Snacks Weekly on Data Science

In this episode, we explore the importance of covariate selection in causal inference and how different types of variables can influence the results. The discussion highlights why careful covariate selection is essential for generating reliable insights and enabling smarter, evidence-based business decisions.
For more details, you can refer to their published tech blog, linked here for your reference: https://booking.ai/covariate-selection-in-causal-inference-good-and-bad-controls-5f56126a984a

In this episode, we explore how Klaviyo used counterfactual learning and uplift modeling to move beyond the question of which treatment works — to the deeper question of for whom it works. We’ll see how the team combined randomized experiments, causal inference techniques, and uplift modeling to power a product that helps marketers deliver smarter, more personalized messages.
For more details, you can refer to their published tech blog, linked here for your reference: https://klaviyo.tech/the-stats-that-tell-you-what-could-have-been-counterfactual-learning-and-uplift-modeling-e95d3b712d8a

In this Halloween special episode, we explore some fun facts and surprising data behind these festive favorites: Did you know Illinois is the top pumpkin-producing state, harvesting nearly 40% of all pumpkins in the U.S.? Or that Reese’s Peanut Butter Cups consistently rank as America’s most popular Halloween candy? And that over — or at least — 20% of pet owners now dress up their pets for Halloween? Now, let’s dive into these facts and the history behind the holiday. Enjoy!

In this episode, we explore how Whatnot improved its feed ranking system by moving from batch predictions to online inference—enabling the platform to scale effectively while capturing real-time marketplace dynamics. This evolution reflects a broader shift in recommendation systems toward more adaptive, real-time personalization.

For more details, check out the full tech blog from the Whatnot engineering team: https://medium.com/whatnot-engineering/evolving-feed-ranking-at-whatnot-25adb116aeb6

In this episode, we explore Tripadvisor’s self-serve experimentation platform for marketing. On the business side, the challenge was measuring campaign effectiveness in a messy, external environment where clean randomization isn’t always possible. On the technical side, the TripAdvisor team developed a system that applies causal inference techniques—particularly the difference-in-differences method—to deliver reliable estimates of campaign impact.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/tripadvisor/introducing-baldur-tripadvisors-self-serve-experimentation-tool-for-marketing-7fc9933b25cc

In this episode, we look at how Meta addressed the challenge of feature selection at scale through Global Feature Importance—a system that aggregates insights across models to surface the most valuable features. This approach not only streamlines model development but also enables machine learning engineers to iterate more effectively and build models that deliver stronger business impact.
For more details, check out Meta’s published tech blog here: https://medium.com/@AnalyticsAtMeta/collective-wisdom-of-models-advanced-feature-importance-techniques-at-meta-1a7a8d2f9e27

In this episode, we explore how to evaluate the retrieval-augmented generation (RAG) capabilities of small language models. On the business side, we discuss why RAG, long context windows, and small language models are critical for building scalable and reliable AI systems. On the technical side, we walk through the Needle-in-a-Haystack methodology and discuss key findings about retrieval performance across different models.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/data-science-at-microsoft/evaluating-rag-capabilities-of-small-language-models-e7531b3a5061

In this episode, we explore how Netflix enhanced recommendation personalization using foundation models. These models can process massive user histories through tokenization and attention mechanisms, while also addressing the cold-start problem with hybrid embeddings. The work highlights how principles from large language models can be adapted to build more effective recommendation systems at scale.

For more details, you can refer to their published tech blog, linked here for your reference: https://netflixtechblog.com/foundation-model-for-personalized-recommendation-1a0bd8e02d39

In this episode, we explore how Vanguard evaluated standard A/B testing against multi-armed bandits for digital experimentation. Their simulated study showed that A/B testing is often the better choice when dealing with a small number of variations, while bandit strategies, such as Thompson Sampling, become more effective as the number of variations increases. The broader lesson is that experimentation design should always be context-aware—balancing simplicity, speed, and interpretability based on your business needs.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/vanguard-technology/smarter-web-wins-a-b-testing-vs-multi-armed-bandits-unpacked-7f5032358513

In this episode, we explore how Instacart tackled the challenge of extracting accurate product attributes at scale. We discuss different solutions—starting with SQL rules, moving to text-based ML models, and finally, Instacart’s multi-modal LLM platform, PARSE. By blending text and image data and enabling rapid configuration, PARSE demonstrates how modern AI tools can streamline data pipelines, reduce engineering overhead, and deliver better user experiences.

For more details, you can refer to their published tech blog, linked here for your reference: https://tech.instacart.com/multi-modal-catalog-attribute-extraction-platform-at-instacart-b9228754a527