Ken Lau

Data Scientist | M.Sc. Statistics | UBC




About Me

I have 6+ years of professional experience in data science.

I completed a M.Sc. in Statistics at the University of British Columbia (UBC).

I like to play Ultimate Frisbee.

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Academics

Univeristy of British Columbia

Master's in Statistics:

September 2013 to August 2015

Bachelor's in Statistics with Co-op:

September 2008 to May 2013

Nash Medal Recipient

Publication

2016: International Journal of Electronics and Communications Volume 79, Issue 9
Authors: Ken Lau, Matias Salibian-Barrera, Lutz Lampe
Title: Modulation Recognition in the 868 MHz Band Using Classification Trees and Random Forests

Selected Coursework

Statistics & Mathematics
Course Description
STAT 545/547M Data Science Tools
STAT 560/561 Statistical Theory
STAT 550 Statistical Consulting
STAT 547C Proabability Theory
STAT 447C Statistical Machine Learning
STAT 443 Time Series
STAT 404 Design & Analysis of Experiments
STAT 306 Regression
STAT 344 Survey Sampling
MATH 316 Advanced Calculus
MATH 307 Applied Linear Algebra
MATH 303 Stochastic Processes
Computer Science
Course Description
CPSC 540 Machine Learning
CPSC 547 Information Visualization
CPSC 546 Numerical Optimization
CPSC 404 Advanced Relational Database
CPSC 320 Algorithms and Data Structures
CPSC 317 Computer Networking
CPSC 313 Hardware and Operating Systems
CPSC 310 Software Engineering
CPSC 302/303 Scientific Computing

Other coursework from MOOC:

Coursera:
  • Introduction to Data Science - University of Washington (with certificate)
  • Social Network Analysis - University of Michigan (with certificate)
  • Text Mining and Analysis - University of Illinois at Urbana-Champaign (with certificate)
  • Game Theory I - Stanford University (with certificate)
  • Game Theory II - Stanford University (with certificate)
  • A Developer's Guide to IoT - IBM (with certificate)
  • Udacity:
  • Intro to Hadoop and MapReduce
  • Data Wrangling with MongoDB

  • Work Experience


    Business Intelligence Engineer - Amazon
    (March 2022 - Present)

    reference: amazon.ca


    Data Scientist - Hothead Games
    (Oct 2017 - Nov 2021)

    • Use Python and R to create machine learning and statistical models for predictions and deep-dive analysis on player behavior improving user engagement and retention.
    • Use SQL and Tableau to build dashboards to better understand player behavior/progression/economy such as funnels to identify pinch points in the game.
    • Develop an A/B test reporting platform for all games, and provide guidance in decision making based on statistical confidence.
    • Help game and marketing teams plan the design of experiments to accurately measure feature and campaign performance.
    • Implement forecasting models using machine learning to determine the performance, improvement, and profitability of a game.
    • Develop a robust model to assess theme test experiments on semi-randomized data.
    • Significantly improve the data-driven culture of the company by effectively communicating analytical results across all departments in the company.
    • Work on data pipelining projects using tools in AWS and Jenkins to add and modify columns in the main tables used by the analytics team.


    Data Scientist - Alberta Gaming and Liquor Commission
    (Aug 2015 - Aug 2017)

    • Build predictive models using statistics and machine learning to determine the success rate of new games based on different factors such as denomination, game theme, platform, level of bet, etc.
    • Analyze and track the impact of various events in Alberta and AGLC including concerts, marketing campaigns, power outages, floor re-organization, etc.
    • Process data and build dashboards that monitor success rates for terminal add-ons at all sites across Alberta.
    • Deep dive analysis on progressive games (slots with jackpots), supply/demand of game play at the casino-level, and impact of different locations on casino floor.
    • Consistently collaborate and communicate with product specialists and managers to improve business processes.



    Research Assistant - UBC (Jan 2014 - Feb 2015)

    • Simulate transmission signals in Matlab.
    • Implement new feature variables for tree-based classifiers using R.
    • Improve predictive performance of existing methods.


    Data Analyst Intern - Environment Canada (Feb 2013 - Aug 2013)

    • Predict air quality in ozone, nitrate, and particle mass elements.
    • Implement and optimize boosting trees and support vector machine regressors.
    • Impute missing data using nearest neighbor and multiple imputation methods.

    Programming Skills


    Advanced Python, R, SQL, C#, Javascript
    Intermediate Matlab, Java, Hadoop, Spark, Git
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    • Contact Me
    • ken.lau171@gmail.com