9 articles
This post describes how Lyft built a Bayesian hierarchical tree model to predict rider conversion in real-time under sparse data conditions.
Airbnb explains how COVID broke their booking-to-trip forecasting models and the architectural changes they built to handle structural data shifts.
Airbnb recaps its 2025 academic research at KDD, CIKM, and EMNLP covering ML, NLP, and recommendation systems.
This article traces the career journey of Peter Coles, Airbnb's Head Economist for Policy and Director of Data Science, from academia to tech industry leadership.
This article describes the architecture, optimization, and evolution of Lyft's Feature Store, a core ML infrastructure platform serving 60+ use cases across the rideshare stack.
This post describes how Lyft evolved LyftLearn, their end-to-end ML platform, from a fully Kubernetes-based system to a hybrid architecture combining AWS SageMaker and Kubernetes.
This post describes a Lyft data scientist's starter project using the Rider Experience Score (RES) tool to estimate long-term causal effects of rider experiences on retention without relying on A/B tests.
This post explains how Lyft models and solves the rider-driver matching problem using graph theory and optimization algorithms.
This post explains how to fetch and manipulate UK Bank Holidays JSON data using Pandas on a Jupyter Notebook to produce a queryable DataFrame.
Lyft
BBC