Data Analyst Sales

Shipeng Wang

** ****** ******* **, ******, Massachusetts 02128

617-***-**** adb6yr@r.postjobfree.com www.linkedin.com/in/shipeng-wang PROFILE

Data analyst with a financial background and experience in big data, machine learning, and statistics. Passionate about explaining data science to non-technical business audiences. EDUCATION

Northeastern University, Boston, MA, USA Fall 2018-Winter 2019 Master of Analytics (STEM) GPA: 3.72

• Focus Area: Data Warehousing, Data Mining, Predictive Analytics, Machine Learning Jianghan University, Wuhan, Hubei, China Fall 2014-Summer 2018 Bachelor of Science in Finance GPA:3.20

• Captain of the college basketball team, two championships in four years

• 2016 Hubei elite students overseas study tour program (Harvard University) RECENT WORK EXPERIENCE

Equity investment analyst, CITIC Asset Management Mar 2018 - May 2018

• Initiated a profound investigation into the apartment-rental industry, analyzed relationship between financing situations and occupancy rates of top 22 companies in Chinese domestic market.

• Investigated investment statements from the listed company – Shenjie Tech, prepared the business model part of inquiries for meeting, helped the financial teams to complete the due diligence. Customer data analyst assistant, Daiichi Sankyo Company Jul 2016 – Sep 2016

• Collected and analyzed the sales data of competitors, helped team to adjust promotion strategies

(boosted sales by 33 percent compared to the same month last year).

• Conducted research used in training purposes to boost company sales with new employees, led a company presentation on a new neurology drug launching. PROJECT EXPERIENCE

Australian Weather Indicator (R, Logistic Regression, PCA)

• Aimed at predicting whether it will rain in Australia the next day.

• Scraped raw data from Australia government website, used wind speed, air pressure, temperature and pressure to build logistic regression model and reached 0.826 of the F1 Score.

• PCA was used to reduce the dimensionality of the data, boosting the F1 Score by 8.35% to 0.895. Analysis for Avocado Species (R, GLM, KNN, Random Forest)

• Used retail scan data from 2013 to predict the species (organic or not) of avocado.

• Compared the accuracy of GLM, KNN, and RF models, the result was that GLM reached the highest 0.943 on Precision, RF reached the highest 0.960 on Recall, and its F1 Score is the highest, 0.943. Analysis for Puma Midwest Store (R, SQL, Python, GIS, NLP, GBR, Interactive Visualization)

• Collected 10,000 data from Twitter API by R, used the sentimental analysis to monitor customer opinion.

• Used SQL to complete data cleaning, Python was used to train the population, tax information and order volume of various cities into GBR model and give predicted sales volume of each city in Midwest USA.

• Cleveland OH and Kansas City KS were predicted to have the highest sales volume and the results were delivered to Puma analysts with interactive visualization using R Markdown. TECHNICAL SKILLS

• Advanced Excel, Tableau, AWS • Proficient in R, Python, MySQL

• Data Science Models (NLP, RM, Clustering) • Time Series Analysis, Linear/Logistic Regression Distinctions

• English (Professional), Chinese (Native)

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