Data analysis

Business Intelligence

Data Visualization

Data Mining

Data Analysis

Statistical Modeling

Machine Learning / AI & Algorithm Creation

Regression Techniques

Stochastic & Deterministic Decision modeling

Market Segmentation

Perceptual Mapping

Process Improvement

Logistics Regression

Linear/Nonlinear Programming

Factor Analysis

Time-Series Analysis

Financial Analysis

Applied Statistics

Project Management

Supply Chain Management

Proven talent for aligning business strategy

and objectives with established data analytics

and supply chain management initiatives to

achieve maximum operational impact with

minimum resource expenditure. Growth-

focused leader with expertise spanning

corporate data analysis, technology solutions

and project management. Exceptionally

dedicated professional with keen inter-

personal, communication and organizational

skills, as well as vendor management,

workplace policy development and resource

allocation expertise for the diverse global

marketplace.

“STEVEN” CHEN-HSIANG MA

**********.**@***.*** 361-***-**** 1900 E. Apache Blvd. Unit 1018, Tempe, AZ 85281 EDUCATION

Arizona State University

W.P. Carey School of Business

Master of Science Business Analytics

May 2018

National Chiao Tung University

College of Management

Master of Business Administration

June 2015

National Chengchi University

College of Social Science

Bachelor of Arts Public Finance

June 2010

SUMMARY

MySQL Tableau EViews Excel

Python @Risk StatTools Word

R Minitab Azure ML PowerPoint

SOFTWARE

SKILLS & QUALIFICATIONS

WORK EXPERIENCE

Data Analyst Intern Find Your Influence Inc.

January 2017 – May 2018 Scottsdale, Arizona

Developed brand sentiment analysis which allowed FYI and its clients to understand influencers’ sentiment towards different brands and the importance of the keyword frequency of Twitter mentions.

Implemented Python to extract tweets on Twitter, conducted text processing, and applied supervised learning via Naïve Bayes to classify the tweets as “positive” or

“negative” sentiment.

The sentiment analysis accuracy rate was high, registering between a 77% and 80% true-positive rate.

Supply Chain Project Manager ASUS

July 2015 – October 2016 Taipei, Taiwan

Coordinated supply chains across 3 countries--India, Indonesia and China--to fulfill production capacity based on demand and supply planning.

Audited data in supply chain model and made timely recommendations and decisions to the product manager or the director of supply chain.

Formulated supply chain safety inventory strategies to ensure the delivery of final products.

Maintained vendor relationships with domestic and international suppliers and subcontractors.

Directed cross-functional efforts with logistics, quality control, finance, sales, engineering, and procurement department to optimize capacity, raise the efficiency of the manufacturing flow, and troubleshoot problems. Marketing Associate/Analyst Merck

June 2014 – June 2015 Taipei, Taiwan

Assessed quantitative sales data and ad-hoc analysis of cardiovascular prescription medicine sold in different distribution channels.

Partnered with product managers to develop and present monthly sales analysis report to senior management.

Projected adjusted marketing strategy and sales target with product managers.

Liaised with product managers and human resources to improve materials for quarterly training sessions.

Financial Specialist/Analyst Cathay Real Estate

August 2011 – August 2013 Taipei, Taiwan

Forecasted corporate cash flow and balanced corporate cash account with banking channels for financial operations.

Executed comparative financial ad-hoc and cost-benefit analysis by evaluating financial indices and statements of leading competitors.

Collaborated with IT specialists and outsourced IT vendors to optimize Oracle ERP system workflow and to construct efficient financial database. Page 1 of 2

Applied Statistics

ANOVA, regression analysis, time-series analysis, forecasting Tools: Microsoft Excel, R, Python, StatTools, Minitab Machine Learning

Classification methods, clustering methods Tools: Microsoft Azure, Python, SAS Text Analysis

Sentiment analysis, data & text processing Tools: Python, R, Tableau Decision Modeling

Linear/non-linear programming, simulation & optimization modeling Tools: Microsoft Excel, R, Python, @Risk, StatTools Enterprise Database Management & Fundamentals

Data modeling, database query, relational & dimensional DBs Tools: MySQL, Tableau Econometrics

Time-series analysis, ARIMA model, GARCH model Tools: EViews, R, Excel, StatTools, Minitab

“STEVEN” CHEN-HSIANG MA

**********.**@***.*** 361-***-**** 1900 E. Apache Blvd. Unit 1018, Tempe, AZ 85281 PROJECTS

2018 Diamond Dollars Case Competition Society for American Baseball Research (SABR) Applying statistical and modeling skills combined with machine learning algorithm to find the optimal solution Analytical Tools: Python, Excel, Azure

Determined the optimal launch angle for four MLB hitters.

Selected 30 players with similar physical and performance metrics by using Euclidean distance measures. Visualized the data of 30 players during 2015 - 2017 seasons to find the batters’ best potential batted-ball angle.

Built a model simulating all the possible pitching and batting scenarios that the subject players could face when at bat by utilizing probability distributions based on large amounts of historical data.

Applied random forest model (scikit-learn package in Python, Azure) to predict the possible at-bat outcomes by training the algorithm with historical data that included every single pitch and at bat from the subject players’ 2015 to 2017 seasons. Capstone Project - Text Mining and Sentiment Analysis Find Your Influence Inc. (FYI) Developed brand sentiment analysis which allowed FYI and its clients to understand influencers’ sentiment towards different brands and frequency of mentions on Twitter. Analytical Tools: Python, Excel

Implemented Python to extract tweets on Twitter, conducted text processing, and stored tweets in Pandas data frame.

Built a data set of thousands of sentences. Each sentence was categorized as “Positive” or “Negative” sentiment, for the training data set. Finally, applied supervised learning via Naïve Bayes to classify the tweets as “positive” or “negative” sentiment.

The sentiment analysis accuracy rate was high, registering between a 77% and 80% true-positive rate. Solar Energy Model – Analytical Decision Modeling

Develop and analyze a simulation model of solar power generation and electricity costs. Analytical Tools: Excel, StatTools, @Risk

Created a multiphase simulation model that simulated hourly, daily and annual sunshine and weather patterns to predict solar power generation and energy demand and cost

Reinforced skills in developing predictive models from data, designing and implementing a complex simulation model and conducting analysis of the model to identify prescriptive solutions for decision makers. Page 2 of 2

ACADEMIC TRAINING

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