WHO WE ARE:

TD is one of the world’s leading global financial institutions and is the fifth largest bank in North America by branches/stores. Every day, we deliver legendary customer experiences to over 27 million households and businesses in Canada, the United States and around the world. More than 95,000 TD colleagues bring their skills, talent, and creativity to the Bank, those we serve, and the economies we support. We are guided by our vision to Be the Better Bank and our purpose to enrich the lives of our customers, communities and colleagues.
TD is deeply committed to being a leader in customer experience, that is why we believe that all colleagues, no matter where they work, are customer facing. As we build our business and deliver on our strategy, we are innovating to enhance the customer experience and build capabilities to shape the future of banking. Whether you’ve got years of banking experience or are just starting your career in financial services, we can help you realize your potential. Through regular leadership and development conversations to mentorship and training programs, we’re here to support you towards your goals. As an organization, we keep growing – and so will you.

ABOUT THE ROLE

We’re looking for a highly motivated Applied Machine Learning Scientist to join our AI2 team. In this role, you’ll drive the development and deployment of ML solutions that power data-driven decision-making across our Canadian Personal Banking division. You’ll work closely with various teams to bring AI capabilities to life and deliver measurable business impact.
This is a unique opportunity work on high-impact initiatives in a fast-growing function. If you’re passionate about solving real-world problems with machine learning and want to make a tangible difference at scale, we’d love to hear from you.

WHAT YOU’LL DO

• Develop, deploy, and maintain modern and scalable ML solutions for use cases such as pricing, forecasting, anomaly detection, and generative AI
• Translate business problems into analytical frameworks and collaborate with cross-functional teams to define success metrics and solution approaches
• Conduct rigorous model evaluation, A/B testing, and monitoring to ensure model performance, fairness, and stability in production
• Communicate complex technical results and recommendations to stakeholders with varying levels of technical expertise to drive change across TD
• Stay current with ML research and industry trends to bring innovative approaches into production