Analyst Intern Data

YINGJIE JIN

***.*******@*****.*** 314-***-**** www.linkedin.com/in/jin-yingjie

EDUCATION

WASHINGTON UNIVERSITY IN ST. LOUIS, ST. LOUIS, MO December 2022 Master of Science in Business Analytics – Customer Analytics GPA: 3.90/4.00

• Degree qualifies for a STEM designation; eligible for 36 months of OPT (12 months OPT+ 24 months of extension) SHANGHAI UNIVERSITY OF INTERNATIONAL BUSINESS AND ECONOMICS, SHANGHAI, CHINA June 2020 Bachelor of Business Administration – International Business Management GPA: 3.89/4.00 PROFESSIONAL SKILLS

• Programming Skills: SQL, Python, Tableau, Power BI, Java, R, Office Suite, SAS, Hadoop, AWS Redshift and ArcGIS

• Analytical Skills: Data Science, Deep Learning, A/B Testing, Text Mining, Big Data, Customer Analytics, GIS Analytics, Database Management, Brand Management, Digital Marketing, Market Strategy EXPERIENCE

AFFINITY SOLUTIONS, New York June 2022 – December 2022 Data Analyst Intern (SQL, Python, Tableau, Jira, Excel, PowerPoint, AWS)

• Built reporting dashboards in Tableau through self-built SQL queries connected to AWS Redshift to manage standard reports to sales team, including cross-shop, customer loyalty, brand health and churn reports

• Utilized machine learning techniques (logistic regression models) in Python to extract actionable insights (identify switchers, predict brand propensity score) about trending customer purchase behaviors

• Managed quality of large datasets (2M records) by identifying categorical and numerical parameters and causation of outliers; provided suggestions for elimination of outliers and enhancement of database efficiency

• Aligned customer data with internal database for effective modeling, targeting, and measurement; conducted A/B testing to evaluate advertising campaign performance and reported campaign effectiveness to clients BAOZUN, Shanghai, China April 2021 – July 2021

Digital Marketing Analyst Intern (Excel, Power BI, Power Query, Power Point)

• Analyzed customer behavior metrics and KPIs using demographic and sales data in CRM system (Python) to clean data and recommended marketing strategies to target more customers (Power BI)

• Conducted a comprehensive analysis of previous promotional campaign results, by comparing sales figures of various customer groups and competitors, to discern the strengths and limitations of the current product line

• Generated reports to present strategies and business insights to improve customer experience and suggested practical digital marketing strategies (budgets and channel distributions for each category) for big promotions PROJECTS

Humana-Mays 2022 Sixth Annual Healthcare Analytics Case Competition, Top 5 Finalist September 2022 – November 2022

• Cleaned and formatted data and executed Feature Selection, using dataset with 48,000 rows and 800+ features, filtered Nan values and encoding categorial variables, used Gini Index, Random Forest and XG Boost classifier methods to select main variables in Python package: scikit-learn, Pandas and NumPy

• Developed a predictive model in Python to identify populations vulnerable to housing insecurity. Selected the best performing machine learning model and analyzed key features; used K-Means Clustering to segment individuals into four clusters to uncover housing insecurity conditions

• Presented actionable recommendations for housing security improvement program (good home selection, finance assistance) to Humana data science department and professors.

Schnucks Customer Purchase Satisfaction Analysis, Center for Experiential Learning- Wash U January 2022 – May 2022

• Analyzed transactions and NPS score datasets containing 100k distinct customers and 126 store information using Python and RStudio and analyzed relationship between NPS and customer behaviors at store level

• Applied 20+ models based on Logistic Regression, Linear Regression, Random Forests to predict customer’s purchase decisions on different categories and departments with specific promotion methods

• Reported the relationship between Detractors and Promoters on purchase behavior and made recommendations to improve the service of specific department

2022 Manhattan College Business Analysis Competition, Third Place Finalist January 2022 – April 2022

• Analyzed variables in terms of reopening, COVID status and life quality that related to the resilience status of countries after pandemic by organizing the Bloomberg dataset, JHU COVID dataset and Oxford Policy dataset in R

• Built robust model to predict the resilience score by utilizing key variables and proposed suggestions to countries clustered by human development level; presented to judges and professionals ACTIVITIES

• 2022 Informs Analytics Conference

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