Stella Y. Yang Email and Phone Number

Focuses on addressing business growth and collaborating with cross-functional teams to internalize search engine marketing (SEM). In the meanwhile, oversee all offline marketing channels.* Supervised 70% the major marketing channels, SEM, display, radio, and TV. Established the groundwork for allocating customer acquisition budgets across marketing channels* Developed and implemented a programmatic auto-bidding system for SEM* Revamped hundreds of thousands of keywords on the Google Ad Exchange platform to enhance bidding accuracy. Addressed challenges related to sparse data and resolved issues with Ad inventory caps using NLP* Migrated the whole company's Ad contents to newly designed auto bidding approach through Google Ads API* Trained junior data scientists: on ETL practices, transferred matured marketing channels, guided on algorithm approach

Built explicable forecasts model on client behaviors and demand volume, supporting both long-term strategic planning and short-term operational decisions* Collaborated building sophisticated company demand forecasting R package, predicting dozens of different types of client demand, counting different impact from client status data and all possible client status changeso Developed the core piece Marketing Mix Model(MMM); kept innovating the model to adjust the dynamic changes in the business landscape, providing guidance in QBRo Build and presented model to assessed Total Addressable Market (TAM) for executive team* Communicate w/ diverse business partners across different levels to derive actionable insights from the modelso Collaborated w/ partners to quantify the impact of strategic initiatives and establish growth targetso Generated preliminary forecasts, identifying key drivers and trends, to assist finance, operations, and marketing teams in assessing risks and opportunities.

Statistical Analysis* Developed customized GLM regression models to estimate parameters for non-t-distribution, enabling precise measurement of A/B testing outcomes.* Utilized robust time series models to forecast site traffic, accounting for rare events, trends, and seasonalityMathematical Modeling* Implemented an attribution model in a Hadoop environment to allocate offline revenue to online marketing channels* Engineered customer segmentation using vector similarity techniques for personalized product recommendations* Employed decision tree algorithms, including bagging, gradient boosting, and random forest, for customer acquisition on imbalanced datasets* Conducted analysis and prediction of customers' Lifetime Value (LTV) using bootstrap methodsData Preparation and Feature Extraction* Developed SAS macros to select top variables from extensive candidate lists, employing information value techniques* Conducted dimension reduction via SAS procedures to cluster variables and identify confounding factorsData Visualization and Automation* Created animated heat map with extensive use of SAS Macro and enabled senior management team to visualize the business trend* Set up Windows Task Scheduler to run batch SAS code commands, which automatically generates monthly customer reports from Oracle and Microsoft SQL database

* Built SAS Macro to perform deep analysis on customers awareness on Staples Brand* Created Monthly MDM Customer Report on customer acquisition and development through building SAS macros* Improved query efficiency though SQL execution plan, reducing query running time from 24 hours to 2 hours* Assisted drafting Functional Specification (30+ pages in SQL code) to move DFA data into Microsoft server in house* Coordinated IT team to implementing MicroStrategy to visualize display online marketing performance from database * Designed cookie level customer path report to track and assess impression, click contribution to revenue* Use Adobe Insight reporting suite to build ad hoc reports for Staples to match online campaign and offline sales

* Simulated the optimization model of least cost energy transition and distribution based on the Long-term Alternative Energy Planning system (LEAP)* Introduced the LEAP system to developing countries by helping prepare tutorial material and propose suggestions of improvement from a user experience perspective* Prepared an in depth literature review of the Environmental Kuznets Curve

* Combined the data from Bureau of Labor Statistics to 366 cities’s data from Bureau of Economic Analysis to study Beveridge curve* Examined job openings and labor turnovers as the economy moves through the business, such as matching process, labor force participating process, long-term unemployment and frictional unemployment* Leveraged Panel Data to analyze the relation between unemployment and vacancies against metro area characteristics through mixed effect modeling