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Data Analyst Python

Location:
La Jolla, CA
Posted:
March 14, 2020

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Resume:

Shu Liu

530-***-**** adcawm@r.postjobfree.com linkedin.com/in/shuliuccb/ sites.google.com/ucsd.edu/shuliu Core Capabilities

• Passionate, data-driven problem solver with experience in data science, machine learning, business intelligence

• 3+ years hands-on experience in big data analytics, predictive modeling, data visualization via Python, R, SQL and Tableau Education

Master of Science in Quantitative Finance (STEM), University of California San Diego, La Jolla, CA, U.S. Sep 2019 – Dec 2020

• Courses: Collecting and Analyzing Big Data, Customer Analytics, Fraud Analytics, Business Intelligence Systems (Tableau), Recommender Systems, Digital Product Management, Computational Financial Method Bachelor of Financial Engineering, Shanghai University of Finance and Economics, Shanghai, China Dec 2014 – Jul 2018

• Courses: Financial Engineering, Database Theory, Mathematical Analysis, MATLAB and Application, C++ and Application

• Awards: Graduated with Summa Cum Laude, Outstanding Dissertation Award (15/431) Specialized Skills

• Programming: Python, R, SQL, Tableau, MATLAB, Power BI, Scala, C++

• Tools/Software: Microsoft Office Suite (Excel (VBA/Macro/Pivot Table) /PowerPoint/Access), Google Analytics, Apache Spark, Amazon Web Services (AWS), Google Cloud, Bloomberg, Git, Linux

• Certifications: IBM Data Science Certificate, Tableau Desktop Certified Associate, Advanced Google Analytics Certificate, Financial Risk Manager (FRM) Part I Passed, Amazon Web Services (AWS) Certified Solutions Architect candidate Professional Experience

Data Analyst Manager, Vibe Inc, La Jolla, CA Mar 2020 – present

• Lead database building from multiple locations via SQL and Python, conduct statistical analysis and define key metrics, boost company profit by using machine learning, deep learning to mine and analyze customer segmentation and behaviors.

• Transform the execution team’s business problem into technical solutions, collaborate with product teams to analyze product new features and engineers to update database for future analysis, visualize results in Tableau, present to executive regularly. Research Assistant, University of California San Diego, La Jolla, CA Sep 2019 – Mar 2020

• Lead manipulation of 200+ GB nationwide census and customer demographic data from various resources, build automatic pipelines via SQL and Python, visualize results by creating dashboards by Tableau, improving efficiency by 40%.

• Develop data modeling, present to supervisor regularly, experiment machine learning (PCA/Random Forest/XGBoost) and deep learning tools (Sklearn/Keras/caret) via Python and R, optimizing prediction accuracy by 18%, increasing profit by 30%. Data Analyst, China Construction Bank, Suzhou, China Jul 2018 – Mar 2019

• Lead analyzing customer digital and campaign data by SQL and R, increased click rate and company revenue by detecting new relationship, led building optimized model which tailored recommendations to each customer, user amount surging by 15%.

• Worked cross-functionally with product team and engineers in transforming business logics into requirements, leveraged Python in web scraping (JSON/API), data cleaning and statistical analysis, used machine learning tools, reducing workload by 30% and saving operational cost by 15%, assisted in ad-hoc analytics requests.

• Ensured weekly interactive dashboards updated via Excel and Tableau, interpreted business insights to senior management. Projects

Predicting Promotions and Response Probability Modeling

• Lead variable selection and predictive modeling to personalize promotions for 5M+ customers, use Python and R to analyze customer behaviors and purchase history, optimize models with machine learning and neural networks, boost accuracy by 30% and profit by 35%, visualize and present on the testing strategies by R (ggplot/Shiny) and Python (seaborn/Bokeh). Predicting House Prices in NYC at Neighborhood Level using Hedonic Pricing Model

• Write advanced SQL scripts, scrape and manipulate census and venue data via Python (Urllib/Beautiful Soup) and Foursquare API, design functions for geographic visualization in Python (Plotly/Folium), lead 100+ features’ selection and providing useful insights, improve model accuracy via machine learning algorithms by 16%, deliver business insights via Tableau.



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