The AI Accelerator team is on a continuous journey towards helping TELUS become a world-class leader in data solutions, doing so by delivering data analytics capabilities built upon unified scalable platforms, advanced AI solutions, high-quality data, and a data-product-oriented culture while always keeping an eye on the horizon, preparing for the next big thing. We are entrepreneurial and live by our AI Manifesto of failing fast and being outcome vs technology-driven, creating value for our customers, team members, communities, and the environment. The team takes pride in our Artificial Intelligence and Machine Learning capabilities and takes ownership of each step of the process. From hypothesis generation, initial exploring of datasets, developing novel AI techniques to discover insights, to developing automation pipelines and web visualizations, we do it all!
Always wanted to work with a team of innovators touching all business units within TELUS, and be part of a culture that embraces creativity and collaboration? If so, we’d love to talk with you!
You’ll be a part of the team and journey that will transform the way we do business across various domains. You’ll collaborate with teams across the company, seeking out various data sources to help identify new business opportunities while championing data-driven decision-making and the accelerated adoption of AI. As a Machine Learning Specialist on the team, you will combine your expert knowledge of data science with your strong ML Ops and software development skills to automate and facilitate data exploration, analytics, machine learning model development, training and deployment and will leverage your experience in building reusable algorithms, functions and libraries.
What you bring Minimum 7- 10 years of hands-on experience in machine learning, AI, and data analysis including deployment of solutions in business workflows. Strong expertise in Python and experience with data science libraries (e.g., Scikit-learn, Pandas, Numpy). Solid background in machine learning algorithms, including regression, classification, clustering, time series analysis, Reinforcement learning and optimization. Experience building and deploying GenAI applications and workflows. Proficiency in SQL and distributed computing. Hands-on experience with cloud platforms such as GCP, AWS, or Azure. Expertise in machine learning frameworks such as TensorFlow, Pytorch, and Keras. Strong understanding of software and AI development lifecycles, with experience in DevOps and MLOps practices Understanding of version control systems (e.g., Git) for collaborative development. Ability to communicate complex technical concepts to non-technical audiences effectively. A fast-moving, agile approach to removing roadblocks and delivering results quickly
Great-to-haves