Hosted by Unknown Host · EN · 5 episodes
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Lucas and Luna sit at a data-science workstation, two thin laptops open to scatter plots and clustering visualizations, and ask: what can we actually learn from the numbers? Each episode of The Data Science Podcast with Fexingo is a grounded, specific conversation about a single analytics problem or machine-learning method — from regularization in regression to the bias-variance trade-off in random forests. Lucas leads with a journalistic eye for how models are built and tested in the real world, citing actual case studies like how Netflix used matrix factorization for recommendations or how healthcare researchers apply survival analysis to clinical trials. Luna keeps the discussion honest, asking about data quality, feature engineering pitfalls, and whether a model’s accuracy actually translates to business value. They never resort to buzzwords: instead, they walk through the workflow from data collection to deployment, discussing trade-offs like interpretability versus performance. The show serves data scientists, analysts, and engineers who want to stay sharp on methods without the hype. Listeners walk away with a clearer understanding of why one algorithm beats another on a given dataset, and what that means for their own projects. Can a neural network ever be truly explainable? And if not, should we trust it anyway?
Unknown Host hosts The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations, a general show with 5 episodes published.
In Episode 33 of The Data Science Podcast, Lucas and Luna drill into a single question that has vexed marketers and analysts alike for decades: how do you know if an ad really caused a sale, or if the person would have b
Episode 32 of The Data Science Podcast explores the critical difference between deploying a model and keeping it relevant. Lucas and Luna break down why most ML teams treat model retraining as a fire drill instead of a s
Episode 31 of The Data Science Podcast with Fexingo examines a growing crisis in applied AI: models that perform perfectly in testing but cause real-world harm. Lucas and Luna dissect a 2025 case where a major hospital n
Episode 30 dives into the documented case of a 2019 health-care algorithm used by millions of US patients that systematically assigned lower risk scores to Black patients than equally sick white patients. Lucas and Luna
Lucas and Luna dive into the hidden bias that plagued credit scoring models for decades—and the data science fix that's finally changing it. They walk through a 2025 study from the Consumer Financial Protection Bureau sh
Episode 28 of The Data Science Podcast with Fexingo explores the growing energy footprint of data centers and AI training. Lucas and Luna break down a real 2025 case: a midwestern utility that had to fast-track two natur
Episode 27 of The Data Science Podcast dives into one of the most common yet under-discussed problems in applied machine learning: production data pipeline failures. Lucas and Luna unpack a real-world case from a mid-siz
Kaggle competitions have launched data science careers and pushed the field forward, but they also teach habits that don't translate to production. Lucas and Luna break down the gap between leaderboard-chasing and actual
Lucas and Luna dive into concept drift—when the statistical properties of a target variable change over time, degrading model performance. Using a concrete case from a credit card fraud detection system, Lucas explains h
Data scientist Lucas and data-savvy Luna dive into data leakage—how a seemingly perfect machine learning model can fail spectacularly because it accidentally learned from information it shouldn't have had. Lucas walks th
Lucas and Luna explore how causal forests — a machine learning method developed from the work of Susan Athey and others — let data scientists estimate ad effectiveness without randomized experiments. Using a real-world c
Episode 22 of The Data Science Podcast dives into a fascinating real-world case: how LinkedIn's data science team used causal inference — specifically a method called double machine learning — to figure out whether tweak
When a data science team grows from two people to twenty, model training turns into chaos. Different engineers pull data from different sources, calculate features differently, and nobody can reproduce a model from six m
Most data science teams treat documentation as an afterthought—a few comments in a Jupyter notebook or a stale Confluence page. In episode 20 of The Data Science Podcast, Lucas and Luna explore why that approach is dange
Lucas and Luna dive into RLHF — reinforcement learning from human feedback — the technique that made modern chatbots safe and useful. They break down the three-stage pipeline: supervised fine-tuning, reward model trainin
When a model silently fails in production, the root cause often traces back to a forgotten metadata point: which version of the training pipeline was used. In this episode, Lucas and Luna unpack a real case from a fintec
When fraud detection models flag only 2% of transactions as suspicious, the real criminals often slip through. In this episode, Lucas and Luna unpack how a data scientist at a major payments processor used graph analytic
Episode 16 of The Data Science Podcast with Fexingo. Lucas and Luna explore how a major European payments company used synthetic data to fix a fraud detection model that was crippled by privacy regulations and extreme cl
In this episode, Lucas and Luna dive into the collaborative filtering algorithm behind Spotify's Discover Weekly. They break down how matrix factorization learns user preferences from sparse listening data, using the exa
Ever wonder how Spotify knows what song you want to hear next? This episode unpacks the collaborative filtering algorithm that powers Discover Weekly and Release Radar. Lucas walks through the math behind user-item matri
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The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations is hosted by Unknown Host. The show is categorised under General and has published 5 episodes.
The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations has published 5 episodes.
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Episodes of The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations average 9 minutes. a focused format where a clear narrative arc and tight preparation matter most.
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