Data Analyst entry level

Mohan Huang

**** * ******** *** #****

Phoenix, AZ, United States

805-***-****

**************@*****.***

EDUCATION

University of California, Santa Barbara Santa Barbara, CA, United States Bachelor of Science in Financial Mathematics and Statistics Graduation Time: June. 2016

! Major GPA: 3.2/4.0

LANGUAGE, SKILLS, & ACTIVITIES

Languages: Mandarin-native, English-fluent

IT Skills: SAS, R, Python and MS Excel & PowerPoint WORK Experience & Projects

PwC Shanghai

Data Analyst Intern July 2018-Now

! Familiared with data science skills and Python packages such as Pandas, Matplotlib and Scikit-learn by doing a case study regarding to US health.

! Used Linear Programming to assign weights of each person to different geo locations and also finish the sampling population code for generating synthetic population.

! Applied KNN and Random Forest to predict other health variables for synthetic population. Discover the pattern in the result and using data visualization to produce a report.

! Utilized Python packages to do credit card fraud detection by studying 28 different features.

! Cleaned data and used logistic regression, Linear regression and nonlinear regression to find patterns and choose regularization weight

! Compared L1 and L2 to find the best fitting pattern for credit card fraud and predict for future behavior. HB-TEA & Snowflake Goleta

Founder &CEO & General Manager Jan 2017 - April 2018

! Managed daily operation to reach sales goal.

! Use data analysis to calculate the operational cost and profit margin to maximize profit, predict raw material usage and employees’ working shift.

! Communicated with UCSB fundraising clubs and organizations such as CSA, Sigma and Pi to negotiate corporations.

! Planned advertising activities and campaign on different social medias.

! In charged of the hiring process, employees training. Case Study: Customer Segmentation and Marketing Campaign Analysis North Windsor Oct 2016 – Nov 2016

! Selected and extracted samples from a list of raw dataset, eliminated duplicates, created a one-subject- per-row dataset with PROC SQL.

! With given requirement, established segmentation for the behavior of customer based on their credit score.

! Defined new variables and assigned different designated values to them based on campaign matrix and derived product information for each customer.

! For all numeric variables, performed data quality check, focusing on minimum, maximum, and missing values. Did correction for errors and marked missing values.

! Introduced a variable as a last-update indicator in order to trace the time of insertion or updating for every samples

! Statistical Methods: Segmentation, Cluster Analysis, Discriminant Analysis. Case Study: Risk Management Analysis North Windsor Oct 2016 – Nov 2016

! Performed 3-month moving average analysis of RRs. For first appearance of every account, assigned missing value to all RRs.

! Based on calculated moving average of RRs, converted original multiple-row-per-account dataset into a one-row-per-account dataset. Only last record of every account was kept with last. automatic variable and IF statement.

! Kept the last month’s status of every account as an indicator to track whether a customer has charged off with IF statement.

! For charge-off customers, calculated the time period from the beginning of study to the time that they charged-off . For non-charge-off customers, assigned a constant for this variable.

! Based on logistic regression analysis, forecasted a list of customers with different delinquency history to see the probability that they would default in the future.

Contact this candidate