Data Analyst Social Media

Tiantian Liu

*** ***** ******, *** ******, NJ ***02 412-***-**** adeqjg@r.postjobfree.com

LinkedIn: https://www.linkedin.com/in/tiantian-liu-tl/ EDUCATION

Columbia University New York, NY

Master of Science in Enterprise Risk Management (STEM) September 2018 - December 2019

• GPA: 3.75/4.0

• Course Highlights: Quantitative Risk Management, ERM Modeling, Financial Risk Management University of Pittsburgh Pittsburgh, PA

Bachelor of Science in Mathematics, Minor in German Language (Magna Cum Laude) September 2015 - April 2018

• GPA: 3.7/4.0

• Honors: 2015-2017 Dean's List, Scholarships of 2018 Culver Award

• Course Highlights: Mathematical Probability, Linear Algebra, Combinatorics, Ordinary Differential Equations, Calculus

• Exchange program in National University of Singapore for one semester SKILLS

• Programming: Python (sklearn, pandas, NumPy, matplotlib, seaborn), SQL (MySql, MS SQL Server, PostgreSQL)

• Software/Technology: Tableau, Google Analytics, Microsoft Excel, Asana, Apache Spark, @Risk, Bloomberg Terminal

• Statistics: Time series analysis, Hypothesis testing, Bayesian analysis

• Languages: Mandarin, English, German

WORK Lions Assurance EXPERIENCE Financial New York, NY Data Analyst Intern April 2020 – Present

• Collected data for over 10,000 Companies in AI, FinTech, and BioTech industries from database (Crunchbase)

• Conducted data cleaning and data validation in Python; analyzed data and reached insights for investment decision-making

• Created dashboards in Tableau to visualize important metrics of OurCrowd Funds and 2 target companies in Drone industry

• Partnered with a client to optimize their social media strategy; resulted in 35% increase in LinkedIn posting views

• Predicted the probability of a company exit by building supervised machine learning models in Python; reached 83% accuracy Phalanx Analysis Group San Francisco, CA

Data Analyst Intern August 2019 - October 2019

• Cleaned the dataset of 5,000 Spice suppliers in China using Excel functions such as VLOOKUPS and handled outliers using IQR method; analyzed the organized data using Pivot Tables; generated price insights about suppliers

• Visualized the suppliers' data by creating charts and interactive dashboard using Tableau; identified the top 5 suppliers and 3 best price regions for the client

• Improved the clients' supply chain cost-management strategies in China; reduced cost of goods sold by 10%

• Built a web-scrapper using Selenium in Python to collect hotel data in top 40 major cities in the US

• Analyzed the collected hotel data and visualized the data using Tableau; discovered 5 trends in terms of fixed costs PROJECTS San Francisco Crime Analysis in Apache Spark March 2020

• Built an ETL pipeline to analyze 2.2 million records of reported incidents from SFPD and a time-series forecasting model

• Discovered the seasonal trends and the variation of the spatial distribution of incidents based on Spark SQL and Dataframe

• Forecasted the number of criminal incidents in San Francisco per day by training and fine-tuning an ARIMA model Supply Chain Demand Forecasting and Data Management March 2020

• Developed a forecasting model in Python for a pharmacy chain store to predict future demand of nutrition products

• Analyzed over 180,000 historical sales data including visualizing data and handling missing data

• Built random forest models for carry-over products and new products respectively; tuned the parameter via grid search

• Improved the model performance by reducing 18% of the mean squared error than the baseline model A/B Testing Design and Analysis in Alteryx February 2020

• Designed a matched-pair experiment to A/B test the price of a chain spa company to increase its gross margin

• Performed an ETL process in Alteryx using tools like Filters, Join, and Summarize; matched treatment and control groups by similar trending and seasonality; analyzed the A/B testing results by A/B Testing tool in Alteryx

• Chose the optimized price and improved the gross margin by 66% per store per week

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