Working Experience

Vanta INC.

Los Angeles (Remote), USA

Senior Data Analyst, Product                                                May 2024- Present

FLYR (Labs)                        

Santa Monica, USA

Delivery Data Technical Lead (Customer Facing)                          July 2023- Jan 2023 

Customer Success Department

Main Job responsibilities: A collaborative role that is key to implementing FLYR SaaS products. Immersed in clients’ data, guided product implementations, troubleshooted issues, led technical discussions, and served as a technical mentor on the team. 

• Product Enhancement: Led a 2-member data analyst team to ensure production data pipeline stability and generate proper data sets for new Machine Learning models while collaborating with data scientists. Utilized Big Query SQL, Python, and tools like Google Cloud Composer, DBT, and Airflow.

• Cross-functional Collaborations: Initiated a data quality improvement project, enhancing product scalability and data reliability for ML models under diverse airline data sources. Required cross-functional communication with product managers, airline experts, data analysts, and data scientists.

• Problem Solving: Identified and addressed product feature issues through thorough discussions with code scripts, product owners, and clients. Designed solutions aligned with mutual interests of the product team and clients.

Data Scientist II, Analytics Department                                    Jul 2022- May 2023

Main Job responsibilities: Data Scientist that worked with cross-functional team focused on user growth.

● Experimental design: Increased weekly active users and feature usage by over 50% through successful marketing experiments. Defined appropriate business metrics, recommended sample sizes for test/control, selected ideal users for experiments, and calculated statistical significance on audience groups before and after the tests.

●  Product analytics: Developed Tableau live dashboards to monitor experiment impact on core and target business metrics in real-time, reducing individual analysis time by 50%. Provided easy access to primary test results for product and marketing teams without data scientist involvement.

●  Efficiency analytics: Conducted an efficiency analysis of push notifications, resulting in a 10% reduction in unnecessary notifications while maintaining high user engagement. Adjusted push audience and frequency based on user profiles and recent activity and refined prediction models for improved accuracy in predicting user activity. Utilized SQL, Tableau, Python, R, and slides for comprehensive analysis.

●  Data cleaning: Overcame data obstacles with effective cross-functional communication and advanced SQL. Successfully simulated missing activity data and delivered complex feature usage for yearly summary review in less than a day, despite no existing definition and code logic. This campaign led to positive social media discussions.

●  Initiated and managed a knowledge sharing space by collecting and organizing scattered information on business domain, tables, and codes leading to an improved on-boarding process and enhanced peer knowledge sharing.

DriveTime Automotive Group Inc.          
Seattle (Remote), USA                                              

Intern, Data Science Department                                             Mar 2022-Jun 2022                                                                                                                

●  Led a team of 5 to design and execute a solution for optimizing reserve prices for used cars in dealer auctions to increase profit margins while maintaining high likelihood of sales, receiving high praise from both business and data science teams.

●  Proposed an innovative approach, including data simulation and predictive modeling (XGboost and LightGBM), to overcome challenges caused by missing important bidding data, which is required for most of the reserved price simulations. Created a compelling data story for the business team through graphs, charts, and results from Explainable AI (SHAP) to clarify how the model makes predictions.

Senior Data Scientist 

Digital Finance and Customer Analyst Department                          Feb 2021-May 2021

●    Directed future development of the bank through organizing a vital restructuring project led by McKinsey & Company. Hand-picked by the CEO’s management team to join the project as the only data analyst.

●    Pioneered a new department and managed an adept analyst team of 5 people. Created cross-functional team focused on implementing new business opportunities based on high-spending medical professionals’ purchasing trends.

●    Audited customers’ overall asset profiles and provided insights by designing and creating detailed dashboards weekly via Excel and Python.

Related Project: Medical professionals’ purchasing trends analysis

Project Manager and Machine Learning Engineer

Intelligent Finance Department                                          May 2018-Mar 2021

●    Promoted experimental design within the department by writing a Python package that offered sample size suggestions, randomized engineering, and significance calculations. Efforts approved by the CTO and later conducting experiments such as A/B testing became basic criteria for everyone in the department before finishing projects.

●    Built the road map for an income prediction model project and implemented it into a fully automated loan approval process that is now patent protected. On average 416 manpower days were reclaimed yearly without increasing any credit risk to the business after project launch after project launch. The project is built with BERT, KMeans, LightGBM. Model result is delivered through REST API calls. Stakeholders involved legal and business departments.

●    Decreased marketing costs by 50% while achieving the same marketing objectives through incorporating marketing strategy and deployment of machine learning models. Model was developed with LightGBM classification and deployed with Azure DevOps. Explained the model with explainable AI such as LIME and SHAP.

●    Organized first YouTube Live event of an online data competition (similar to Kaggle) hosting over 600 attendees despite no prior experience hosting video streaming.

Related Project: Income prediction, Travel behaviors prediction, Foreign currency exchange, ESBeriment

Data Scientist    

Customer Risk and Value Department                                      Sep 2016-May 2018

●    Lead data scientist for a cross-functional team with 10 diverse team members from different departments. Increased credit card revenue by 3% yearly through implementing data insights and A/B testing into marketing campaigns, heavily using SQL, SAS EG, Python, and Excel to analyze millions of credit card transactions.

●    Designed and maintained product performance reports on credit card usage, including but not limited to customers’ top transaction categories, numbers of active credit cards, and performance of social media ads.

●    Initiated a project to acquire and retain loyal customers which is now used as the core strategy for the credit card department. Defined metrics for determining customer loyalty and designed various monitoring methods using prediction models like logistic regression and random forest to maintain 40% more customers.

Related Project: Credit Card Acquisition, Customer Loyalty retention