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.
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Unifying Ads Engagement Modeling Across Surfaces [Pinterest]
In this episode, we discuss how Pinterest moved from three independently evolving ads engagement models toward a unified modeling framework. The team started with a simple merged model, then progressively refined it by combining complementary modeling components, adding surface-specific calibration and multi-task learning, preserving surface-specific model exports, and optimizing both model computation and serving infrastructure. The result was a system that could share learning across surfaces while still supporting the differences that mattered for each one, with the final approach delivering significant improvements in both offline and online metrics.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/pinterest-engineering/unifying-ads-engagement-modeling-across-pinterest-surfaces-4b5cd3d99e67 (https://medium.com/pinterest-engineering/unifying-ads-engagement-modeling-across-pinterest-surfaces-4b5cd3d99e67)
19 Oct 2026
Predicting Rider Conversion with Sparse Data Using Bayesian Trees [Lyft]
In this episode, we talk about Lyft’s need to predict whether riders will convert across a wide range of highly specific situations, where the available data becomes increasingly sparse as the situations become more specific. Instead of relying entirely on a global model or trusting noisy local estimates, Lyft built a hierarchical Bayesian framework where specific models can borrow information from broader models. This allows the system to capture local behavior when enough evidence exists while relying more on stable, broader patterns when local data is limited.
For more details, you can refer to their published tech blog, linked here for your reference: https://eng.lyft.com/predicting-rider-conversion-in-sparse-data-environments-with-bayesian-trees-07227ff92789 (https://eng.lyft.com/predicting-rider-conversion-in-sparse-data-environments-with-bayesian-trees-07227ff92789)
12 Oct 2026
Budget Allocation at Scale [Atlassian]
Atlassian's Head of Data Science, [Host Name], interviews [Guest Name], a data scientist at Atlassian, about their innovative two-stage system for optimizing marketing budget allocation, achieving significant cost reductions.
Who has been a guest on Snacks Weekly on Data Science
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Expedia
Agoda's team
Meta's data science team
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Apple Podcasts (US)
5.0 / 5
10 ratings
Spotify
5.0 / 5
8 ratings
Podcast Authority Score: 63 / 100
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Quality
97
Social presence
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YouTube
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Engagement
71
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