Technical Talks

Chenyu Qiu

Senior Applied Scientist | Uber

Scalable Continuous Monitoring for Large-scale A/B Experimentation

ABOUT THE TALK

Data Sci & Algos

At Uber, our A/B Testing Framework and Continuous Experiment Monitoring talk reveals how we've revolutionized experimental analytics at scale. We'll demonstrate our solution to the "peeking problem" that plagues traditional experiment monitoring approaches. This presentation showcases our automated platform that processes thousands of monitoring analyses daily using regression-adjusted estimators with anytime-valid inference. This advanced statistical methodology eliminates 95% of noise without sacrificing true signals, enabling Early Experiment Detection and Performance Insights. Learn how our Spark-powered computational framework efficiently batches experiments and metrics for scalable processing. We'll share Real-World Case Studies showing how this system has transformed Uber's Data-Driven Decision Making, minimizing undetected regressions and accelerating product innovation across our global platform.

Senior Applied Scientist

Chenyu Qiu

Uber

Chenyu Qiu is a Staff Scientist at Uber and the science lead of the Experimentation Analysis team. His work focuses on building scalable experimentation and causal inference tools to drive sound and efficient business decisions. Prior to joining Uber, Chenyu earned his PhD from the University of Chicago and was a Pre-Doctoral Fellow in Energy Economics at the National Bureau of Economic Research.

Technical Talks

Scalable Continuous Monitoring for Large-scale A/B Experimentation

ABOUT THE TALK Data Sci & Algos

ABOUT THE TALK

Data Sci & Algos