Eric Guan

Welcome to my website. I use this website to store a detailed version of my resume and keep track of the projects that I have accomplished.

Personal Information

I'm currently working for Canadian Imperial Bank of Commerce (CIBC)'s Marketing Analytics group, mainly responsible for performing analysis for direct marketing campaigns. In the past, I have worked for CIBC's Global Operational Risk Management group, responsible for the bank's operational risk capital modelling process using the Advanced Measurement Approach (AMA) model. I have also worked in the insurance industry (both life and property & casualty) as actuarial analyst during my co-op terms, where I mainly worked on model validation and catastrophe modelling projects. I passed all the preliminary exams from the Society of Actuaries (SOA) and the Enterprise Risk Management (ERM) Module.

I did my Master of Science degree in Statistical Sciences from the University of Toronto (2017-2019), and my Bachelor of Mathematics degree (co-op program) from the University of Waterloo (2011-2016), major in Actuarial Science (Finance option) and Statistics, minor in Applied Mathematics. Over the years, I have obtained solid background and work experience in applying Statistical and Machine Learning methods in quantitative analysis, especially under the topic of Quantitative Risk Management.

WORK EXPERIENCE

Manager, Marketing Analytics, Marketing Solution and Client Offers, Canadian Imperial Bank of Commerce (CIBC), Toronto, ON, May 2019 – Now

• Build customer segmentation models by leveraging machine learning techniques using Python, some examples include analyze employee mortgage needs and engage employee mortgage offers
• Perform deep dive analysis for CIBC’s mortgage business using time series and correlation models, some examples include market analysis and channel/segment analysis
• Improve the quarterly campaign ranking analysis using regression and hypothesis testing methods
• Assist in monthly reporting tasks by analyzing complex data from Oracle databases using SQL queries and SAS models

Senior Analyst, Risk Capital Modelling Group, Global Operational Risk Management, Canadian Imperial Bank of Commerce (CIBC), Toronto, ON, Jan 2017~Apr 2019

• Led the Advanced Measurement Approach (AMA) capital modelling group, responsible for calculating and reporting CIBC's operational risk capital numbers on both monthly and quarterly basis
• Conduct model performance reviews by preparing quarterly backtesting reports and annual review report
• Perform stress testing and correlation/sensitivity analysis required by OSFI, model validation, and audit
• Develop various loss models for different purposes, including single loss models, copula-free models, and country risk models

Actuarial Analyst, Sun Life Financial, Model Validation & Analytics, Toronto, ON, Sept 2015~Dec 2015

• Validated the Segregated Fund PathWise Conversion model by replicating product features and analyzing selected sample policies
• Validated the Indonesia Interest Study model by reviewing GGY AXIS data and regenerating sample reinvestment and sensitivity testing
• Assisted in developing new model risk guidelines by applying new frequency-severity criteria and designing automation algorithms

Actuarial Analyst, Munich Reinsurance Company of Canada, Property & Casualty, Toronto, ON, Jan 2015~Apr 2015

• Analyzed and calculated data for each reinsurance treaty from company's underwriting platform on a retrocession basis
• Created Pareto-Poisson Models based on aggregate data for natural-catastrophe scenarios
• Prepared Probable Maximum Loss curves for each reinsurance treaty based on various perils and lines of business

EDUCATION

Master of Science, Statistical Sciences, University of Toronto, Toronto, Canada (2017~2019)

Bachelor of Mathematics, Actuarial Science, Statistics, and Applied Mathematics Co-op, University of Waterloo, Waterloo, Canada (2011~2016)

Society of Actuaries (SOA) exams and modules

• Exam P Probability
• Exam FM Financial Mathematics
• Exam MFE Models for Financial Economics
• Exam C Construction and Evaluation of Actuarial Models
• Exam MLC Models for Life Contingencies
• Enterprise Risk Management (ERM) Module

Programming Skills

• Python (Machine learning, Data manipulation)
• R (Statistical modelling, Data visualization)
• Matlab (Simulation, Loss modelling)
• C++
• SAS
• SQL (Queries for Oracle database)
• Tableau (Interactive data visualization)
• LaTeX (Better than Microsoft Word)
• HTML (Can be seen from this website)
• Excel & VBA (Pivot table)

Past projects

I have done a plentiful number of projects involving statistics and machine learning (mainly during my graduate study at U of T). The following is a list of projects that I have done in the past that are related to various aspects of statistics/machine learning. Most of the projects follow the format:

1. Overview of the mathematical definitions and properties of the models/methods
2. Understand the abstract concepts through intuitive ideas (if possible) and complex examples
3. Apply the models/methods to either a simulated dataset or a real-world dataset

Quantitative Risk Management

Mathematics

Statistics

Machine Learning & Computational Statistics

Deep Learning

Reinforcement Learning

Graduate Courses at the University of Toronto

• Supervised Project-Insurance Risk Models
• Extreme value theory
• Methods of Applied Statistics I,II
• Directed Reading in Biostatistics
• Functional Data Analysis
• Statistical Methods for Machine Learning
• Monte Carlo Methods
• Computational Inference and Graphical Models
• Causal Inference
• Statistical Learning Theory
• The Measurement of Statistical Evidence

Undergraduate Courses at the University of Waterloo

Math Courses

• Real Analysis
• Complex Analysis
• Vector Calculus and Fourier Series
• Ordinary Differential Equations
• Partial Differential Equations
• Calculus of Variations
• Chaos Theory

Actuarial Science Courses

• Mathematics of Financial Markets
• Corporate Finance
• Quantitative Risk Management
• Loss Models
• Analysis of Survival Data

Statistics Courses

• Generalized Linear Models
• Stochastic Processes
• Computer Simulation of Complex Systems
• Data Visualization
• Time Series Analysis
• Mathematical Statistics
• Sampling Theory and Practice

Coursera - Deep Learning Specialization

• Course 1 - Neural Networks and Deep Learning
• Course 2 - Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
• Course 3 - Structuring Machine Learning Projects
• Course 4 - Convolutional Neural Networks
• Course 5 - Sequence Models

Last updated on Oct 27, 2019