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François Leblanc

Data Scientist at Treasury Board of Canada Secretariat

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About Me

Experienced Data Scientist with a demonstrated history of solving problems in the Canadian Government. Skilled in Python and its data and science stacks, GIS, Unix, Computer Vision, Machine Learning and Software Development.

Experience

Treasury Board of Canada Secretariat

Data Scientist

Environment and Climate Change Canada

Data Scientist

Working in ECCC's Data Lab, in a multi-disciplinary team tasked with developing data solutions to key problems throughout the department.

  • Rapid development of data solutions by use of data science techniques, statistics, machine learning, data warehousing, and visualization (Python, SQL, PowerBI, Tableau)
  • Using deep learning to automate bird counting and positioning from aerial imagery (Python, Keras, Azure)
  • Using text analytics techniques for public consultations (Python, gensim, spacy) streamlining the analysis process of program analysts to effectively understand what is being said through thousands of comments
  • Automating the rider sign-in for the departmental shuttles, using onboard tablets equipped with employee card readers (PHP, PowerBI, Azure)
Previous Positions: Physical Scientist, HR Data Analyst

Canadian Space Agency

Mission Planner - Data Acquisition

  • Provided Synthetic Aperture Radar (SAR) satellite imagery to Government of Canada clients, university research teams and the "Space & Major Disasters" International Charter
  • Oversaw and co-wrote a fully-tested and documented Python package to automate RADARSAT-2 image acquisition plan tasks
  • Planned the acquisition programs of RADARSAT-2 imagery for research, national security and humanitarian aid operations

Education

University of Ottawa

2007 - 2012

Bachelor of Science (B.Sc.)

Field Of Study: Geology, Physics. Specialization in Geophysics and GIS.
Senior-level courses include:

UC Irvine, Extension

June 2015 - Present

Data Science Certificate

Projects

Bayesian Inference Study Group

Put together this little website to serve as a backdrop for a series of study group sessions organized internally for public servants across the Canadian Government. We are currently follwing John Kruschke's Doing Bayesian Data Analysis textbook, 2nd edition.

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jupyter-bayes

Docker container for Jupyter notebook server, fully loaded Bayesian inference data science environment in R, Python and Julia. Great for use in Jupyterhub.

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autocrop.io

Open source project, currently working on API server and self-hosted service. Perfect for profile picture processing for your website or batch work for ID cards, autocrop will output images centered around the biggest face detected.

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Skills

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