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Job description:

• 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. 

• 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