Simon Ho
Data Scientist
Technical Skills
Data analysis
Data cleaning
Data visualization
Data pipelines
A/B testing
Experimental design
Hypothesis testing
Metric design and evaluation
Machine learning
Supervised learning
Unsupervised learning
Computer vision
Apache Kafka
Databases (SQL)

Data Scientist with expertise in data analysis, statistics, and machine learning. Over 8 years of experience in planning and leading data-driven research projects, from developing data collection and analytics pipelines, to clearly communicating insights to both technical and non-technical audiences.

User Researcher
Sprung Studios2021 - Present
  • External research partner for AAA PC, console, and mobile games.
  • Programmed internal tools (Python, C#) to collect, analyze, and visualize datasets.
  • Researched and developed new data-driven analytics services.
  • Led client meetings to scope project requirements, develop research plans, and present reports.
Research Scientist (visiting)
Microsoft2019 - 2020
  • Designed and implemented algorithms for extracting novel spatial metrics from HoloLens 2 sensors.
  • Constructed data pipeline to collect, clean, and analyze hardware sensor data.
  • Performed exploratory analysis to research the usability of new metrics in different production contexts.
  • Provided insights to the data science team regarding the value, viability, and potential pitfalls of implementing the spatial metrics in production.
Research Scientist
Attentional Neuroscience Lab2013 - 2020
  • Applied theoretical statistical knowledge to the analysis of datasets for over 16 published experiments.
  • Constructed multiple data pipelines, from data collection, transformation/aggregation, to data analysis.
  • Developed algorithms to extract model features from raw multidimensional sensor data.
  • Created Python dashboards to aid cleaning of structured data. Reduced data cleaning time by 90%.
  • Programmed open-source data collection platform to collect experiment data across 9 projects, resulting in 50% faster project completion. Link: GitHub/cognitive-battery
  • Created open-source Python package to analyze time-series data. Link: GitHub/sensormotion
  • Published results in 7 peer-reviewed journals and gave presentations at 11 research conferences.
Data Scientist
UBC Centre for Teaching, Learning and Technology2016 - 2019
  • Employed data analysis and experiments to help instructors evaluate new teaching methods.
  • Analyzed A/B test involving 6000 online students to identify factors that affected engagement.
  • Conducted experiments to determine whether the benefits of a novel visualization technique outweighed the cost. Results saved over $2000 in future production cost.
  • Used statistical methods to streamline a multi-institution survey. Reduced total survey length by 28%.
Technical Projects

Webcam eye tracker

  • Developed a webcam-based eye tracker to predict real-time eye gaze location.
  • Programmed data collection pipeline to collect and transform 50k webcam images.
  • Built deep learning computer vision architecture, using PyTorch, to model webcam images.
  • Achieved a final eye tracking error of 1cm, rivalling more expensive infrared-based approaches.

Smartphone walking kinematics

  • Published novel method to measure outdoor walking kinematics using smartphone sensors.
  • Technical lead on cross-functional, collaborative research project with the University of Bristol.
  • Designed algorithms to compute walking metrics from 60 million data points.
  • Method was faster and cheaper than traditional techniques, saving over $7000 in equipment cost.

Game character planner

  • Developed web character planner, used by 250,000 players, for a mobile game using JavaScript/React.
  • Incorporated insights from usability testing into the design of 3 new site features.
University of British Columbia2015 - 2020

Cognitive Science, minor in Quantitative Methods

University of British Columbia2013 - 2015

Cognitive Science

BA (Honours)
University of British Columbia2009 - 2013


Graduate Statistics Courses
  • Research methods
  • Exploratory data analysis
  • Multiple linear regression
  • Analysis of variance
  • Multivariate statistics
  • Measurement theory and survey design
  • Latent factor analysis
  • Structural equation modelling
  • Item response theory
Neural Networks and Deep Learning
Coursera (
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera (
Structuring Machine Learning Projects
Coursera (