Snacks Weekly on Data Science

Pan Wu
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This podcast is about making data science and machine learning knowledge accessible and less intimidating. Every week, I will handpick one selected industrial tech blog to break it down. We will discuss some key data science concepts and machine learning algorithms, and how they are applied in those real-world applications. Subscribe to the channel and enjoy Snacks Weekly on Data Science!

  1. HÁ 3 H

    Catalog Attribute Extraction with Multi-Modal LLMs [Instacart]

    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

    10min

  2. 1 DE SET.

    Segmenting Supply with a Data-Driven Methodology [Airbnb]

    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 for your reference: https://medium.com/airbnb-engineering/from-data-to-insights-segmenting-airbnbs-supply-c88aa2bb9399

    8min

  3. 25 DE AGO.

    Causal Inference with Bayesian Structural Time Series Model [Walmart]

    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-bayesian-structural-time-series-model-with-f7eaf7267d69

    9min

  4. 18 DE AGO.

    Advancements in Embedding-Based Retrieval [Pinterest]

    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 users. For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/pinterest-engineering/advancements-in-embedding-based-retrieval-at-pinterest-homefeed-d7d7971a409e

    10min

  5. 11 DE AGO.

    How Data Scientists Lead and Drive Impact [Meta]

    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/@AnalyticsAtMeta/how-data-scientists-lead-and-drive-impact-at-meta-6b5b896821b2

    10min

  6. 4 DE AGO.

    Building Scalable Risk Management Platform [Revolut]

    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-77e63c552503

    10min

  7. 28 DE JUL.

    Tackling Interference Bias with Marketplace Marginal Values [Lyft]

    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-marketplace-marginal-values-to-address-interference-bias-a11aff6e670f

    10min

  8. 21 DE JUL.

    Causal Inference with Double Machine Learning [Microsoft]

    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 refer to their published tech blog, linked here for your reference: https://medium.com/data-science-at-microsoft/introduction-to-causal-inference-using-double-machine-learning-5daa642321f3

    8min
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Sobre

This podcast is about making data science and machine learning knowledge accessible and less intimidating. Every week, I will handpick one selected industrial tech blog to break it down. We will discuss some key data science concepts and machine learning algorithms, and how they are applied in those real-world applications. Subscribe to the channel and enjoy Snacks Weekly on Data Science!

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