Profile

Strengths include data-driven and analytical problem solving, quick learner, professional writing capabilities, excellent time management and prioritization, effective communication skills, self-motivated, and a team player.

Technical Proficiencies:

  • Fluent in Python & R
  • Database management with SQL
  • Visualizations in Tableau and PowerBI
  • Machine Learning & modelling using both supervised and unsupervised techniques.
    • Specific experience coding and evaluating random forests, kNN, ensembles, SVM, linear regression, GLM, k-means, k-medians, NLP, Naive Bayes, meta-ensembles (model stacking), boosting (XGBoost), neural networks, CNNs, logistic regression, dimensionality reduction with PCA, linear discriminant analysis, and DBSCAN.
  • Proficient with Keras and Tensorflow
  • Familiar with Snowflake, Databricks, AWS, Docker
  • Version control with Git and GitHub
  • Graphing in R with ggplot and shiny, in Python with matplotlib and seaborn

Business Proficiencies:

  • Report creation and presentation of conclusions to an audience or team (technical or non-technical audiences)
  • Experienced in Microsoft Office, Adobe Illustrator & Photoshop, CRM tools
  • Experienced in identifying and creating cost-effective strategies to improve online communication with customers
  • Ability to collaborate with internal and external stakeholders to streamline communication and ease job completion

Work Experience

TransLink | November 2019 - Present | Analytics Professional in Training

As a member of the Research and Analytics department, I worked on projects that assist stakeholders in various departments throughout the company, including planning, marketing, finance, and business technology services.

  • Created a machine learning model in Python that used DBSCAN, k-means, and LDA to segment over 2 million yearly Compass Card customers into 6 classes based on their weekly usage.
    • Monitored and reported the stability of the classes, retention of customers, make-up of the classes by fare type, movement of customers over time, and growth/loss of the customer base by class.
    • Became a source of ridership information and reporting throughout the COVID-19 pandemic using data from the segmentation model, along with other Compass Tap data to monitor and report the changes in ridership during COVID-19 by class of customer, time of day, fare payment method, and city of trip origin. This data has been reported to various stakeholders in planning, marketing, and the customer experience committee.
  • Developed a PowerBI dashboard, SQL, and Python processes to automate COVID-19 related weekly and monthly overall ridership reports for various internal and external stakeholders, including Finance, Planning, CUTA, and UITP.
  • Worked in a cross-functional team with Business Technology Services to test and review cloud-based data storage platforms, Snowflake and Azure, and their integration with Databricks and Tableau.
  • Developed linear regression models to predict daily and weekly transit ridership using external data sources, with a mean average weekly accuracy of 99.3%. This data assists finance and planning with decision making while internal data reporting systems are offline.


Silvercreek Realty Group | 2015 - 2018 | Marketing Associate

Member of the Business Development Team that grew the company into a top-100 brokerage in the US (based on gross sales), increased the number of real estate agents at the company by over 60%, from 800 to 1300 agents, and obtained yearly production growth of over 20%.

  • Responsible for tracking internal recruiting and retention statistics, identifying areas for improvement and presenting those findings to the BusDev and management teams.
  • Tracked and reported monthly, quarterly, and yearly brokerage sales statistics.
  • Mobile & electronic communications manager between the brokerage and the 1300 real estate agents.
  • Designed, automated, and tracked performance metrics of internal and external marketing campaigns developed for either recruiting or retaining real estate agents.
  • Assisted in website design and development for the internal and external brokerage websites.
  • Core team member for the development of a 14-week agent training program and managed ongoing distribution of the program.


