Tag: data science
Less is More: Engineering Data Warehouse Efficiency with Minimalist Design
Data science helps Uber determine which tables in a database should be off-boarded to another source to maximize the efficiency of our data warehouse.
Science at Uber: Powering Uber’s Ridesharing Technologies Through Mapping
Dawn Woodard, Director of Data Science, considers travel time prediction one of Uber's most interesting mapping problems.
Science at Uber: Bringing Research to the Roads
Uber Principal Engineer Waleed Kadous discusses how we assess technologies our teams can leverage to improve the reliability and performance of our platform.
Science at Uber: Building a Data Science Platform at Uber
Uber Director of Data Science Franziska Bell discusses how we created data science platforms at Uber, letting employees of all technical skills perform forecasts and analyze data.
Using Causal Inference to Improve the Uber User Experience
Uber Labs leverages causal inference, a statistical method for better understanding the cause of experiment results, to improve our products and operations analysis.
Power On: Accelerating Uber’s Self-Driving Vehicle Development with Data
A key challenge faced by self-driving vehicles comes during interactions with pedestrians. In our development of self-driving vehicles, the Data Engineering and Data Science teams at Uber ATG (Advanced Technologies Group) contribute to the data processing and analysis that help make these interactions safe.
Improving Uber’s Mapping Accuracy with CatchME
CatchMapError (CatchMe) is a system that automatically catches errors in Uber's map data with anonymized GPS traces from the driver app.
Solving Big Data Challenges with Data Science at Uber
How engineers and data scientists at Uber came together to come up with a means of partially replicating Vertica clusters to better scale our data volume.
Data Science at Scale: A Conversation with Uber’s Fran Bell
We spoke to Data Science Director Fran Bell about machine learning at Uber and what she finds most challenging—and rewarding—about her work.
First Uber Science Symposium: Discussing the Next Generation of RL, NLP, ConvAI, and DL
The Uber Science Symposium featured talks from members of the broader scientific community about the the latest innovations in RL, NLP, and other fields.
Why Financial Planning is Exciting… At Least for a Data Scientist
In this article, Uber’s Marianne Borzic Ducournau discusses why financial planning at Uber presents unique and challenging opportunities for data scientists.
Experience in AI: Uber Hires Jan Pedersen
Uber welcomes Jan Pedersen as a Distinguished Scientist to our Uber AI group, where he will bring his extensive experience to our efforts in improving artificial intelligence and machine learning.
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.
Omphalos, Uber’s Parallel and Language-Extensible Time Series Backtesting Tool
Uber Engineering created Omphalos, our new backtesting framework, to enable efficient and reliable comparison of forecasting models across languages.
Gleaning Insights from Uber’s Partner Activity Matrix with Genomic Biclustering and Machine Learning
Uber Engineering's partner activity matrix leverages biclustering and machine learning to better understand the diversity of user experiences on our driver app.
Welcoming Peter Dayan to Uber AI Labs
Arriving now: Uber's Chief Scientist Zoubin Ghahramani introduces Uber AI Labs' newest team member, award-winning neuroscientist Peter Dayan.
Turbocharging Analytics at Uber with our Data Science Workbench
Uber Engineering's data science workbench (DSW) is an all-in-one toolbox that leverages aggregate data for interactive analytics and machine learning.
Building an Intelligent Experimentation Platform with Uber Engineering
Composed of a staged rollout and intelligent analytics tool, Uber Engineering's experimentation platform is capable of stably deploying new features at scale across our apps. In this article, we discuss the challenges and opportunities we faced when building this product.
