Data Science Los Angeles

Peiyao Zhu

206-***-**** *******@*****.*** linkedin.com/in/peiyao-zhu

EDUCATION

University of Southern California Los Angeles, CA Dec 2024 Master of Science in Applied Data Science GPA: 3.75/4

● Courses: Experimental Design, Big Data, Data Mining, Database Systems, Natural Language Processing, Deep Learning University of Washington Seattle, WA June 2022

Bachelor of Science in Applied Mathematics Minor: Data Science, Informatics GPA: 3.71/4 SKILLS

● Programming: Python, SQL (MySQL, PostgreSQL), R, Scala, Java, MATLAB, Git

● Data Tools: Tableau, Power BI, Google Analytics (GA4), AWS (Redshift, DynamoDB, EC2, S3), GCP (BigQuery), Hadoop, Spark, MongoDB, Matplotlib, Seaborn

● Data Science & Analytics Techniques: Data Cleaning, Data Warehousing, Data Modeling, Statistical Analysis, Machine Learning, Forecasting, Segmentation, A/B Testing & Experiment Design, Hypothesis Testing PROFESSIONAL EXPERIENCE

fAIshion Los Angeles, CA

Data Science Analyst Mar 2025 - Present

● Increased monthly active users for an AI-powered browser extension by 33% by developing and maintaining Tableau dashboards and Google Analytics (GA4) to monitor user behavior KPIs

● Developed data pipelines using Python and SQL to clean and transform millions of raw user event logs into business metrics, identify trends, and perform QA checks to ensure 100% reporting data accuracy

● Authored 10+ analysis reports using VLOOKUP and Pivot tables in Excel to translate complex product data insights into actionable strategies, supporting internal stakeholders to understand product performance

● Implemented automated GA4 to BigQuery data export pipelines processing 1M+ user events, reducing manual reporting time from 20 hours to 2 hours weekly and enabling real-time analytics

● Managed Jira workflows for multiple data projects, collaborating with PMs to prioritize 20+ ad-hoc data and QA requests, accelerating project delivery timelines by 30% and fostering cross-team productivity Community School of Arts Foundation Los Angeles, CA Data Scientist Feb 2025 - Present

● Performed exploratory data analysis on 100K+ records of CRM data via Python to analyze and surface key donor acquisition drivers, improving donor targeting efficiency by 20%

● Built a predictive model using LightGBM and scikit-learn to predict donor likelihood based on geographical and financial attributes, enabling targeted outreach that boosted donor acquisition by 20%

● Collaborated with a 3-member team using GitHub to manage and deploy data projects across environments Amazon Seattle, WA

Business Intelligence Engineer Intern May 2024 - Aug 2024

● Drove a 30% reduction in brand infringement by building a deep-dive analytic dashboard using AWS QuickSight, serving 50+ Brand Protection team members for KPI monitoring and risk management

● Reduced manual effort by 18 hours/week by deploying 10+ ETL processes using AWS Glue, consolidating seller-related data from 30+ tables into 250+ KPIs for streamlined analysis and reporting

● Developed advanced SQL scripts to extract detail-level infringement metrics from 15 TB+ raw e-commerce data stored in AWS Redshift cloud data warehouse, accelerating root cause analysis by 40%

● Designed and implemented dimensional data models to transform risk reporting requirements into efficient warehouse structures, improving data integrity and consistency

● Presented seller data story to leadership and non-technical stakeholders, translating complex fraud analytics data into compelling narratives that secured approval for abuse restriction strategy initiatives

● Collaborated with cross-functional teams and stakeholders utilizing Agile methodologies to align data initiatives with business objectives and conduct quality assurance, ensuring successful completion of projects China Guangfa Bank Foshan, CN

Data Scientist Intern Sept 2020 - Nov 2020

● Improved feature click-through rate by 24% by analyzing 100M+ user event logs from a mobile application using Python

(pandas) and SQL, providing insights for product analysis

● Streamlined business reporting by creating automated Power BI dashboards for conversion tracking, boosting operational efficiency by 70% and enabling real-time performance monitoring

● Analyzed millions of user records using Excel's statistical functions and regression analysis, identifying trends that increased customer retention by 23%

● Collaborated cross-functionally with diverse stakeholders, collecting requirements and creating monthly user story reports highlighting shifts in industry trends and their impact on customer expectations and behaviors

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