Oct 26, 20269 min
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...
About this Podcast:
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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Contact path
Verified email
Booking probability
35%
Guest openness
Selective
80/100
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What is Snacks Weekly on Data Science about?
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.
Who hosts Snacks Weekly on Data Science?
Snacks Weekly on Data Science is hosted by Pan Wu. The show has published 161 episodes to date.
What has Snacks Weekly on Data Science covered recently?
Recent episodes include "Unifying Ads Engagement Modeling Across Surfaces [Pinterest]" (26 October 2026, 9 minutes), "Predicting Rider Conversion with Sparse Data Using Bayesian Trees [Lyft]" (19 October 2026, 8 minutes), and "Semantic Search for LinkedIn’s Hiring Assistant [LinkedIn]" (5 October 2026, 9 minutes).
How often does Snacks Weekly on Data Science publish?
Snacks Weekly on Data Science publishes roughly weekly, with episodes averaging 10 minutes across 161 episodes.
How hard is it to get booked on Snacks Weekly on Data Science?
Booking probability for Snacks Weekly on Data Science is 35%, and the host is rated Selective on guests. Snacks Weekly on Data Science requires a Pod Score of 80 or higher. Snacks Weekly on Data Science's contact route on record: verified email.
Who should pitch Snacks Weekly on Data Science?
The strongest fits for Snacks Weekly on Data Science are IT Services & Software Development. Snacks Weekly on Data Science has an education audience.
About the host
Pan Wu hosts Snacks Weekly on Data Science, an education podcast with 161 episodes published.
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Recent episodes
Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.
- Oct 19, 20268 min
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...
- Oct 5, 20269 min
In this episode, we discuss how LinkedIn moves beyond keyword matching to find candidates based on actual qualification fit. The team first created a scalable relevance definition using LLM-based supervision, distilled...
- Sep 28, 20269 min
In this episode, we discuss how Meta measures the business value of shared ML infrastructure when that value appears indirectly through downstream model launches. Instead of relying on adoption, usage, or traditional...
- Sep 21, 20269 min
In this episode, we discuss two LLM-assisted pipelines for turning unstructured text into a structured taxonomy with minimal manual annotation. The bottom-up approach lets semantic representations and clustering...
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- Sep 14, 20269 min
In this episode, we talk about how Instacart identified a limitation of a standard taxonomy: it could classify products, but it couldn't connect them the way real shoppers do. The team developed a solution around...
Episode #154
Agent-driven ML Exploration [Faire]
Sep 7, 202611 minIn this episode, we discuss how Faire addresses a fundamental bottleneck in machine learning development: there are more potential experiments than human scientists can realistically run. They developed Autoscience...
- Aug 31, 202610 min
In this episode, we discuss how DoorDash built a reliable metadata generation platform that could operate across millions of constantly changing menu items. The solution combined multimodal generation, automated...
- Aug 24, 202611 min
In this episode, we explore how The New York Times built a real-time decision system that estimates the causal impact of different paywall actions, optimizes those decisions based on business objectives, and...
Episode #151
Recommending Travel Destinations [Airbnb]
Aug 17, 202610 minIn this episode, we explore how Airbnb built a destination recommendation model to predict the places users are most likely to travel to next. The model predicts travel intent using sequences of user actions, carefully...
- Aug 10, 202610 min
In this episode, we discuss how Meta built an AI system with persistent memory, structured organization, secure access to enterprise tools, and reusable workflows. The result was an AI assistant that could participate...
Episode #149
Scaling Experimentation Quality [Booking.com]
Aug 3, 202610 minIn this episode, we explore how Booking.com scaled experimentation quality across the organization. Instead of relying on strict enforcement, the team chose to optimize for flexibility while building the supporting...
- Jul 27, 202612 min
In this episode, we discuss how Netflix built an LLM-based evaluation framework for creative writing quality without losing the nuance that makes expert review valuable. They achieved this by creating a golden dataset...
- Jul 20, 202610 min
In this episode, we explore how Glassdoor customized the Two-Tower model for its specific production environment. The gap between generic semantic embeddings and behavior-driven embeddings proved significant, and...
- Jul 13, 202611 min
In this episode, we talk about why Lyft needed to understand which rider experiences drive long-term retention. We discuss how raw observational data can produce misleading conclusions because of confounders such as...
- Jul 6, 20269 min
In this episode, we explore how Flipkart rethought ranking in its "Similar Products" widget. Instead of relying on fixed ad slots, the team built a unified ranking system that enables sponsored and organic products to...
- Jun 29, 20269 min
In this episode, we explore how Pinterest rethought its ads serving infrastructure to support more expressive ranking models beyond the traditional two-tower architecture. Their solution was not simply to build a better...
Episode #143
Analytics AI Agent [Meta]
Jun 22, 202611 minIn this episode, we explore how Meta addressed a fundamental limitation of applying LLMs to enterprise analytics. While modern models are highly capable of generating code and SQL, they still fall short when it comes to...
- Jun 15, 20269 min
In this episode, we explore how CVS Health tackled a classic large-scale engineering operations problem: keeping application runbooks accurate, complete, and continuously compliant across hundreds of internal systems...
- Jun 8, 202610 min
In this episode, we explore how Uber tackled one of the most operationally challenging parts of its marketplace: airport pickups. Unlike normal city rides, airport demand is highly bursty, queue-driven, and heavily...
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