data mining, visualization, modeling, hypothesis test,machine learning

Mengqi (Maggie) Zhao

+1-850-***-**** **************@*****.***

SUMMARY

• A proactive and results-oriented statistician with several years of experience in data analysis

• Strong presentation, problem-solving and interpersonal skills

• IT: Proficient with R, SAS (SAS Certified Base Programmer), MySQL, Python, MATLAB, C++, Tableau, MS Excel, Access, Outlook, Word, PowerPoint, Mac OS X and Windows systems EDUCATION

Florida State University (FSU) Master in Statistics Tallahassee, FL Aug. 2015 – May. 2017

• GPA:3.792/4.0 Courses: Machine Learning(A),Computational Methods(A),Time Series and Forecasting(A) China University of Mining and Technology (CUMT) Xuzhou, China Sep. 2011 – Jun. 2015 B.S. in Mathematics & Minor in Finance

• GPA:3.61/4.0 Honors: 1st Class Scholarship 2013 & 2014, Excellent Union Leader 2013 Shandong University (SDU) Mathematics Jinan, China Sep. 2013 – Jul. 2014

• GPA:3.54/4.0 Courses: Probability Theory (A), Economic Forecasts and Decision-Making (A) PROFESSIONAL EXPERIENCE

Data Analyst Intern Qilu Securities Jinan, China Jun. 2014 – Sep. 2014

• Built data queries with MySQL, conducted data visualization with Excel

• Implemented functional depth and color scale to visualize the performance of different financial products

• Built time series models in SAS, forecasted the growth trends and updated models with Mean Squared Error

• Wrote reports about model explanation for internal team and helped clients make investment decision Research Assistant National College Student Innovation Program May. 2013 – May. 2015

• Selected program: Application of the Schrödinger Equation in Nonlinear Analysis

• Performed data generation and analysis with Python to verify the existence of solutions

• Handled multiple tasks at one time, communicated regularly with co-workers, presented the work progress PROJECT & PRESENTATION EXPERIENCE

Spectral Regularization Algorithms for Learning Large Incomplete Matrices Jan. 2017 – May. 2017

• Captured business requirements from Netflix movie-rating problem, improved the movie recommendation system, predicted movie ratings to solve both missing at random and missing not at random problems

• Data set is a huge matrix with 8.6 billion potential entries but only 1.2% or 108 entries are observed

• Performed data integration and analysis in R, applied SOFT-IMPUTE and HAED-IMPUTE algorithms

• Compared the performance of algorithms by train error and test error under different conditions Neural Decoding in Motor Cortex Feb. 2017 – May. 2017

• Performed statistical analysis and data visualization with MATLAB to understand the brain mechanism

(neural activity) and make inferences about the external behaviors (hand movement)

• Built Kalman Filter and Inhomogeneous Poisson Model on train data, applied Sequential Monte Carlo

• Performed neural decoding on test data, compared all methods by estimation accuracy and elapsed time Research on Heart Disease Jan. 2016 – May. 2016

• Utilized SAS and R to predict the risk of heart disease

• Built models by decision tree and logistic regression, modified models by cross-validation, AIC, ANOVA, and formulated Likelihood Ratio Test to select a reasonable model with a 98% predictive accuracy

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