Job description

What You'll Do: - Participate in team planning sessions and educational presentations - Review the data to understand its overall scope and structure - Identifying the right metric to measure the success of the model and project - Work in teams to pre-process the data for analysis and model development - Uncover trends, insights, and key features to help guide where ML solutions can be applied - Develop models around the available data and suggested set of problems - For viable use cases develop proof of concept working models - Suggest and explore novel solutions to a problem and detailed recommendations for how to integrate the models in business - Communicating and presenting your teams work to AltaML and Public Sector Partners - Get familiarity with various data processing tools and exposure to AI/ML algorithms - Independently researching and connecting with your cohort to gain domain knowledge and learn state-of-the-art techniques - You will work with multiple tools and gain experience in multiple AI/ML and data processing fields. You will be given resources and mentorship to help with your growth and career advancement. What You Bring: - Minimum Requirements: - Enrolled in a Post secondary degree program at U of Alberta, U of Calgary (Masters Only), U Waterloo, Queens U, Mount Royal U, or U of Toronto for the duration of the internship term - General knowledge of and experience developing in Python and data science libraries/packages such as pandas, scikit-learn, matplotlib - Understanding of Machine Learning and software development concepts - Must be able to work 40 hours per week during the internship - Must have a valid citizenship and/or work permit that legally allows you to work full-time during the internship term - Nice to have: - Exposure to version control/Git - Experience with a cloud platform such as Azure, AWS or GCP - Ability to work independently and within a team environment - Well-developed communication skills, with the ability to engage in client meetings and deliver professional presentations

Requirements