Work Location:Montréal, Quebec, Canada
Hours:37.5
Line of Business:TD Wealth
Pay Details:135 000 $/$135,000 - 175 000 $/$175,000 CAD
This role is eligible for a discretionary variable compensation award that considers business and individual performance.
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 at TD. Our 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:Role OverviewThe Quantitative Research team at TD Asset Management is looking for an experienced, highly curious, and technically strong researcher to contribute to the development of systematic equity investment strategies.
The role is centered on alpha research and the development of systematic stock-selection signals. We are looking for someone who combines:
- The scientific discipline to formulate hypotheses, design rigorous experiments, and distinguish robust results from statistical noise.
- The practical mindset to turn research ideas into working solutions and improve them through evidence-based iteration.
- The investment judgment to connect model behavior with financial intuition, portfolio outcomes, implementation constraints, and risk.
Candidates may come from quantitative finance, machine learning, computer science, statistics, physics, engineering, or another highly quantitative discipline. Direct investment experience is valuable, but candidates from scientific or technology backgrounds will also be considered if they demonstrate a serious interest in financial markets and an ability to apply their modeling expertise to investment problems.
Our research uses techniques ranging from traditional statistical methods to modern machine learning, deep learning, natural language processing, and agentic AI systems. We are interested in these methods when they help identify differentiated signals, improve our understanding of markets, or make the research and investment process more effective.
We place particular value on research that explores less commoditized data, identifies less visible relationships within traditional datasets, combines structured and unstructured information, or approaches familiar investment problems from a differentiated modeling perspective.
Responsibilities- Lead the research, development, and improvement of systematic stock-return forecasting models used in the management of equity portfolios.
- Translate economic, market, or data-driven observations into testable research hypotheses and investable signals.
- Apply appropriate quantitative methods, including statistical learning, machine learning, time-series modeling, optimization, deep learning, natural language processing, and generative AI.
- Design rigorous empirical experiments using appropriate validation methods, point-in-time data, realistic portfolio constraints, transaction costs, and implementation assumptions.
- Identify and manage research risks such as overfitting, data leakage, multiple testing, survivorship bias, unstable relationships, regime dependence, and poor data quality.
- Evaluate models based on predictive performance, economic significance, stability, interpretability, scalability, risk exposures, implementation costs, and incremental portfolio contribution.
- Work with structured and unstructured data, including market data, company fundamentals, analyst information, corporate filings, earnings-call transcripts, news, and alternative datasets.
- Use AI-assisted and agentic coding tools to accelerate research, software development, testing, debugging, documentation, and analysis.
- Design or integrate agents into research, development, data-processing, and model-scoring pipelines, including tool calling, workflow orchestration, structured outputs, automated testing, evaluation, monitoring, and appropriate human review.
- Determine when agentic or generative-AI methods add meaningful value and when more transparent or deterministic approaches are preferable.
- Develop high-quality research prototypes and contribute to their transition into reliable, monitored, and maintainable production processes.
- Monitor the intended and unintended exposures of investment strategies and analyze the drivers of performance, risk, turnover, and changes in model behavior.
- Stay current with developments in financial research, machine learning, artificial intelligence, data science, and systematic investing.
- Identify and evaluate new data sources, with attention to their quality, provenance, historical coverage, biases, stability, scalability, and investment relevance.
- Ensure that research and models are appropriately documented, reproducible, tested, and reviewed in accordance with model-governance, regulatory, audit, privacy, and responsible-AI requirements.
- Collaborate closely with quantitative researchers, portfolio managers, data specialists, technology teams, and risk professionals.
- Contribute reusable code, research tools, datasets, documentation, and methodological knowledge to the broader team.
- Provide technical guidance and constructive review to other researchers while remaining directly involved in modeling, experimentation, and implementation.
Job RequirementsThe ideal candidate will have:
- A Ph.D. in a quantitative discipline such as quantitative finance, economics, computer science, machine learning, data science, statistics, mathematics, physics, or engineering, or an equivalent combination of advanced education, research, and relevant professional experience.
- At least five years of relevant experience in quantitative investment research, machine learning, data science, applied research, or a closely related technical field.
- Demonstrated experience developing machine-learning or statistical models in complex, noisy, and changing environments.
- Strong theoretical and practical knowledge of applied statistics, probability, linear algebra, optimization, experimental design, and machine learning.
- Experience with relevant methods such as supervised and unsupervised learning, regularization, feature engineering, time-series modeling, representation learning, deep learning, natural language processing, probabilistic modeling, or portfolio optimization.
