Yushu Chai Email and Phone Number

Senior Data Scientist with focuses on MLE and AI; Tech Lead- Predictive Modeling on Customer Data- Forecasting Platform- Causal Inference Tools and Pipelining- Fraud Data Science and AI: Detection and Prevention- Data Pipelining, Machine Learning Development and Deployment on Google Cloud Platform

Senior Ecosystems Data Scientist at the Messaging Ecosystems team (Messenger, Facebook Messaging, Instagram Direct Messaging)- North Star Goal metric setting and operationalization- Strategic analysis, reporting, and presentation- User pattern segmentation and prediction- Forecasting KPI trends- Dashboarding of ecosystem metrics- Key XFNs: data engineering, product teams, UXR, etc.

Built and led the model development workflows of user life-time value model, engagement/churn model, and key metrics forecast model, plus design of experiments for game products; created Data Science tech stack for optimizing modeling and analytics pipelines; collaborated with the different functional teams to use data for decision making.

Create, develop, and deploy data science/machine learning pipelines/products as solutions to business problems, to enable personalization of in-game contents/experience, translate data stories and player insights into recommendations, and drive business decisions across cross-functional teams. Project experience include:Personalization of in-game contents and experience:- Recommender systems of in-game items in mobile/console games; built end-to-end pipelines and deployed as API on cloud- Matchmaking systems of multiplayer games; designed new conceptual framework and modeled on expanded types of data- Recommendation of real-world gaming related sessions on an event app that helped users navigate and discover information of their favorite games and genresML based tools that enabled data-driven decision-making and accelerated operations of XFN teams:- Multi-touch attribution as ML pipeline and model that quantified the performance of marketing campaigns of various games (presented at the Joint Statistical Meetings 2018 https://www.amstat.org/meetings/joint-statistical-meetings)- Marketing budget allocation/portfolio optimization tool as web application and API on Kubernetes- Automated ML model building tool that generated model insights on survey data for UXR; deployed as API on Kubernetes- Forecasting tool for key metrics in R and PythonExperimentation and causal inference- Facilitated multiple games on design of experiments to measure performance of feature updatesTech Lead- Mentored summer intern and new hires- Led workstreams of machine learning pipelining and cloud based deployment

Graded students’ homework for upper division linear algebra classes.