• Designed A/B experiments to test incremental value of offering Rewards to member on different health activities. Executed multiple campaigns through collaboration with a cross-functional team of Marketing and Engineer• Built, designed a Uplift Model using Catboost for a rewardable campaign to identify persuadable audience segment for a health activities, which improved campaign targeting strategies and achieved 20% more ROI, then built an Internal Optimization Uplift Package with a group of 5• Researched and prototyped a GenAI use case to understand product grievance root cause to improve satisfaction, using LLMs including Hugging Face Sentence Transformers, GPT-4, few-shot prompting. Created a Looker Dashboard for monthly performance tracking for leadership• Analyzed and monitored health status of Rewards transaction data system, reported on debit/credit balance out, and used that data to analyze Members’ Rewards Utilization on different rewardable activities to understand member segmentation • Constructed analytical and machine learning pipelines in Google Cloud to identify business opportunities and measure project success including establishing engineering standards, automating ETL

• Built Random Forest to predict customer LTV for optimization, resulting in increasing ROI 8% more• Automated workflow by centralizing an ETL data pipeline, using Python and SQL via APIs ,to track ads spend, budget, and campaign performance, interacting with Operating System, data qa&wrangling&compiling• Designed and built weekly Power BI Dashboards for efficient reporting, which increase efficiency for 80%• Built a Prophet Model to model holiday season ads traffic trend to help optimizing holiday ads strategy• Collaborated with Engineering team on Airflow DAG for implementing tasks scheduling

• Taught weekly lab sessions and enhance students’ understanding of self-prepared lecture materials and familiarity with R• Communicated complex Machine Learning concepts in meaningful ways to students’ from different research fields

• Developed an automatic report dashboard by R shiny for real-time clinical trial data from sketch• Created 8 R functions for a package, with integrating the special format requirement of safety report for new drug application and the convenience for team usage• Organized and designed the internal training of R

• Improved IBM Machine quality, maintenance cost, workers’ efficiency in APAC area by providing insights on work allocation, cost performance and major maintenance reason in weekly report, which reduced 12% cost and 8% maintenance tickets• Built SQL Pipelines and Cognos dashboard to track the fluctuation of KPI to avoid Machine downtime

• Built Predictive Models for potential 4G users to guide campaign strategy and increase conversion by R and SQL, which identified targeting users from 1M users’ behavior data by classification model with Naïve Bayes, Random Forest, Logistic Regression• Utilized Unsupervised Pattern Recognition and Feature Engineering Techniques such as PCA, Step-wise Selection, Correlation Analysis, Outlier Detection for proper model input• Delivered actionable insights by interpreting feature importance score and presenting by Tableau Data Visualization