- A detailed understanding of the data used for modeling and the ability to identify data-quality issues, leakage, biases, instability, and weaknesses in the research design.
- The ability to explain model assumptions, limitations, failure modes, validation choices, and observed differences between in-sample and out-of-sample performance.
- Strong proficiency in Python and its scientific and machine-learning ecosystem, along with experience using SQL and working with large or complex datasets.
- Practical experience with agentic coding and AI-assisted development, including the use of coding agents to explore ideas, develop and review code, test hypotheses, debug implementations, and improve research productivity.
- Experience integrating agents or AI components into research, development, data, or model-scoring pipelines. Experience with evaluation frameworks, automated testing, tool integration, monitoring, and human oversight is particularly relevant.
- Familiarity with sound software-engineering practices, including version control, testing, code review, modular design, reproducible environments, and collaborative development.
- For candidates from a finance background, a strong theoretical understanding of modern modeling techniques and evidence of remaining current with developments in machine learning and AI.
- For candidates from a scientific or technology background, demonstrated curiosity about financial markets and a thoughtful understanding of how modeling methods need to be adapted to financial data.
- Strong communication skills and the ability to explain technical research to both quantitative and non-quantitative stakeholders.
- Intellectual honesty, independent judgment, and the willingness to challenge or abandon an idea when the available evidence does not support it.
- The ability to lead research independently while collaborating effectively and sharing knowledge across the team.
- A CFA designation, progress toward the designation, or equivalent demonstrated investment knowledge is an asset, but is not required.
Research and Execution MindsetWe value candidates who:
- Develop practical solutions and refine them through iteration.
- Share early working versions to gather feedback, test assumptions, and accelerate learning.
- Use evidence and research outcomes to determine the direction of subsequent improvements.
- Balance timely execution with the level of quality, validation, and robustness required at each stage.
- Understand which elements must be reliable from the outset and which can be progressively improved.
- Stay current with developments in financial modeling, machine learning, AI, and agentic systems.
- Understand models and their underlying data in depth, including their assumptions, limitations, and potential failure modes.
- Use AI and agentic tools to strengthen the research and development process without replacing sound scientific or investment judgment.
- Prefer a useful and well-scoped initial delivery followed by disciplined improvement, rather than unnecessarily delaying learning in pursuit of completeness from the outset.
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 strive to make every interaction, product, and experience remarkably human and refreshingly simple for 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 foster deeper relationships, ensure disciplined execution, and build a simpler, faster banking experience. TD is deeply committed to being a leader in client experience, that is why we believe that all colleagues, no matter where they work, are client facing. Together, we are reimagining what banking can be for our clients, colleagues and communities.
Our Total Rewards PackageOur Total Rewards package reflects the investments we make in our colleagues to help them and their families achieve their financial, physical, and mental well-being goals. Total Rewards at TD includes a base salary, variable compensation, and several other key plans such as health and well-being benefits, savings and retirement programs, paid time off, banking benefits and discounts, career development, and reward and recognition programs.
Learn moreAdditional Information:We're delighted that you're considering building a career with TD. Through regular development conversations, training programs, and a competitive benefits plan, we're committed to providing the support our colleagues need to thrive both at work and at home.
Please be advised that this job opportunity is subject to provincial regulation for employment purposes. It is imperative to acknowledge that each province or territory within the jurisdiction of Canada may have its own set of regulations, requirements.
Colleague Development If you're interested in a specific career path or are looking to build certain skills, we want to help you succeed. You'll have regular career, development, and performance conversations with your manager, as well as access to an online learning platform and a variety of mentoring programs to help you unlock future opportunities.
If you're passionate about helping clients and building deep, lasting relationships, TD offers diverse career paths where you can grow your expertise and make a meaningful impact.
We're committed to your success and foster a respectful workplace where diverse perspectives are valued, everyone has fair opportunities to grow, and you can unlock your full potential to achieve your career goals. Here at TD, we hire and develop the best.
Training & OnboardingWe will provide training and onboarding sessions to ensure that you've got everything you need to succeed in your new role.
Interview Process We'll reach out to candidates of interest to schedule an interview. We do our best to communicate outcomes to all applicants by email or phone call.
Accommodation Your accessibility is important to us. Please let us know if you'd like accommodations (including accessible meeting rooms, captioning for virtual interviews, etc.) to help us remove barriers so that you can participate throughout the interview process.
We look forward to hearing from you!
Language Requirement (Quebec only):Maîtrise d'une langue autre que le français pour offrir du soutien ou traiter avec des employés ou des collègues qui ont besoin de services et de soutien dans une langue autre que le français.
