thoughtful decisions regarding training data, negative sampling, and auxiliary learning signals.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/glassdoor-engineering/improving-embedding-based-candidate-generation-for-recommender-systems-with-a-two-tower-model-c222123beb7f 

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