Julien St-Pierre Fortin, Developer in Montreal, QC, Canada
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Julien St-Pierre Fortin

Verified Expert  in Engineering

Machine Learning Developer

Montreal, QC, Canada
Toptal Member Since
November 13, 2019

Julien's expertise is deploying software solutions in data and AI. He is passionate about helping clients create innovative products and gain valuable insights from data. He genuinely cares about his clients and goes above and beyond to ensure highly successful projects.


Next.js, React, Terraform, AWS CLI, Git, OpenAI GPT-4 API, OpenAI GPT-3 API...
C#, MySQL, .NET, Object-oriented Programming (OOP), Snowflake...
Mino Games
SQL, Scikit-learn, Pandas, Python, Data Science, Git, NumPy, BigQuery




Preferred Environment

Slack, GitHub, Google Docs, Miro, Figma

The most amazing...

...product I've built is Succession HQ, a SaaS app to optimize operations for manufacturing SMBs.

Work Experience


2023 - PRESENT
  • Built retrieval-augmented generation algorithms to power the product's business insights engine.
  • Architected and built the data infrastructure, as well as the app's back end and front end collaborating with the engineering team.
  • Worked with the CEO to define business requirements and processes to reach our goals.
  • Collaborated with the design lead to build our design system.
Technologies: Next.js, React, Terraform, AWS CLI, Git, OpenAI GPT-4 API, OpenAI GPT-3 API, TypeScript, SQL

Software Engineer

2019 - PRESENT
  • Played a role in developing Coveo's latest generative question-answering capabilities.
  • Unified data modeling for the service and support line of business.
  • Improved data access security of search engine data.
Technologies: C#, MySQL, .NET, Object-oriented Programming (OOP), Snowflake, Data Build Tool (dbt), AWS CLI, Terraform, Java, Scala, PyTorch, OpenAI GPT-3 API, Git, OpenAI GPT-4 API, TypeScript, NumPy, TensorFlow, Test-driven Development (TDD), SQL

Data Scientist

2019 - 2019
Mino Games
  • Built machine learning models from cohort KPIs to get early feedback on marketing activity, such as the likelihood of acquired cohorts' profitability.
  • Developed revenue forecast models using curve-fitting methods.
  • Built dashboards to monitor user behavior with SQL, BigQuery, and Mode Analytics.
  • Developed a Python library to streamline the machine-learning process.
Technologies: SQL, Scikit-learn, Pandas, Python, Data Science, Git, NumPy, BigQuery

Data Scientist

2016 - 2019
  • Constructed machine learning models using scikit-learn and TensorFlow based on user behavior to predict cohorts' lifetime value (LTV) and profitability.
  • Co-developed many data science tools, a model factory, and APIs with Pandas and Flask.
  • Built an automated task scheduler using Apache Airflow.
  • Researched user dynamics spanning multiple products with Markov chains and recurrent neural networks.
  • Co-developed a Vue-based user interface to visualize model predictions.
Technologies: Docker, Jupyter Notebook, TensorFlow, Matplotlib, Scikit-learn, Pandas, Python, Data Science, Git, PostgreSQL, NumPy, Apache Airflow

Research Assistant

2014 - 2015
  • Developed deep learning models with MATLAB to predict water temperature from meteorological data to support engineering at the Beauharnois hydropower plant in Quebec.
  • Conducted research on the development of a network for measuring and collecting water temperature data from covariates. We used mutual information and dimensionality reduction methods using MATLAB.
  • Conducted web scraping of the Statistics Canada website to obtain meteorological data using Python.
Technologies: Deep Learning, MATLAB, Python, Data Science

Research Intern

2013 - 2013
  • Participated in a research internship at TRIUMF on the IRIS experiment with Dr. Rituparna Kanungo.
  • Improved detector calibration with elastic scattering simulations in C++. The goal of the experiment was to demonstrate the particular halo structure of the lithium 11 isotope.
  • Collaborated with an international team of physicists.
Technologies: C++, Data Science

Learning Generated Abstractions

When we program an AI to learn simple yet purely virtual representations, what happens in the middle of the process? I used Generative Adversarial Networks (GANs) to generate abstract images from samples obtained via simple procedures in Python.

Reliable Vision System for Wildlife

The goal of this project was to build an autonomous camera system that automatically detects and counts bird species based on input images. We used YOLOv3 and Inception ResNet v2 algorithms for bird detection and classification, respectively.

Installation with Ed Fornieles Studios

I built a Node.js back end to power a Unity art installation at Ca' Pesaro Venice art gallery during La Biennale di Venezia 2019. It was connected to real-time financial analytics, running in a Docker container on AWS infrastructure.
2015 - 2018

Master of Science Degree in Data Science and Operations Research

HEC Montréal - Montreal, Quebec, Canada

2011 - 2014

Bachelor of Science Degree in Theoretical Physics

Université Laval - Québec, Canada


Scikit-learn, NumPy, Pandas, TensorFlow, Matplotlib, PyTorch, React, Keras, Node.js


Git, AWS CLI, Terraform, MATLAB, BigQuery, Apache Airflow, Slack, GitHub, Google Docs, Miro, Figma


Python, C#, SQL, Snowflake, Java, TypeScript, C++, Scala


Data Science, Test-driven Development (TDD), Object-oriented Programming (OOP)


Jupyter Notebook, Docker


MySQL, PostgreSQL


.NET, Next.js, Unity


Deep Learning, Artificial Intelligence (AI), Machine Learning, Modeling, Physics, OpenAI GPT-3 API, Numerical Methods, Data Build Tool (dbt), OpenAI GPT-4 API, Generative Adversarial Networks (GANs)

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