Hosted by Pan Wu · 🇺🇸 us · EN · 143 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 143 episodes published.
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
In this episode, we explore how Faire built its ads system from scratch. On the business side, we discuss why ads matter for a growing marketplace: enabling brand discovery, creating a new revenue stream, and strengtheni
In this episode, we explore how Agoda tackled a costly engineering bottleneck by integrating GPT into their CI/CD pipeline to analyze failing SQL stored procedures automatically and suggest optimizations — complete with
In this episode, we explore how LinkedIn is reimagining job search with AI and large language models — evolving from rigid, keyword-based systems to flexible, intent-aware experiences that feel more conversational and pe
In this episode, we explore how Uber tackled the challenge of personalizing CRM communications at scale through contextual bandit strategies enhanced with generative AI embeddings, lightweight and powerful models like Li
In this episode, we explore how seemingly perfect-looking SQL generated by AI agents can be “lying” when essential logic is missing. The Thomson Reuters Labs team highlights the need for deeper evaluation beyond simple s
In this episode, we explore how Airbnb measures Listing Lifetime Value by separating it into baseline LTV, incremental LTV, and marketing-induced incremental LTV, and how this framework helps address challenges like meas
In this episode, we explore how OLX evolved its ranking algorithms—from the pairwise logic of RankNet to the metric-optimized power of LambdaRank—to ensure users find exactly what they’re looking for. We discuss how movi
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 143 episodes.
Snacks Weekly on Data Science has published 143 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 10 days ago.
