Assisted IR team of writing 200+ pages investor presentation materials, hosted 4 conference calls and assisted the IR Team to reach out 300+ investors in total;Analyzed capital market performance of Vobile with data collected from Wind and Bloomberg;Conducted 20+ AB tests to improve the Youtube operation performance;Used Python to collect oversea performance data of 20+ local TV stations, wrote 5+ pages industrial report based on the results of group statistics to motivate business developer to attain premium content.

Assisted of teaching in PMBA6092 Python for Managers and Strategists for about 40 MBA students both in lectures and tutorials. Offered 1 weekly assignment tutorial.Provided irregular supports for 2 group projects on technical problems.

Used Python to analyze the 1500+ rows of data on overseas-listed firms in China and concluded insights concerning listing year, industry, headquarters and etc.Researched on overseas-listed firms in foreign countries including Britain, Brazil, Israel and etc and concluded the reasons of listing in New York Stock Exchange (NYSE) and Nasdaq.Researched on Special Purpose Acquisition Company (SPAC) in United States, summarized similar listing structures like "Listed company +Private Equity (PE)", backdoor listing and listing of venture capital enterprises in China, and assisted post-doctors of writing and revising 5+ pages research brief.

Communicated with 15+ media on advertising and collected the advertising indicators like Impression and Clicks.Used Python to clean and analyze 250+ advertising placements of data and completed 10+ data report to optimize the advertising planning and improve the conversion rate.Applied CTR, iResearch, Jirang to scratch the data of advertising fee; wrote 5 competitive analysis reports with 120+ pages to help the clients know about the industry performance and make adjustments.

Used Python to clean and process 1,300,000+ rows of Amazon's e-commerce transaction data in the UK, identifying sales data attribution, customer segmentation, customer profiling to forecast customers’ purchase behaviors, and visualized the results and applied the RFM model and K-means clustering analysis to segement customers into 8 and 4 types.Used nltk package in Python to identify the nature of the product and categorize the products into 8 types based on the results, implemented mlxtend to determine the correlation among products within product portfolios and proposed display strategies for product portfolios on Amazon.Executed the logistic regression method to predict customers' willingness to spend, and utilized the confusion matrix and accuracy score to evaluate the model’s effectiveness.

Collected and organized relevant information of 20+ potential target companies; analyzed their operating conditions, business models, financing conditions, etc. to select potential listed companies;Conducted desk research and competition analysis of the tea and beverage industry to identify barriers and patterns of the industry;Combined industry conditions to develop capital strategies for potential target companies, and assisted the whole team of writing 40+ pages IPO project proposal;Performed industry chain research on the photovoltaic industry, analyzed industry drivers and industry development trends.

Conducted data mining and data visualization based on quantitative surveys of 500+ participants and summarized the key insights including development opportunities and challenges regarding the public welfare industry.Analyzed qualitative data, produced the keywords for a client’s brand, and used “The Big Ideal” model to position the specific brand based on current industry conditions and helped to complete 120+ pages workshop slide.Performed qualitative and quantitative analysis to profile clients and drafted a company’s marketing plan based on client profiles; assisted in finishing 150+ pages branding and marketing strategy report.

Assisted the clerk at the counter to do the customer service, including opening an account, downloading the China Galaxy Securities APP, teaching the usage of the APP and helping clients know the transaction information.Collated of data of the clients and pigeonhole the corresponding files, which is convenient for tracking the clients' transaction and dealing with the potential problems.