Loading…
Hosted by Pan Wu · 🇺🇸 US · EN · 151 episodes
Established thought leaders with verified media credentials.
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!
Pan Wu hosts Snacks Weekly on Data Science, a education show with 151 episodes published.
In 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 d
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 in
In 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 proce
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 ca
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 closing
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 geog
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 co
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
In 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
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. To
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 influ
In this episode, we discuss Expedia’s need to evaluate many ranking ideas quickly so that A/B testing would not become a bottleneck. We explore their three-stage experimentation funnel: backtesting to remove weak candida
In this episode, we look at how companies deal with large volumes of unstructured text and why traditional clustering methods often fall short at scale. We explore two LLM-powered approaches shared by data scientists fro
In this episode, we discuss a classic scaling problem in fraud and risk operations: too much manual review, inconsistent judgments, and growing complexity. We explore the team’s solution, Bumblebee, a multi-agent AI arch
In this episode, we explore how OLX improved discovery by combining keyword search and vector search instead of forcing a choice between the two. Keyword systems remain excellent for precision, while vector systems add s
In this episode, we explore how Udemy built a multilingual AI platform to bring its generative AI features to learners around the world. The team approached localization across three levels: a translation-first approach
In this episode, we explore how Meta uses the “Ladder of Evidence” framework to evaluate the effectiveness of new product features. Instead of relying on a single analytical method, this framework helps teams choose the
In this episode, we explore how Vimeo built a customized AI system for subtitle translation—one that goes beyond basic text translation to tackle the much more challenging problem of synchronizing language with timing. W
In this episode, we explore how the New York Times engineering team used AI agents to scale unit test coverage across their News site. They accomplished this by building a custom coverage measurement tool, designing a tw
In this episode, we explore how Shopify evolved its product classification system across three major stages: from a traditional logistic regression model with TF-IDF features, to a multi-modal approach combining text and
Sponsor detection runs nightly. Check back soon.
No public pitch examples yet for this show.
Generate your own personalised pitchBased on semantic analysis of episode topics and host coverage, this show is a strong guest fit for executives in:
Industry fit is computed by PitchCentric using vector embeddings of the show's episode catalog.
Shows with the most semantically similar episode content. Pitch one, pitch all; producers cluster.
Snacks Weekly on Data Science has a verified contact on file. Create a free PitchCentric account to access it and generate a personalised pitch in seconds. Research at least 3 recent episodes first and lead with a specific angle that serves their education audience.
Snacks Weekly on Data Science is hosted by Pan Wu. The show is categorised under education and has published 151 episodes.
Snacks Weekly on Data Science has published 151 episodes.
Snacks Weekly on Data Science regularly covers education. It sits in the education category.
Snacks Weekly on Data Science is accessible for guests with genuine education expertise. A personalised, episode-aware pitch will still outperform a generic one every time.
Snacks Weekly on Data Science hasn't explicitly signalled guest openness in recent episodes. That doesn't rule out pitching. your hook just needs to be especially compelling and relevant to their recent content.
Episodes of Snacks Weekly on Data Science average 9 minutes. a focused format where a clear narrative arc and tight preparation matter most.
Our data rates Snacks Weekly on Data Science's guest bar at 80/100 (Premium tier). Established thought leaders with verified media credentials. Sign in to PitchCentric to see how your own Pod Score compares against this show.
Methodology. Booking Probability™ blends Listen Score, 30-day Virality, open-to-guests detection, and Apple ratings. Data refreshed every 60 minutes. Listen Score and Booking Probability are calculated by PitchCentric. Last enriched 3 days ago.