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Python Data

Location:
Kew Gardens, NY
Posted:
May 19, 2020

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Resume:

Yipeng (Leslie) Wu

Philadelphia, PA ***** 312-***-**** addb1d@r.postjobfree.com

Extensive experience with data mining, machine learning, statistical modeling and programming. Experience with creating scalable and automated data-driven solution on large-scale data to provide business insights. Proficient in Python, SAS, SQL and R. EDUCATION

TEMPLE UNIVERSITY, The Fox School of Business, Philadelphia, PA August 2018 – May 2020 (Expected) Master of Science, Quantitative Finance and Risk Management, GPA 3.69/4 Selected coursework:

Machine Learning (Python), Data Science (Python), Financial Econometrics (SAS), Financial Time Series (SAS), UNIVERSITY OF CALIFORNIA, SAN DIEGO, San Diego, CA August 2010 – June 2014 Bachelor of Science, Mathematics & Economics

PROFESSIONAL EXPERIENCE

Nielsen, Shanghai, China January 2020 – March 2020 Data Scientist - Analytics Intern (Remote)

• Implemented Sales Data Integration process in SQL and R to collect sales data from various product distribution channels and align it to the field force geographies applying all the business rules and exceptions that reduced collection process time by 40%.

• Performed Root Cause Analysis for drug labels and provided consultancy to optimize clients’ brand planning strategy

• Developed dynamic automated dashboards on Tableau capturing trend analysis, competitor performance summaries and other key metrics to monitor real-time data quality and performance

• Communicated with clients on regular feedback of the ongoing incentive compensation plans by monitoring business KPI such as

% of making quota, % of annual earning, % of incentive paid by product, and % of forecast achieved etc GLOBAL AI, New York, NY June 2019 – August 2019

Quantitative Research Intern

• Developed automated algorithm on Python to handle data web scraping and query, data imputation, statistical hypothesis tests and statistical analysis that saved team effort up to 10 hours weekly

• Assessed geopolitical risks and sentiment score for targeted companies and countries by synthesizing over 30+ GB equity transaction data from cloud-based data source using Python

• Optimized middle-high frequency trading strategies by analyzing sentiment scores and data exploration with themes/sectors, news volume, market capitalization for applying in time series analysis; stored and managed processed dataset on AWS

• Collaborated with portfolio manager, traders and data team to execute portfolio strategies and achieve the goal of annual returns

• Created scalable and automated process to manage equity portfolio to balance return and risk by frequencies and strategies. BANK OF COMMUNICATIONS, Zhuhai, Guangdong, China January 2015 – December 2016 Strategy Data Analyst, Private Banking Department

• Built and maintained weekly/monthly clients’ investment transaction database on SQL with 50K+ clients and 10 million+ dollars AUM for Private Banking Department in charge of 22 sub-branches

• Led the research on ways to enhance sales strategy by seasonal identifying and targeting highest value and potential clients through setting queries to analyze clients’ transaction history and explore corresponding consumer indices; cooperated with sales team on strategy execution and helped the team to rank at top 3 among 21 branches for the year of 2015 and 2016

• Presented research results and solutions to senior management and peer analysts and explained quantitative results to nontechnical audience through data visualization and presentations ACADEMIC PROJECTS

Predictive modeling - Hotel Booking (Python) February 2020

• Created descriptive analysis and correlation analysis on the hotel booking dataset and identified relevant variables for predictions.

• Implemented Python functions to perform data quality check and impute missing values by PCA algorithms

• Performed logistic regression and generalized linear model to train the data and predict the hotel booking rates

• Optimized logistic regression by applying stepwise regression to choose the best sets of explanatory variables Credit Risk Analysis - Logistic Regression (Scikit-Learn/Python) September 2019

• Created visualizations and preliminary analysis on customer’s historical loan application and credit data

• Trained and tested dataset after One-Hot Encoding and PCA and evaluate the performance of multiple logistic regression model

• Compared accuracy of multiple algorithm model and optimized model accuracy and efficiency with GridSearchCV SKILLS AND CERTIFICATE

• Technical Skills: Python,SQL, R, Tableau, Excel VBA, Matlab, SAS, AWS, SPSS, eViews, Bloomberg

• Certificates: SAS (Basic & Advanced), FRM Level I Candidate



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