Data Management

SIHONG (KELSEY) CHEN

** ***** **, *** ****, NY, *0044 917-***-**** ********@*******.***

SKILLS AND ABILITIES

Technical Skills: SQL, Mysql, Tableau, Oracle, R, MATLAB, SPSS; Python, C; E-views, Microsoft Office, MS Excel (VBA, VLOOKUP) Languages: Fluent in English & Mandarin (Native)

Interests: Baking, Cooking Chinese dishes, Diving, Traveling EDUCATION

2017-2018 FORDHAM UNIVERSITY, GABELLI SCHOOL OF BUSINESS New York, NY M.S., Applied Statistics and Decision Making

Relevant Coursework: Applied Regression Analysis, Data Mining, Auto Trading Systems, Database Management 2013-2017 SOUTHWESTERN UNIVERSITY OF FINANCE AND ECONOMICS Chengdu, China B.Ec., Economics; B.S., Mathematics

Relevant Coursework: Financial Statistics Analysis, Numerical Analysis, Financial Risk Management 2nd Class Scholarship in 2015-2016 Academic Year

2016-2017 FU JEN CATHOLIC UNIVERSITY, SCHOOL OF MANAGEMENT New Taipei, Taiwan Exchange student; Global Finance Major

WORK EXPERIENCE

Present CMRubinWorld New York, NY

Product Management

• Presented big idea of the new product and find hit point to present it in order to get the sponsor

• Contribute to creative strategy and content creation; Twitter account management Summer 2017 China Securities Co., Ltd Chengdu, China Data Analyst Intern, Funds Department

• Visualized data, develops and creates daily reporting related to various types of business information – EXCEL, Tableau, PPT

• Responsible for data infrastructure management, data management tools, data modeling, database procedures, data integration services; Provided executive information management reporting as required – SQL

• Coordinated with fund manager on portfolio management using Logistic Regression and Decision Tree methods; 10+ routinely formatted investment documents – Python

• Participated in external funds research activities and investment consulting events and classes; Sent memorandum to clients Spring 2016 Industrial & Commercial Bank of China Neijiang, China Business Analyst Intern, Key Client Department

• Precisely and accurately inspected third-party financial reports; evaluated verified capital structure, managed acceptance bills for partner companies with RFM analysis – EXCEL

• Surveyed operating status of 3 multi-million-size companies in: construction, chemical, and real estate industries with specific focus on income statements and balance sheets

• Examined by-law process to determine periodic interest payments, loan balances, and payback periods; Competently managed commercial loan portfolios and supported the credit administration processes for prominent companies RELEVANT ACADEMIC PROJECTS

Fall 2018 Database Management for Fordham Clinic – SQL, Oracle

• Used Oracle Data modeler to develop a logical model (ER diagram) identifying objects that the clinic needs to track and relationship between the objects

• Utilized DDL to create tables, constraints, supertype, and subtype relationship; DML to load and manipulate the database to build patients' reports; DCL to control database

Fall 2018 Vehicle Violation Analysis and Prediction– Python, SPSS

• Utilized data of 2018 parking violation tickets, separated features by meanings, selected variables by visualization with target

• Preprocessed data and transformed features into more related features to prediction, cleaned meaningless data and features

• Built decision tree and artificial neural network models with defined functions; Applied models on different depth and decided the best model based on accuracy score

Spring 2018 2018 Stock & ETF Trading Competition – Earned 2nd prize with a total return of 5.16% – MATLAB

• Back-tested trading algorithms and selected potential stocks using the MACD method; practiced trading works with Interactive Brokers TWS in the US stocks market

• Created GUI interfaces for portfolio management strategies, covering RSI and MACD back-testing functions and auto-trading functions

Spring 2018 Deloitte Competition: March Data Crunch Madness - Modeler & Programmer – SQL, SPSS, R

• Collected, processed, and analyzed March 2017 National Collegiate Athletic Association data with SQL, SPSS and R

• Visualized logistic regression results with Tableau and effectively determined key indicators for forecasting activities

• Initiated Log-loss model and addressed validation works with 2018 operating data, moderating forecasting data on the basis of weighted score, auto-correlation, and redundancy indexes with machine learning methods

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