Snacks Weekly on Data Science

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.
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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 t...

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 conte...

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 deli...

In this episode, we explore how Airbnb developed a structured framework that combines unsupervised clustering and supervised modeling to classify listings into meaningful supply personas based on availability patterns. This data-driven approach helps Airbnb enhance personalization, improve experimentation, and gain deeper insights into its global supply base.
For more details, you can refer to their published tech blog, linked here ...

In this episode, we explore the Bayesian Structural Time Series model as a causal inference methodology and walk through a real-world example of how Walmart leveraged it to measure the impact of a simple yet meaningful product taxonomy change.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/walmartglobaltech/decoding-causal-incrementality-in-e-commerce-leveraging-bayes...

In this episode, we delve into how Pinterest has enhanced its embedding-based retrieval system to provide a more personalized, relevant, and dynamic Homefeed experience. By scaling their models with richer feature interactions, refreshing the content corpus with trending Pins, and leveraging cutting-edge machine learning techniques, Pinterest is able to serve better content—faster and more accurately—to hundreds of millions of user...

In this episode, we dive into what it’s like to be a data scientist at Meta. Grounded in product leadership, data scientists at Meta apply deep analytical expertise to drive measurement, navigate complex product ecosystems, and shape key decisions—ultimately delivering meaningful impact on product outcomes.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/@Analyt...

In this episode, we explore how Revolut is reimagining risk management. By developing a modular, scientifically grounded, and explainable platform, the team has enabled faster, more accurate, and more transparent risk decisions—spanning diverse products and global markets.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/revolut/reinventing-risk-at-revolut-77e63c552...

In this episode, we explore how Lyft tackles interference bias in marketplace experiments using Marketplace Marginal Values (MMVs). We break down why interference is a natural challenge in two-sided platforms like Lyft, and how their team uses optimization, simulation, and advanced metrics to measure causal effects more reliably.

For more details, check out the original tech blog linked here: https://eng.lyft.com/using-marketplac...

In this episode, we explore how causal inference helps companies like Microsoft answer high‑stakes product and business questions when A/B testing isn’t possible. We dive into Double Machine Learning—a technique that leverages ML models to control for confounding variables and isolate true causal effects. The result is a flexible, rigorous framework that every data scientist should have in their toolkit.

For more details, you can re...

In this episode, we explore how Whatnot tackled the challenge of scaling feed recommendation systems across a rapidly growing platform. We dive into WhataMix—a DAG-based framework that enables teams to build, test, and deploy feed logic using reusable, modular components. It’s a great example of how thoughtful system design can accelerate development while maintaining high standards in machine learning infrastructure.

For more detai...

In this episode, we explore how Expedia is integrating both generative and traditional AI to enhance the travel experience. The company’s approach leverages generative models for open-ended, natural language tasks, and relies on traditional models for structured, mission-critical problems. By playing to the strengths of each, Expedia is able to build smarter, more adaptable AI systems without overcomplicating things or compromising...

In this episode, we dive into how Glassdoor addresses the challenge of maintaining data quality at a petabyte scale. By treating data as a product, the engineering team built a centralized, scalable platform that enables proactive validation, continuous monitoring, and cross-team collaboration. From data contracts and static code analysis to LLM-based logic checks and anomaly detection, we unpack the key practices behind their appr...

In this episode, we explore how Agoda used large language models (LLMs) to improve user experience through building a conversational AI product. By focusing on prompt engineering, grounding data, and smart evaluation, the team built a scalable assistant that adds real value to the user journey.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/agoda-engineering/how-w...

In this episode, we explore how Meta tackles the complex challenge of setting aligned, measurable, and high-impact goals across a vast organization. Whether you’re in data science, analytics, or product leadership, this episode offers practical insights into building a more effective goal-setting system.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/@Analytics...

In this episode, we will explore how Hike applied transformer-based models to predict user behavior in their Rush Gaming Universe. We will look at the business motivation and break down the technical solution, from input features to prediction and evaluation. This case is a good example of how modern deep learning techniques can drive real impact in improving user experience.
For more details, you can refer to their published te...

In this episode, we will explore quantization techniques for language models. We will look at the business motivation—making large language models more efficient—and unpack the technical solutions that make this possible. 

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/@EsperantoTech/quantization-and-mixed-mode-techniques-for-small-language-models-b3366dbad554

In this episode, we’ll explore the unique security challenges posed by agentic AI systems and why embedding trust and safety into these systems from the ground up is critical. We’ll review a few key ingredients for building a secure agentic AI future.

For more details, you can refer to the blog, linked here for your reference: https://medium.com/intuit-engineering/owasp-dishes-out-key-ingredients-for-a-secure-agentic-ai-future-be862...