商业分析专业
Rachel
可提供服务: 文书修改 文书写作 全套申请 选校定位 网申 申请咨询 签证咨询
可服务区域/国家: 美国
已助力 27 人实现梦想 准时率:99.9%
综合评分:
简介
本科毕业于美国前三十的伊利诺伊大学香槟分校,统计学与工业工程运筹学双学士学位,DIY完成本科硕士申请。研究生主申BA和DS,收到USC,Duke,哥大,WUSTL,UMN,UT Austin,USF等学校的offer。研究生毕业于德州大学奥斯汀分校Business Analytics专业(BA第一的神校)。现就职于美国旧金山downtown一家Fintech公司,职位是数据分析师。经历过在美转专业申请的血泪史,具有丰富的统计/数据科学/商业分析申请经验。
教育背景
德克萨斯州大学奥斯汀分校
/硕士
/商业分析
伊利诺伊大学厄巴纳-香槟分校
/学士
/统计学 /工业工程
专长
实习经历
Company – Data Analyst Intern ● Collaborated in cross functional team, gathered data from 1.5M insurance services with SQL, conducted statistical analysis with SAS and visualized them with Tableau dashboards ● Identified how roadblocks of healthcare insurance claims change with age and provided data-driven solutions for members to prepare for most frequently denied medical procedures to reduce claim denied rate
工作经历
Company – Data Analyst, San Francisco, CA ● Worked cross-functionally with Risk Management and Operation teams extracting, manipulating and analyzing First Payment Delinquency Performance of over 149K+ personal loan records with SQL and built visualization dashboards on Mode Analytics to monitor delinquency performance and recognize the early signal of potential charge-off ● Took the initiative to analyzed loss rate trending, mined delinquency behaviors to differentiate applicants, conducted Time Series analysis in Python to predict future delinquency performance and successfully achieved 83% accuracy ● Conducted Feature Selection and Segmentation Analysis with Logistics Regression and Classification Trees using Python to find important and stable features for application model, handled unbalanced data with K-fold cross-validation and resampling, and reported significant findings for business decisions Company – Data Scientist Intern, Austin, TX ● Predicted customers’ next transaction based on their previous transactions with large data set of 175K+ customers using LSTM model, input transaction sequence and output with probabilities of transactions, and obtained 69% accuracy for fraud detection ● Gained business insights via Exploratory Data Analysis with Tableau, and validated the existence of transaction patterns ● Conducted word embedding (Word2Vec and Doc2Vec) on transaction series, used cosine similarity to cluster customer transactions and segment customers, and utilized T-SNE to visualize the clustering on Google Cloud Platform Company – Supply Chain Data Analyst, Morton, IL ● Led 5 other interns to optimize the yard and successfully improved the picking process by 41% ● Manipulated database of 10K+ historical data, retrieved data with SQL, built predictive model on seasonal differences, and visualized relationship between items with R ● Implemented integer-programming models with C++ and CPLEX, simulated picking process with Python, and built an Excel-based tool to realize repeatability of the optimization for future projects
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