Customer Analysis, Python, SQL, R, Tableau, Spark, Machine Learning

Yuan Chen

628-***-**** *******@*******.*** San Francisco, CA https://www.linkedin.com/in/yuanchen1114/ P R O F I L E

Master of Business Analytics graduate with 1.5 years’ hands-on experience in user experience analysis and a solid statistical background. Seeking an analytics position to utilize quantitative acumen and early career experiences. Specialties: Machine Learning, Causal Inference (AB Testing), Social Network Analysis, Survival Analysis, Web Scraping Tools: Python, R, SQL, Tableau, MongoDB, Adobe Analytics, Spark Certificates: AWS Certified Practitioner

E D U C A T I O N

University of California, Davis San Francisco, CA

Master of Science, Business Analytics Aug. 2019 — Jun. 2020 Highlighted coursework: Machine Learning, Advanced Statistics, Data Management, Data Visualization, Optimization Decision Making Nanjing Tech University Nanjing, CHN

Bachelor of Finance Sept. 2015 — Jun. 2019

Awards: Premier Academic Scholarship (ranked #1 in major for 3 consecutive years) P R O F E S S I O N A L E X P E R I E N C E

People Fun (One of the world's top developers of casual mobile games) San Francisco, CA Data Scientist Intern Aug. 2019 — Jun. 2020

• Created players’ personas using K-means clustering based on behavioral and spending data and identified the business opportunity of the player segment with high engagement but short lifespan.

• Established a churn prediction model in Python using conditional inference survival forest to re-engage slipping away players. The model increased the 30-day retention rate by 7% (potential savings of $43,000).

• Built a personalized In-App Purchase pricing algorithm to maximize players’ lifetime value using random forest regression

(potential impact of $800,000).

• Developed behavioral metrics for modeling through exploratory data analysis over 200+ GB of users' log data on Google Cloud. Didi Chuxing (the largest ride-sharing company in China) Beijing, CHN Quantitative UX Analyst Intern Jan. 2019 — Mar. 2019

• Initiated an anomaly detection model for the daily UX metrics monitoring. Established the model based on ARIMA time series forecasting with Python. The project decreased the false alarm rate by 10% and reduced the time invested from 3hrs to 15s.

• Reduced the customer service cost incurred by a spike in drivers’ reported issues by refining the responsibility judgment algorithm. The improvement of the algorithm reduced the number of reported issues by 10%.

• Streamlined the KPI reporting system and coordinated with 7 sub-area teams of 55 members with daily anomaly diagnosis. Tencent Computer System (World’s largest video game company) Shenzhen, CHN Quantitative UX Analyst Intern Jul. 2018 – Sept. 2018

• Assessed the threat posed by a new competing networking product and developed competitive strategies by quantifying the socialization degree based on the product’s relationship network and interaction patterns using complex network model with Python.

• Developed content strategy through analyzing the topics and lifecycle of popular posts based on content data covering 2,000 posts using topic modeling.

• Addressed the issue of response bias through the simulation experiment using Python. The weighting adjustment increased the accuracy of metric prediction by 18%. P R O J E C T & C O M P E T I T I O N

User Lifetime Value Prediction based on Venmo Data (Spark Project) San Francisco, CA

• Built user’s dynamic spending profile using text analytics and emoji analytics with Spark.

• Created user’s social network characteristics using the transaction data.

• Established user lifetime value prediction model with 0.81 r-squared accuracy based on the spending profile, social network metrics, and transaction behavior metrics. 1st Place: UC Davis COVID-19 Challenge San Francisco, CA Subject: The role of bipartisanship in the battle with COVID-19 Spring quarter, 2020

• Lead a team of 4 in validating the relationship between the political affiliation and Covid-19 severity using causal inference model.

• Validated and quantified the impact of the intervention on containing the spread of the virus through panel regression in R.

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