Data Analyst Python

Mingcan Lyu

781-***-**** **********@********.*** www.linkedin.com/in/mingcan-lyu

SUMMARY

Seeking full-time data driven analytical position

Hands-on experience of data analysis in fin-tech company and credit risk data analysis in commercial banking

Solid knowledge of statistics, programming and data visualization

Excellent analytical, interpersonal and communication skills

TECHNICAL SKILLS

Computer Skills

Python, R, SQL, Excel VBA, SAS, Tableau, Microsoft Office, Bloomberg, Capital IQ

Models

Regression Model, Time Series Analysis, Machine Learning

Certifications

Passed FRM Part II, CFA Level III Candidate

EDUCATION

Brandeis University, International Business School

Waltham, MA

Master of Science in Financial Mathematics, Risk Management (STEM) GPA: 3.62/4.00

08/2018 – 12/2019

Coursework: Analyzing Big Data, Machine Learning, Information Visualization, Financial Risk Management, Computer Simulations and Risk Assessment, Portfolio Financial Modeling, Options and Derivatives, Fixed Income Securities

Xiamen University

Xiamen, China

Bachelor of Science in Marketing GPA: 3.88/4.00 (Top 5%)

08/2014 – 06/2018

EXPERIENCE

ForwardLane Inc.

New York, NY

Data Analyst

07/2019 – 09/2019

Implemented project management on multiple Natural Language Processing (NLP) and Machine Learning projects

Maintained and expanded training dataset for Named Entity Recognition (NER) project by scraping and labeling financial and economic related articles from morningstar.com and reuters.com

Validated model of NER project and tracked performance of each entity; redefined entities with poor performance

Collaborated with Director of Data Science and Product Manager cross-functionally to brainstorm the search algorithm for Keyword Aggregation project; created and updated project status report and project documentation

Scrapped potential customers’ data from multiple sources to construct keyword database; wrote SQL queries to search articles containing exact keywords and scored relevancy of the articles manually

China Minsheng Bank

Shanghai, China

Credit Risk Data Analyst

01/2018 – 03/2018

Assessed current market conditions by gathering macroeconomic data and related government policies to support decision of underwriting commercial mortgage loan (50 million CNY)

Cleansed and visualized market data and forecasted market trend utilizing EWMA and GARCH model in Python

Audited applicant’s feasibility report and analyzed the profitability and operating capacity of the expansion project

Evaluated property value based on third-party appraisal report and calculated ratios to assess risk quantitatively

Identified potential risks of the project, including market risk, mortgage risk, construction process risk and future sales risk, and proposed corresponding risk management measures

Completed 10+ pages loan evaluation report in a team of four and presented the report during monthly meeting

ACADEMIC TEAM PROJECT

Brandeis University

Waltham, MA

Credit Risk Modeling by Analyzing Big Data in R and SAS

10/2019 – 12/2019

Collected 50,000 records of U.S. residential mortgage data, including macroeconomic variables, loan variables and borrower profile variables, at origination and observation time

Wrangled and preprocessed data to handle missing values and outliers; performed exploratory data analysis in SAS

Built predictive models for loan status based on machine learning algorithms (logistic regression, KNN, SVM, decision tree and random forest model) in R and compared accuracy and interpretability of different models

Drove business insights from the result and applied the models to detect early signals for troubled loan

Brandeis University

Waltham, MA

Portfolio Construction and Stress Testing in Python

03/2019 – 05/2019

Constructed portfolio including diversified assets based on tracking error minimization and risk-parity strategy

Fitted linear regression model between macroeconomic variables and portfolio return; performed stress testing by loading in macroeconomic data under adverse and severely adverse scenarios from Federal Reserve's CCAR files

Calculated portfolio VaR and CVaR under different scenarios to testify its ability to absorb potential risk

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