{"$schema":"https://raw.githubusercontent.com/jsonresume/resume-schema/v1.0.0/schema.json","basics":{"name":"Linda (Mengyu) Zhang","label":"Growth Product Leader @ Handshake","image":"https://saywise-production-profilepicturestoragebucket-kbfooccc.s3.amazonaws.com/profile-pictures/e59a1ead-b3d1-4d2b-8ce9-83c0152c908e/linkedin-1791302884845.png","location":{"city":"San Francisco, CA, United States"},"profiles":[{"network":"LinkedIn","username":"mzhang79","url":"https://linkedin.com/in/mzhang79"}]},"meta":{"canonical":"https://saywise.com/member8190","version":"v1.0.0","lastModified":"2026-10-06T16:08:07.243Z"},"x_saywise":{"handle":"member8190","pronouns":null,"availability":null,"oneLiner":null,"profileUrl":"https://saywise.com/member8190","markdownUrl":"https://saywise.com/member8190/profile.md","pdfUrl":"https://saywise.com/member8190/resume.pdf","archetype":{"code":"HE-DG","name":"Junction Navigator","url":"https://saywise.com/sca/c7f4b23eb0be","rarity":"One of the first Junction Navigators on Saywise"}},"work":[{"name":"Handshake","position":"Principal Product Manager, Growth","location":"United States","url":"https://joinhandshake.com","startDate":"2025-09-01","summary":"Lead product strategy and execution for Handshake’s growth team, owning Acquisition, Notifications, and Mobile. Drove up to 2× YoY WAU growth through product-led growth initiatives. Leverage AI to accelerate product strategy, prototyping, and execution."},{"name":"TikTok","position":"Staff Product Manager","location":"San Francisco Bay Area","startDate":"2023-05-01","endDate":"2025-09-01","summary":"Core product, Social Team (Feb 2024 – Present) - Scaling inbox and notification reach through targeted push strategies and badging experiments, driving higher interaction volume - Leading growth for TikTok’s Direct Messaging ecosystem, focusing on activity status, lightweight interactions, and conversation triggers to deepen social engagement - Designed and built core messaging infrastructure, including permissions models, typing indicators, and user state logic Creator Monetization (May 2023 – Feb 2024) - Launched TikTok’s first creator monetization data platform, enabling org-wide visibility into creator earnings and incentive performance - Built the first recommendation system for the Creator Monetization Center to improve content discovery and creator engagement - Re-designed and shipped the 2024 pricing strategy for the TikTok Creator Rewards Program, improving payout alignment and transparency"},{"name":"Meta","position":"Data Science Manager","location":"Menlo Park, California","url":"https://meta.com","startDate":"2019-07-01","endDate":"2023-05-01","summary":"- Lead teams in Messenger product foundation including presence, search, sharing, notification for both Messenger and IG direct"},{"name":"IRI","position":"Data Science Analyst","location":"Greater Chicago Area","url":"https://iriworldwide.com","startDate":"2017-01-01","endDate":"2019-06-01"},{"name":"Steelcase","position":"Advanced Analytics intern","location":"Grand Rapids, Michigan","url":"https://steelcase.com","startDate":"2016-06-01","endDate":"2016-11-01"},{"name":"University of Illinois at Urbana-Champaign","position":"Teaching Assistant","location":"Urbana-Champaign, Illinois Area","startDate":"2015-08-01","endDate":"2016-05-01"},{"name":"Lenovo","position":"Data Analyst intern","location":"Beijing City, China","url":"https://lenovo.com","startDate":"2015-06-01","endDate":"2015-08-01"},{"name":"Chubb","position":"Intern analyst","location":"Hong Kong","url":"https://chubb.com","startDate":"2013-02-01","endDate":"2013-03-01"}],"education":[{"institution":"University of Illinois at Urbana-Champaign","studyType":"Master of Science (MSc) in Statistics","startDate":"2014-01-01","endDate":"2016-01-01"},{"institution":"University of California, Davis","url":"https://ucdavis.edu","studyType":"summer session in Statistics","startDate":"2013-01-01","endDate":"2013-01-01"},{"institution":"Zhejiang University","studyType":"Bachelor of Science (BSc) in Statistics","startDate":"2010-01-01","endDate":"2014-01-01"}],"projects":[{"name":"Multiclass Classification on Company Credit Ratings","description":"• Retrieved companies’ credit rating data from Standard & Poor’s and did multi-classification on it • Predicted new companies’ credit ratings using some classification models such as principal component analysis, logistic regression, naïve bayes method, support vector machine and random forest method"},{"name":"Researches on Financial Risk Management of Realty Industry in Hangzhou, China","description":"• To form a general overview of the realty industry in Hangzhou, China by collecting related data and analyzing relevant mathematics models. This could also provide reference to realty investment. • focused on theories of time series, financial mathematics and financial risk management course and layed the theoretical foundation in forms of group study; mastered data analysis software such as C language, R language and so on;"},{"name":"Discriminant Analysis of Color-Quality Type For Wine","description":"This wine data is from UCI Machine Learning Repository and it contains two datasets. The two datasets are related to red and white variants of the Portuguese \"Vinho Verde\" wine. There are 12 variables and 4898 observations of white wine, 1599 of red wine. Among all variables, fixed acidity, volatile acidity and citric acid are predictors measuring the degree of acidity. Residual sugar, chlorides measure other indexes. Free sulfur dioxide and total sulfur dioxide are measuring the sulfur dioxide’s content. These are all chemical predictors. Density, pH, sulphates and alcohol are measuring physical attributes. The quality of wine is with the score between 0 and 10. For my individual part of this project, I used the discriminant analysis to classify the quality of red and white wine. I classified the data into six levels that are red wine with low quality (RL), red wine with median quality (RM), red wine with high quality (RH), white wine with low quality (WL), white wine with median quality (WM) and white wine with high quality (WH). Classifying a new data point into one of the groups above based on the discrimination is my final goal of this project."},{"name":"Walmart Sales Forecasting with Regression Analysis","description":"Provided with historical sales data for 45 stores in 81 departments of Walmart in different region, I predicted the weekly sales for recent years using different methods of regression model such as simple linear model, logistic regression model and random forest model. With this prediction, I can see how time, holiday and other possible factors will affect the future sales."}],"skills":[{"name":"Statistical Modeling"},{"name":"Machine Learning"},{"name":"Data Analysis"},{"name":"Statistics"},{"name":"Data Mining"},{"name":"Analysis"},{"name":"Project Management"},{"name":"Research"},{"name":"Business Analysis"},{"name":"Analytics"},{"name":"R"},{"name":"SAS"},{"name":"SQL"},{"name":"Python"},{"name":"Matlab"},{"name":"Tableau"},{"name":"SPSS"},{"name":"Microsoft Excel"},{"name":"PowerPoint"},{"name":"Microsoft Office"},{"name":"LaTeX"},{"name":"Photoshop"},{"name":"Microsoft Word"},{"name":"Teamwork"},{"name":"Qualitative Research"},{"name":"SAS E-Miner"},{"name":"Text Mining"},{"name":"JMP"}],"publications":[{"name":"Preparation and Evaluation in Vitro and in Vivo of Docetaxel Loaded Mixed Micelles for Oral Administration","summary":"• Calculated drug loading content (DL), encapsulation efficiency (EE) and precipitated drug percentage (PD) of micelles according to relevant data and formulas • Presented data in the form of line chart by utilizing Matlab in the parts of synthesis & characterization of the CSO-SA, in vitro release of DTX and pharmacokinetic studies"}]}