The Wharton School

KU Leuven

Interlake High School

Management & Technology Summer Institute

Machine Learning at Square

Cash App - redefining the world's relationship with money

I built deep learning models that saved Block 8+ figures yearly. I then led a team to design and build a next generation ML inference stack that could serve these models, along with all legacy models. I also lent my expertise optimizing complex systems under load to other teams - one of my changes removed ~5ms from every single Cash App request, saving customers about a year of waiting per day.

2023: Lead team of 8 engineers building cross-org ML architecture, including model hosting, feature serving, and embedding storage. Our services serve up to 100k QPS and span multiple business units. 

2022: Lead team building org-wide ML model hosting and feature serving infrastructure for Cash App.

2021: Built text embedding models for Cash App risk and improved most important metrics by XX%+. Transitioned to lead team building infrastructure for to serve these deep learning models.

2019-2020: financial crimes ML.

Square

Senior Staff Machine Learning Engineer

Sharing and growing the world's knowledge.

Ads ranking: lead a team of 9 machine learning engineers to power our advertising backend (e.g. candidate retrieval, clickthrough/conversion prediction). Set OKR's, vision, and roadmap. Represent and advocate for the team cross-functionally.

Ads product: led launches of gender targeting, promoted answers, and auction insights.

Ads data science: building a world-class advertising platform through product experimentation (users, businesses) and data analysis. Lead initiatives to understand and improve auction dynamics, clickthrough prediction / recommendation systems, viewership fatigue, and ads quality. Built core data tables and systems.

Cross-team: Drive significant improvements to data tooling and infrastructure. Intern mentor and technical interviewer.

Quora

Data Scientist / Product Manager

Head teaching assistant for "Modern Data Mining" with Professor Linda Zhao. 

Cross-listed course for undergraduates, MBA's, and PhD students, focusing on statistical learning methods. This is a heavily technical course with a strong emphasis on R programming ability. I wrote homework, quizzes, exams, projects, and custom R tooling for the course, held office hours and was a reassuring presence on Piazza.

Teaching Assistant - Stat 471/571/701

Belong anywhere.

Data scientist intern with the Host and Community Operations (HCO) Data team. Analyzed market maturity and growth patterns on the supply side. Prepared data visualizations and presentations for leadership and other stakeholders of various technical familiarity. Built machine learning model to cluster markets among relative stages of growth. Worked with R, Hive, Presto.

Core contributor to internal R package, Rbnb, including adding robust caching tools, creating notification functionality, as well as totally rewriting the core internals to be more flexible, extensible, and predictable.

Airbnb

Data Scientist Intern

Teaching assistant for Statistics 470 - Statistical Computing in R, with Professor Andreas Buja. This was the first iteration of the class.

Teaching Assistant - Stat 470

I built some "novel" anomaly detection methods (via machine learning and robust Mahalanobis distances) that were probably too novel to actually be adopted at scale.

Expedia, Inc.

Data Science Intern

Develop and optimize graph algorithms for social network analysis in Java. Clean and prepare data in SQL and Java. With Professor Lynn Wu.

Chris Hua