Women in Data Science at Uber: Moving the World With Data

During an October 2018 meetup, members of our Women in Statistics, Data, Optimization, and Machine Learning (WiSDOM) group presented on their technical work at Uber.

Four Ways Uber Visualization Made an Impact in 2018

Uber's Head of Urban Computing & Visualization reflects on his team's work visualizing data to better understand urban mobility in 2018—and beyond.
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Sessionizing Uber Trips in Real Time

Uber's many data flows required modeling the data associated with a specific task, such as a rider trip, into a state machine. The state machine lets engineers focus on just the events needed to successfully accomplish a trip.

Analyzing Experiment Outcomes: Beyond Average Treatment Effects

Quantile treatment effects (QTEs) enable our data scientists to capture the inherent heterogeneity in treatment effects when riders and drivers interact within the Uber marketplace.

Uber’s Big Data Platform: 100+ Petabytes with Minute Latency

Responsible for cleaning, storing, and serving over 100 petabytes of analytical data, Uber's Hadoop platform ensures data reliability, scalability, and ease-of-use with minimal latency.

Uber Expands Advanced Visualization Ecosystem with Mapbox Integration

Uber Visualization announces partnership with Mapbox to enhance our data visualization tools and grow our open source community.
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Marmaray: An Open Source Generic Data Ingestion and Dispersal Framework and Library for Apache...

Today we introduce Marmaray, an open source framework allowing data ingestion and dispersal for Apache Hadoop, realizing our vision of any-sync-to-any-source functionality, including data format validation.
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Talking Safety with Uber Data Scientist Sunny Jeon

In an interview for the Uber Eng blog, Data Scientist Sunny Jeon talks about how his team develops solutions in order to advance Uber's core value of safety.

Forecasting at Uber: An Introduction

In this article, we provide a general overview of how our teams leverage forecasting to build better products and maintain the health of the Uber marketplace.

Under the Hood of Uber’s Experimentation Platform

Uber's experimentation platform empowers us to improve the customer experience by allowing teams to launch, debug, measure, and monitor product changes.

Maximizing Process Performance with Maze, Uber’s Funnel Visualization Platform

Uber developed Maze, our funnel visualization platform, to identify possible UX bottlenecks and provide insight into the various ways riders and drivers interact with our platform.

Databook: Turning Big Data into Knowledge with Metadata at Uber

Databook, Uber's in-house platform for surfacing and exploring contextual metadata, makes dataset discovery and exploration easier for teams across the company.

Herb: Multi-DC Replication Engine for Uber’s Schemaless Datastore

Facing the need for a resilient data structure over thousands of storage nodes to serve the 15 million rides per day that occur on our platform, Uber engineers developed Herb, our data replication solution. Herb ensures data availability and integrity across our data centers.

M4 Forecasting Competition: Introducing a New Hybrid ES-RNN Model

With a solid margin, Uber senior data scientist Slawek Smyl won the M4 Competition with his hybrid Exponential Smoothing-Recurrent Neural Networks (ES-RNN) forecasting method.
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Food Discovery with Uber Eats: Building a Query Understanding Engine

Uber engineers share how we process search terms for our Uber Eats service, using query understanding and expansion to find restaurants and menu items that best match what our eaters want.

From Beautiful Maps to Actionable Insights: Introducing kepler.gl, Uber’s Open Source Geospatial Toolbox

Created by Uber's Visualization team, kepler.gl is an open source data agnostic, high-performance web-based application for large-scale geospatial visualizations.

Growing the Data Visualization Community with deck.gl v5

deck.gl v5 incorporates simplified APIs, scripting support, and framework agnosticism, making the popular open source data visualization software more accessible than ever before.

Mediation Modeling at Uber: Understanding Why Product Changes Work (and Don’t Work)

Uber Labs leverages mediation modeling to better understand the relationship between product updates and their outcomes, leading to improved customer experiences on our platform.

Queryparser, an Open Source Tool for Parsing and Analyzing SQL

Written in Haskell, Queryparser is Uber Engineering's open source tool for parsing and analyzing SQL queries that makes it easy to identify foreign-key relationships in large data warehouses.

Implementing Model-Agnosticism in Uber’s Real-Time Anomaly Detection Platform

Uber Engineering extended our anomaly detection platform's ability to integrate new forecast models, allowing this critical on-call service to scale to meet more complex use cases.

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