Data Engineering

Sichao Yin

**** ****** ****** ********, ** ***** 213-***-**** *********@*****.***

SUMMARY

A talented and self-motivated data analyst who has 6 years of experience in computer science, engineering and statistics with strong analytical, project management and communication skills

A hard worker and life-long learner who really wants to learn and challenge and who is particularly interested in positions related to data analysis and data science TECHNIQUES AND TOOLS

Programming: excellent at R, Python, SQL, Matlab and Tableau; have used Java and C

Applications: MS Office(Excel VBA), Six Sigma, MS Project, Auto CAD, Solid Works, SAP Certified – Associate Business Foundation & Integration with SAP ERP 6.0 EHP5 (Certificate ID: 000*******)

Machine Learning and Data Mining: Linear regression, Logistic Regression, Decision Trees, Random Forests, Bagging, Boosting, Support Vector Machines (SVM), Neural Networks, Principal Component Analysis, Factor Analysis, K-Nearest Neighbors (KNN) and K-means

EDUCATION

University of Southern California (USC) Los Angeles, CA Master of Science, Industrial and Systems Engineering May 2013

Machine Learning, Optimization: Theory and Algorithms, Modern Enterprise Systems, Financial Engineering, Quality Management for Engineers, Advanced Production Planning and Scheduling University of Shanghai for Science and Technology (USST) Shanghai, China Bachelor of Science, Computer Science & Material Forming and Control Engineering (double major) July 2011

Introduction and Relational Databases, Data Structures and Algorithms, Operating Systems, Software Engineering EXPERIENCE

Data Scientist Teleflora Los Angeles, CA

March 2017 - Present

Acquire and analyze data by building statistical model at scale to provide insights and give data-driven suggestions for business

Develop learning algorithms, design pipelines to build machine learning applications by doing end-to-end analysis and with domain knowledge

Kaggle Competition Acquire Valued Shoppers Challenge Los Angeles, CA November 2016-January 2017

Predicted repeat customers among targeted customers offered incentives based on their pre-offer shopping histories

Applied feature engineering to the large raw data (350 million rows and over 300,000 shoppers) to filter, select, and create predictive features using Python

Applied resampling method on the unbalanced data and Fitted model using: Linear Model, Generalized Linear Models with Elastic Net Regularization (GLMNet), Ridge Regression, Classification And Regression Tree

(CART), Random Forest, Boosting, Generalized Boosted Models, Principal Component Regression, Support Vector Regression and ensemble learning

Used 10-fold cross-validation to test model predictability evaluated by precision and accuracy

Identified 10 most important features in deciding the probability of a customer making repeat purchases Data Analyst CSKH Technologies Company Limited Beijing, China September 2014-October 2015

Provided variance analysis of monthly data from multiple sources to develop trend analysis reporting

Made recommendations to the team for budget optimization while assisting in documenting and producing monthly performance report

Manipulate the database (MySQL) and create dashboards to visualize the data and findings Team Leader Rational Option Pricing USC

December 2012-May 2013

Simulated the Black-Scholes model using Black-Scholes equation, European-style options price calculation and blue chip stocks

Evaluated Binomial Options Pricing Model in simulating the arbitrage-free pricing of the options by a tree graph with ten tree steps and compared performance with Black-Scholes model Team leader Analysis of the Experiment of Single-walled Carbon Nanotube Synthesis USST December 2010-May 2011

Developed the database management system for the experiment of single-walled carbon nanotube synthesis with basic functions to calculate, compare and save results of mass values, height values and mean density

Provided an easy data analysis system with statistics and visualization of the experiment results

Analyzed the results and applied the system in further experiment in single-walled carbon nanotube synthesis

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