Ning (Nikki) Gong

**** ***** ******, *** *******, CA 90064

adbl24@r.postjobfree.com 917-***-****

A Data Scientist focusing on statistical experimentation and product optimization to drive business impact Education

Columbia University New York, NY

B.S. Industrial Engineering (STEM), Minor in Psychology GPA: 3.9/4.0 Tau Beta Pi Graduated in 05/2019 Relevant Coursework: Statistical Computing and Data Science (R), Business Analytics (R), Data Analytics &Python (Python), Database System Management (SQL), Mathematical Optimization Modeling, Simulation Modeling and Analytics (R) Professional Experience

Activision Blizzard, Inc Santa Monica, CA

Associate Data Scientist in Player Science— Call of Duty Mobile (CODM) 07/2019-02/2020

● Designed and evaluated statistical A/B tests using R to inform game decisions. Executed tests in collaboration with cross- functional teams, which included product managers, engineers, designers, and producers

● Communicated test results through presentations to studio heads, Activision Blizzard senior leadership, and other product and engineering teams, making strategic recommendations which were adopted by the business leadership

● Created an automated A/B test analysis and reporting framework using R markdown, reducing time required to obtain initial results from 1 week to 5 minutes with minimal user input to be used by technical and non-technical audiences

● Performed quality assurance with engineering team on ETL data pipeline with SQL/AWS-SparkSQL in Databricks. Standardized reporting data visualizations and deployed dashboards in Tableau that tracked key KPIs of the game

● Performed exploratory data analysis in revenue and engagement drivers, identified improvement opportunity through statistical analysis and gave strategic recommendations to product managers

● Identified causal impact on user engagement and monetization from changes in game by applying machine learning model

(random forest, regressions) and causal inference on observational data from natural experiment (difference-in-differences)

● Predicted high-propensity churners to support marketing teams by delivering a churn risk model using MBG/NBD algorithm UBS Financial Services Inc. New York, NY

Business Analytics Intern 06/2018-08/2018

● Led a team of interns to quickly automate data transformation for a project using Visual Basic for Applications (VBA), generating time savings of 800+ hours of analysts’ time

● Developed non-sales employee performance evaluation framework by analyzing the existing sales performance framework, establishing business KPI metrics from scratch and conducting 15+ interviews with employees

● Analyzed large UBS internal datasets using SQL and Excel Vlookup/PivotTable to evaluate performance at employee level, and composed data requirement documentation for the data engineering team to address key missing data

● Communicated business requirements in client revenue report and utilized VBA to automate the report with data from 20+ sources, generating both time savings of 1000+ hours and budget savings of $100,000 per year Independent Machine Learning Project

Recidivism Rate Prediction Engine (Github link: https://github.com/ng2620/Eudaemonia.git)

● Implemented a model using R to forecast the probability of a probationer recommitting a crime with 72% accuracy rate, which improved prediction accuracy by 24% compared to the benchmark model KNN

● Pre-processed data and imputed missing values by using R package “MICE” that combined multiple imputation methods

● Tuned the hyper-parameters of various machine learning models including lasso logistic regression, LDA, decision trees, KNN. Found the optimal probability threshold for classification by confusion matrix

● Identified the most important predictor variables that contributed to the recidivism rate from lasso logistic regression Technical Skills & Musical Achievements

● Coding Skills: Professional experience in: SQL, R (CARET, dplyr, ggplot2, shiny, Rmarkdown), Python (Numpy, Pandas, scikit-learn), Tableau, and Advanced Excel (Macros/VBA/Pivot Tables/Vlookup); Working knowledge of: Distributed Computing (AWS in Databricks), Java

● Machine Learning Algorithms: Supervised Learning methods (Linear regression, Lasso logistic regressions, Decision Tree, KNN method, Random Forest, Gradient Boost, XG Boost), Unsupervised Learning Methods (K-means)

● Musical Achievements: Opera Singer (5th Place in Chinese National Amateur Opera Singing Competition); Officially released album ‘Where Luna Awakened’ with 8 songs and a music MTV video “Moon Bear”; Violinist (Amateur Level 8)

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