Education

University of British Columbia | Master’s in Data Science | 2019

Skills Acquired:

  • Hypothesis development and testing using statistical analysis.
  • Supervised & unsupervised machine learning for both regression and classification.
  • Model performance estimation for regression: RMSE, MAE, R2; and classification: accuracy, precision, recall, AUROC
  • Coursework using AWS, Docker, Tensorflow, Keras
  • Version control with Git and GitHub
  • Data Wrangling with SQL, Excel, APIs, web scraping (HTML & JSON)
  • Data visualization in R with ggplot2 and Shiny, in Python with Matplotlib and Seaborn

Capstone Project:

  • Partner company: Tetrad
  • Team size: 4
  • Project scope: Predict the success of a potential new franchise location for a restaurant chain.
  • Language used: R
  • Project description:
    • Created a predictive analytics tool that leverages demographic, business, and traffic data in geographic zones to predict the success of opening a franchise location at a given location by predicting its potential profitability. This supervised learning model that can be used across multiple industries by any established B2C chain/franchise company.
    • Feature selection: the algorithm scans through thousands of data variables and uses greedy selection to choose variables most predictive of success/failure for the company.
    • Modelling: Meta-ensembling (model stacking) used to reduce error (+/- 15% of true value), prevent overfitting, and alleviate single-algorithm biases. Algorithm selects between linear regression and/or random forests regressor (based on lowest RMSE) for use in the final stacked model.
    • Report generation: Produces automated report that estimates the quality of the predictions using statistical error measurements (RMSE, MAE, classification accuracy) and informs about the variables selected by the algorithm.
  • Process:
    • Extensive exploratory data analysis which included using graphing for feature visualization, correlation matrix, and linear/non-linear base models – in both R and Python.
    • Explored linear and non-linear machine learning techniques including linear/logistic regression, random forests, SVM, boosted decision trees, and kNN – in both R and Python. Evaluated regression models using RMSE, R2, and MAE, evaluated classification models using accuracy, recall, and precision.
    • Explored feature selection techniques based on Pearson correlation, regression weights, regularization, and greedy selection to pick approximately 150 strongest features from 2700 while maintaining interpretability.
    • Created custom greedy selection algorithm that selects variables for a stacked (two-level) model without violating the validation set in the selection process.
  • My role: Primary developer of our variable selection, stacked model, and report generator scripts.
  • Note: Certain details of this project are protected by an NDA, but I am happy to answer questions about our project approach, steps taken in problem solving, and methods/algorithms used.


University of Idaho | B.S. Marketing | B.S. Human Resources Management | Minor in Advertising | 2015

Honours:

  • Summa Cum Laude
  • Dean’s List 2011-2015
  • Recipient of the 2013 Alumni Award for Excellence

Curriculum:

  • Coursework in marketing management, pricing, production, human resources hiring, interpersonal management, and teamwork.
  • Integrated business curriculum included additional coursework in finance, accounting, project management, and information systems.
  • Coursework in advertising storyboards, copywriting, journalism, and multi-platform campaign management.

Activities & Honours

Professional Track & Field Athlete | 2015 - Present
  • 2017 Team Canada Athlete | 3,000m Steeplechase | 2017 IAAF World Championships - London, UK
  • 2016 Olympic Trials Qualifier & Olympic Time Standard Qualifier | 3,000m Steeplechase
  • Skills Developed:
    • Superior time management
    • Effective goal setting (short and long term), planning, and budgeting
    • Ability to work as a team or as an individual to reach a goal
    • Ability to perform under stress


OTFC Website Manager (Volunteer) | 2017 - 2018
  • Responsible for the website upkeep of Oceanside Track and Field Club (Youth club based out of Parksville, BC)


NCAA Division I Cross Country and Track & Field | University of Idaho | 2010 - 2015
  • 2014 and 2012 USTFCCCA Division I All- Academic Honoree
  • Seven-time All-Conference Academic Honoree
  • Four-time NCAA Preliminary Rounds Track & Field Championships Qualifier and 2015 Semi-Finals Qualifier
  • Nine-time First Team All-Conference and Four-time Second Team All-Conference


Delta Zeta National Sorority | Active Member: 2011 - 2014 | Alumna: 2014 - Present
  • Founding Mother of the Pi Kappa Chapter at University of Idaho
  • Key committee member responsible for revision of chapter bylaws in 2014