Snacks Weekly on Data Science

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 optimizing for long-term brand–retailer relationships and shipping a complex system within just six months.

For more details, you can refer to their published tech blog, which is linked in the episode description: https://craft.faire.com/building-faires-ads-system-from-scratch-5c24fc916995

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 the final call, leading to significant improvements in engineering productivity.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/agoda-engineering/how-agoda-uses-gpt-to-optimize-sql-stored-procedures-in-ci-cd-29caf730c46c

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-of-job-search-at-linkedin

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 systems that adapt in near real time to maximize impact.
For more details, you can refer to their published tech blog, linked here for your reference: https://www.uber.com/blog/enhancing-personalized-crm

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 their published tech blog, linked here for your reference: https://medium.com/tr-labs-ml-engineering-blog/is-your-ai-agent-lying-with-perfect-sql-3a6a7d69bccf

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.com/airbnb-engineering/how-airbnb-measures-listing-lifetime-value-a603bf05142c

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 tech blog, linked here for your reference: https://tech.olx.com/from-ranknet-to-lambdamart-leveraging-xgboost-for-enhanced-ranking-models-cf21f33350fb

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 balancing accuracy, cost, latency, and user experience — especially in AI features that directly interact with users.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/udemy-engineering/evolution-of-the-udemy-ai-assistant-intent-understanding-system-ec3ee0039364

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 thinking, collaboration, and analytics to drive meaningful product value.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/@AnalyticsAtMeta/data-scientists-framework-for-navigating-product-strategy-as-data-leaders-2eb62b20f505

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 more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/paypal-tech/estimating-incremental-lift-in-customer-value-delta-cv-using-synthetic-control-522be5e3da3a