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Data Analyst Python

Location:
Walnut, CA, 91789
Salary:
70000
Posted:
September 09, 2020

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Resume:

Nathan Huang

Los Angeles, CA ***** 434-***-**** adfyba@r.postjobfree.com

EDUCATION

University of Virginia Charlottesville, VA

• Master of Science: Statistics Sep 2017 - May 2019 Core Courses: Experimental Design, Data mining, Machine Learning, Statistical Consulting and Statistics Inference City University of Hong Kong Hong Kong, China

• Bachelor of Science: Computing Mathematics; Minor: Finance &Accounting Sep 2012 - May 2017

• Exchange Program at Acadia University, Canada Sep 2014 - Dec 2014 WORK EXPERIENCE

Global Security Solutions Los Angeles, CA

Data Analyst Jun 2020 - Present

• Communicated with stakeholders on how to design price strategy based on supply and demand

• Collaborated with cross-functional teams like engineering and finance teams and made business hypotheses

• Pulled data from multiple sources and performed the ETL process and EDA in SQL and Python

• Developed forecasting model (Random Forest, Regression) to predict inventory and demand in Python

• Presented business insights to stakeholders, improved NPS by estimated 80%, and revenue by estimated 20% Rang Technologies Piscataway, NJ

Data Analyst Aug 2019 – Jun 2020

• Generated business problems from stakeholders like the decrease on Airbnb booking revenue

• Developed predictive model using Python to predict price based on different property types and neighborhoods

• Designed KPIs and metrics like revenue by country, NPS (net promoter score) and average booking price and create dashboard in Tableau

• Presented insights behind data and helped company to save estimated monthly loss of $2.6M and increase estimated booking rate by 58%

Global AI New York, NY

Data Scientist Intern Oct 2018 - Nov 2018

• Consulted with Yelp clients and provided business recommendations to maximize their revenue.

• Leveraged and interpreted large datasets in SQL to support ad-hoc requests and performed analysis using Python and R to uncover key insights about customer review data

• Manipulated data, performed EDA and developed a time series model on customer review history using Python

• Provided visualizations and recommendations on next steps to business clients and helped them increase average monthly revenue by 18%

Ogilvy Shanghai, China

Data Analytics Intern Jun 2018 - Sep 2018

• Improved marketing campaign performance by analyzing traffic data from 5M customers

• Created automatic dashboard to track CLV, click-through rate by different marketing channels in Tableau

• Developed statistical model (regression) to predict sales, clicks, impressions in Python and designed A/B test to evaluate new features and web layout on HTML campaign pages to improve conversion rate

• Optimized marketing strategies by building k-means clustering to create marketing persona and customer segmentation on new users, retention users and churned users

• Provided business insights and recommendations behind data and increase conversation rate by 30% for our clients PROJECTS EXPERIENCE

Instacart Shopper Behaviour Analysis Spring 2019

• Implemented solutions on inventory and recommendation systems by analyzing customer shopping history

• Designed key metrics like daily purchases volume, purchase frequency, number of reorders by-products

• Leveraged and implemented large data set in Hive, performed the ETL process, and analyzed data utilizing SQL

• Visualized data in Tableau to track customer reorder rate, average order value, and transaction growth rate

• Conducted experiments like A/B testing to evaluate new feature in the recommendation system

• Presented the descriptive statistics, methodologies, and findings to stakeholders and translated insights to non-tech people, resulting in a lift in average order value by estimated 26% SKILLS

• Technical Skills: SQL/NoSQL, Tableau, Python (Matplotlib, Pandas, Numpy, Scikit-Learn), R, SAS, Microsoft EXCEL

(Pivot Table, VLOOKUP), PowerBI

• Statistical Skills: Logistic Regression, Cross-Validation, K-Nearest Neighbors, Decision Tree, Random Forest, Neural Network, K-means Clustering, Dimensionality reduction



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