Applied Machine Learning Scientist II

Work Location:

Toronto, Ontario, Canada

Hours:

37.5

Line of Business:

Analytics, Insights, & Artificial Intelligence Pay Details:$81,600 - $115,200 CADThe pay details posted reflect a temporary market premium specific to this role that is reassessed annually.

TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience  compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.

As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.

Job Description:

Department Overview Applied Machine Learning Science is a specialized field within advanced analytics that brings deep expertise in Artificial Intelligence (AI) / Machine Learning (ML) to drive innovation and improvements in business practices. This area is focused on developing and applying a broad set of evolving advanced AI/ML techniques to solve business problems. It includes gathering, analyzing, and modeling a wide range of data, applying mathematical models/techniques and tools, and utilizing software engineering best practices to develop and implement the solutions.

Key Responsibilities Provide technical expertise, develop, and implement state-of-the-art machine learning models relevant to products for personal banking: marketing, risk management, and strategy optimization.

Analyze large and complex—structured and unstructured—datasets (scaling to petabytes), deploy deep learning techniques including large language models to develop solutions.

Perform feature engineering and selection from raw data to enhance model accuracy and interpretability.

Conduct research, support development, prepare and deliver presentations and communicate progress, plan and risk related to projects to stakeholders.

Actively manage relationships within and across various business lines, corporate and/or control functions and ensure alignment with enterprise and/or regulatory requirements.

Write/create and maintain appropriate documentation.

Ensure respective programs/policies/practices are well managed, meets business needs, complies with internal and external requirements, and aligns with business priorities.

Lead / facilitate and/or implement actions / remediation plans to address performance / risk /governance issues.

Keep abreast of emerging issues, trends, and evolving regulatory requirements and assess potential impacts.

Maintain a culture of risk management and control, supported by effective processes in alignment with risk appetite.

Act as a brand ambassador for your business area/function and the bank, both internally and/or externally

Job Requirements Advanced university degree (Master's and Ph.D.) in STEM—statistics, computer science, mathematics, physics, engineering etc.)5+ years of relevant experience.

Developed and deployed machine learning models for marketing use cases such as customer propensity, next best action, churn prediction, customer lifetime value, and audience targeting, with demonstrated business impact.

Exposure to financial services, banking, retail, telecommunications, or digital marketing ecosystems, including customer segmentation, campaign management, and personalization platforms.

Proven experience in development and deployment of machine learning models in production.

Solid understanding of machine learning, deep learning, large language models, and traditional statistical methodologies.

Hands-on coding experience with Python, PySpark, PyTorch and SQL are required including cloud platforms, and MLOps practices to build, monitor, and product ionize machine learning solutions.

Strong problem-solving skills and good writing skills as well as ability to communicate complex concepts to non-technical audiences.

Experience with advanced AI techniques such as generative AI, large language models (LLMs), recommendation systems, graph analytics, or causal inference applied to marketing and customer experience problems are highly desirable.

Who We Are:

TD is one of the world's leading global financial institutions and is the fifth largest bank in North America by…