Charles Demontigny, Data Science Developer in Montreal, QC, Canada
Charles Demontigny

Data Science Developer in Montreal, QC, Canada

Member since February 11, 2022
Charles is a senior data scientist with 5+ years of experience in Python programming, SQL, predictive analytics, and data-driven marketing on the Google Cloud Platform. He's been working with a wide variety of clients, from startups to Fortune 500 companies. Charles has a good understanding of the business aspects behind the technical work, and he can deliver through the entire data pipeline process while analyzing large datasets using data science techniques and dashboards.
Charles is now available for hire




Montreal, QC, Canada



Preferred Environment

Python, SQL, Pandas, Plotly, Dash, Google BigQuery, Scikit-learn, StatsModels, Google Cloud, Python API, Google Cloud Platform (GCP)

The most amazing...

...project I've worked on included helping a startup grow its user base with ML, predicting which users are most likely to convert to premium, and targeting them.


  • Senior Data Scientist

    2019 - PRESENT
    • Developed a propensity scoring system using ML for an application for runners. The goal was to predict which free user bases where more likely to convert and when—to help them target email marketing communications.
    • Created a system that reads grocery invoices and detects the products and the banner at which the purchase was made for a couponing company using the Google Cloud AI's Vision and Natural Language APIs with the Python SDK.
    • Performed CLV predictions and segmentation using CRM data at a US-based hotel chain resulting in the retention of high-value guests using Python and Scikit-learn.
    • Built a machine learning-based system predicting the real estate value of more than two million properties in Canada with XGBoost.
    Technologies: Python, SQL, Machine Learning, Data Analytics, Data Science, Google Cloud Platform (GCP)
  • Data Scientist

    2017 - 2019
    JLR Solutions Foncières
    • Developed, integrated, improved, and maintained a machine learning model able to estimate the market value of the houses in Canada with LightGBM in Python and SQL for the ETL process.
    • Built a housing price index based on the three-stage least-square regression methodology by Case and Shiller (1987) using Python and SQL Oracle.
    • Wrote reports on the state of the real estate market. Produced econometric analyzes based on real estate microdata. Communicated the analysis produced from the data and was interviewed on radio and newspapers about these studies.
    Technologies: Python, R, Machine Learning, Data Analytics, Data Science, Google Cloud Platform (GCP)


  • Predicting Customer Lifetime Value

    In this project, transactional data is transformed into a single view per customer. Average order value, purchase frequency, and recency are then used to feed a probabilistic model to produce a prediction of future customer lifetime value.

    The marketing department uses this to produce targeted campaigns on high-value customers, those at risk of churn, or high potential. Finally, it is possible to push lists of our best customers to Google Ads and Facebook Ads to acquire customers that look like our best.

    In this particular case, open data is used. However, I developed this type of project with several clients, but I am not allowed to share it for obvious data privacy reasons.


  • Languages

    Python, SQL
  • Libraries/APIs

    Pandas, Scikit-learn, Python API
  • Paradigms

    Data Science
  • Other

    Econometrics, Machine Learning, Data Analytics, Dash, Google BigQuery, Structuring Machine Learning Projects, Customer Data, User Analysis, Deep Learning
  • Tools

  • Platforms

    Google Cloud Platform (GCP)


  • Master's Degree in Econometrics
    2015 - 2017
    ESG-UQAM - Montreal
  • Bachelor's Degree in Economics
    2012 - 2015
    ESG-UQAM - Montreal, Canada


  • DeepLearning.AI – Deep Learning Specialization

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