Yilin Wang

Nanjing,Jiangsu

Summary

Driven to learn quickly, interest in Information Technology good problem solving ability, strong adaptability and training in industry operations. Background in both chemistry and business supporting team needs. Flexible and hardworking team player focused on boosting productivity and performance with conscientious and detail-oriented approaches.

Overview

year of professional experience

Work History

Intern

CHIA TAI TIANQING PHARMACEUTICAL GROUP

08.2022 - 11.2022

Gained valuable experience working within a specific industry, applying learned concepts directly into relevant work situations.
Prepared comprehensive reports summarizing data analysis findings, informing key decision-makers of important trends and patterns.
Sorted and organized files, spreadsheets, and reports.
Provided project progress updates and proposed solutions to issues.

Volunteer

CSSA-MAN

09.2021 - 09.2021

Assisted with Orientation Week
Supported engaging, fun, and smooth-running Orientation Week by helping with organization and planning.
Used strong interpersonal communication skills to answer questions about daily life from new students
Guided new students to familiar with campus buildings

Education

Master of Business - Information System

University of Queensland

Brisbane, QLD

12.2024

Bachelor of Science - Chemistry

University of Manchester

Manchester UK

06.2022

Skills

Teamwork and Collaboration

Quick Learner

Problem-Solving

Multitasking and Organization

Interpersonal Skills

Analytical mindset

project experience

- To predict the future churn rate of customers for banks based on labeled data using Python language.
- The correlation between features and with dependent variable is compared by EDA and visualized using Matplotlib and Seaborn.
- Data split is performed by stratified sampling and one-hot encoding is used to convert the categorical features into numerical.
- Train the model with logistic regression, random forest and KNN respectively and apply regularized tuning to reduce the Overfitting problem. Find the optimal model by cross-validation. Analyze feature importance or coefficient to find out the factors that most affect the results.

Timeline