Data Scientist - TD Asset Management

TD•Toronto, ON

9d•CA$105,000 - CA$145,000•Onsite

About The Position

TD Asset Management (TDAM), a member of TD Bank Financial Group, is a leading North American investment manager offering progressive investment solutions to both institutional and individual investors. For over two decades, the organization has established competitive market positions in active, quantitative, and passive portfolio management. As of December 31, 2025, TDAM and its affiliates manage over $508 billion in assets on behalf of pension, insurance, endowment/foundation, and corporate clients, as well as high-net worth clients and retail mutual funds. In attaining this client commitment, TDAM has built one of Canada’s largest and most respected investment management and research teams with more than 220 Portfolio Managers, Associate Portfolio Managers, Traders and Analysts. Versatile job accountabilities and skills will be needed, as the candidate will be working with a small team and to ensure efficiency various hats will be required.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, or a related field.
Extensive experience using Python including a strong grasp on machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
Strong background in machine learning, large language models (LLMs), agentic AI frameworks, and modern software engineering
Theoretical and practical knowledge of machine learning, NLP (natural language processing), deep learning an statistics

Nice To Haves

Strong software engineering skills: version control, CI/CD, testing, and code optimization
Experience with data storytelling using Tableau, QlikView, Mode, Matplotlib, D3, or similar data visualization tools

Responsibilities

Help to grow a team of data scientists and software engineers
Partner closely with portfolio managers, sales, technology, operations, senior management and other stakeholders within the organization to guide their decisions making process through construction on ML tools
Proactively identify and champion projects that solve complex problems across multiple domains
Apply specialized skills and fundamental data science methods (predictive modeling, Generative AI, time series forecast, deep learning, NLP) to develop solutions that improve business decisions
Design and implement end-to-end data pipelines: work closely with stakeholders to build models, tables or schemas that support business processes
Guide and mentor developers and data scientists, fostering skill growth and best practices in AI/ML and software engineering