Snacks Weekly on Data Science

How Data Scientists Lead and Drive Impact [Meta]

In this episode, we dive into what it’s like to be a data scientist at Meta. Grounded in product leadership, data scientists at Meta apply deep analytical expertise to drive measurement, navigate complex product ecosystems, and shape key decisions—ultimately delivering meaningful impact on product outcomes.For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/@AnalyticsAtMeta/how-data-scientists-lead-and-drive-impact-at-meta-6b5b896821b2

Building Scalable Risk Management Platform [Revolut]

In this episode, we explore how Revolut is reimagining risk management. By developing a modular, scientifically grounded, and explainable platform, the team has enabled faster, more accurate, and more transparent risk decisions—spanning diverse products and global markets.For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/revolut/reinventing-risk-at-revolut-77e63c552503 

Tackling Interference Bias with Marketplace Marginal Values [Lyft]

In this episode, we explore how Lyft tackles interference bias in marketplace experiments using Marketplace Marginal Values (MMVs). We break down why interference is a natural challenge in two-sided platforms like Lyft, and how their team uses optimization, simulation, and advanced metrics to measure causal effects more reliably.For more details, check out the original tech blog linked here: https://eng.lyft.com/using-marketplace-marginal-values-to-address-interference-bias-a11aff6e670f

Causal Inference with Double Machine Learning [Microsoft]

In this episode, we explore how causal inference helps companies like Microsoft answer high‑stakes product and business questions when A/B testing isn’t possible. We dive into Double Machine Learning—a technique that leverages ML models to control for confounding variables and isolate true causal effects. The result is a flexible, rigorous framework that every data scientist should have in their toolkit.For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/data-science-at-microsoft/introduction-to-causal-inference-using-double-machine-learning-5daa642321f3

Scalable and Blendable Feed Construction [Whatnot]

In this episode, we explore how Whatnot tackled the challenge of scaling feed recommendation systems across a rapidly growing platform. We dive into WhataMix—a DAG-based framework that enables teams to build, test, and deploy feed logic using reusable, modular components. It’s a great example of how thoughtful system design can accelerate development while maintaining high standards in machine learning infrastructure.For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/whatnot-engineering/whatamix-blendable-feed-construction-2c94c21f6635