The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations

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 natural gas plants to power a single hyperscale campus. They discuss the gap between model efficiency gains and total compute growth, the role of on-site renewables, and whether the gri...

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-sized fintech company where a silent schema change broke a credit risk model for three weeks, costing over $2 million in mispriced loans. They walk through the specific failure modes—...

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 data science work—using the iconic 'Porto Seguro's Safe Driver Prediction' competition as a case study. They explore how public leaderboards encourage overfitting, why feature eng...

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 how data scientists monitor for drift using metrics like PSI (Population Stability Index) and KL divergence. Luna challenges whether traditional thresholds are enough, and they expl...

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 through a concrete example: a hospital's model that predicted sepsis with 99% accuracy, only to crash to 50% in production because it had learned to detect the time-stamp pattern of...

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 case from a mid-market e-commerce company that spent $2 million on YouTube ad campaigns, they show how the company's data team built a causal forest model to isolate the true increm...

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 tweaking the feed algorithm actually caused more engagement, or if the apparent lift was just correlation. Lucas and Luna walk through the problem of confounding variables, the differen...

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 months ago. This episode walks through the real story of a fintech startup that hit exactly this wall: their credit-risk model worked in a notebook but fell apart in production beca...

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 dangerous, especially as models move into production. They walk through a real case: a fintech startup whose fraud model silently degraded because feature definitions in the code didn't...

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 training with human comparisons, and proximal policy optimization. Using real examples, they explain why reward hacking is a real danger and how alignment is never truly finished. A must-...

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 fintech lender that lost two million dollars because a model was accidentally retrained on stale features. They walk through the metadata fields every team should track—from git commit h...

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 analytics and network features to uncover a hidden fraud ring that had evaded every rule-based and machine learning model for 18 months. The key insight: instead of scoring individual tran...

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 class imbalance. Synthetic data—artificially generated records that preserve statistical patterns without exposing real user information—allowed the team to augment their training se...

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 example of a user who jumps from classical to indie rock. They discuss cold-start problems, the trade-off between exploration and exploitation, and why your weekly playlist feels eeri...

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 matrices, matrix factorization, and how Spotify tackles the cold-start problem for new users and new artists. Luna probes the trade-off between personalization and the filter bubble, an...

Episode 13 of The Data Science Podcast with Fexingo dives into causal inference—specifically, how data scientists can estimate cause-and-effect relationships from observational data when A-B testing isn't possible. Lucas and Luna walk through a real-world case: how a health-tech startup used double machine learning (DML) to determine whether its app's push notifications actually reduced hospital r...

Episode 12 of The Data Science Podcast dives into why traditional frequentist A/B testing can lead to false positives and how a Bayesian approach fixes it. Lucas and Luna walk through a concrete example: an e-commerce team testing a new checkout flow that looked like a winner at 5,000 visitors but collapsed at 10,000. They explain p-hacking, the peek problem, and how Bayesian methods with prior di...

Episode 11 of The Data Science Podcast tackles the hidden danger of imbalanced datasets. Lucas and Luna walk through a real-world example: a fraud detection model trained on 99.9 percent legitimate transactions and 0.1 percent frauds. The model achieved 99.9 percent accuracy yet caught zero frauds. They explain why accuracy is a terrible metric on imbalanced data, introduce precision-recall curves...

In this episode, Lucas and Luna tackle one of the most frustrating problems in modern AI: hallucination in large language models. They break down the specific mechanisms that cause models to confidently generate false information, using the example of a customer support chatbot that invented a refund policy. Lucas explains how retrieval-augmented generation (RAG) and grounding techniques can reduc...

In this episode, Lucas and Luna explore how interpretability tools like SHAP and LIME uncovered a hidden signal in a hospital's cancer diagnosis model. They walk through a real case where the model was accurate but biased by a spurious correlation with patient age, and how a data scientist used local explanations to catch it before deployment. The conversation covers the difference between global...

In this episode, Lucas and Luna dig into a specific pitfall of A/B testing that tripped up a real fintech company: Simpson's Paradox. They walk through the exact scenario where a landing page variant showed higher conversion overall but lost on every customer segment, and explain why a proper stratified analysis would have caught the trap. Lucas brings a concrete example from a 2025 A/B test at a...

Episode 7 of The Data Science Podcast tackles the critical concept of distribution shift — what happens when the data your model sees in production differs significantly from its training data. Lucas and Luna walk through a concrete example from a ride-hailing app that saw its demand prediction model fail during a holiday surge. They explain covariate shift, prior probability shift, and concept dr...

Machine learning models don't break the way software does. They rot slowly, like fruit left on the counter. In this episode, Lucas and Luna explore a real-world case from a fintech lending company that deployed a fraud detection model in late 2024. By February 2026, the model's precision had dropped from 92% to 61% — not because of a bug, but because borrower behavior shifted. This is data drift:...

In this episode of The Data Science Podcast, Lucas and Luna explore how recommendation algorithms create filter bubbles that trap users in narrowing loops. Using a real example from a major social media platform's newsfeed algorithm in early 2026, they break down the mechanics behind collaborative filtering, the feedback loop that causes category convergence, and the one metric engineers use to de...

In this episode of The Data Science Podcast, Lucas and Luna dive into the art of feature engineering — the process of transforming raw data into inputs that make machine learning models actually work. They anchor the discussion around a specific case: a mid-sized hedge fund that improved its equity factor model's Sharpe ratio from 0.7 to 1.4, not by changing algorithms, but by redesigning feature...