Job Objective:

Full time data scientist role working to develop machine learning models to predict simulated building energy use. This role will be to support the development of key features in an economic analysis software tool. A successful candidate will have experience in creating simulated data sets and machine learning. They will be detail oriented and comfortable creating an entire methodology to predict building energy use given a feature set of 50-80 performance values. An understanding of building science, energy modeling, and machine learning will be seen as assets.Job Responsibilities:

• Research and Identify best sampling methodology for machine learning models built to estimate simulated target variables.
• Write and maintain sample data creation scripts.
• Implementing machine learning models to predict building energy simulation in a method that can be handed off to the development team for implementation in code base.
• Improving model performance where possible and increasing the scale of coverage such as adding new building types, climate zones, and building codes.
• Create complete model error analysis framework.
• Optimize model sample size.
• Maintain and re-build machine learning models when feature requests are provided by users.
• Work on user facing visualizations of machine learning model error.
• Work on user facing visualizations of design space and model prediction.
• Provide related teams with progress updates.
• Work with development team to implement sample data creation and model development scripts into code base.

Competencies and Education:

• Bachelor degree in Mathematics or Statistics
• Masters degree in Data Science , Mathematics, Machine learning or equivalent
• Competency in Python
• Experience with PowerBI, highCharts or other graphics package
• Experience working in Agile development framework
• 2-5 years relevant work experience
• Strong attention to detail
• Strong analytical thinking skills
• Self starter, keen to take initiative- identifying and solving problems
• Ability to distil and communicate complex ideas to people of all skill levels
• Interest in building science, sustainability, and economics
• Experience in building energy simulation is seen as a strong asset

*Not a remote position, must be available to work in person in Toronto
Job Types: Full-time, Permanent
Pay: $80,000.00-$113,000.00 per year

Benefits:

• Dental care
• Extended health care
• Paid time off

Ability to commute/relocate:

• Toronto, ON M5T 1X5: reliably commute or plan to relocate before starting work (preferred)

Experience:

• relevant work: 5 years (preferred)

Work Location: In perso

• Research and Identify best sampling methodology for machine learning models built to estimate simulated target variables.
• Write and maintain sample data creation scripts.
• Implementing machine learning models to predict building energy simulation in a method that can be handed off to the development team for implementation in code base.
• Improving model performance where possible and increasing the scale of coverage such as adding new building types, climate zones, and building codes.
• Create complete model error analysis framework.
• Optimize model sample size.
• Maintain and re-build machine learning models when feature requests are provided by users.
• Work on user facing visualizations of machine learning model error.
• Work on user facing visualizations of design space and model prediction.
• Provide related teams with progress updates.
• Work with development team to implement sample data creation and model development scripts into code base