# VP, Quantitative Research - TD Asset Management at TD Bank

TD Bank is hiring a VP, Quantitative Research - TD Asset Management in Montreal, Canada. Level rates it Builds AI ●●●●; you can [apply on TD Bank](https://jobsbylevel.com/go/b77dfb33-cf49-4f3f-a017-dec94e271865?utm_medium=ai-agent).

AI Level 4, AI centrality 89 out of 100. Montréal, Québec.

## Details

- Company: [TD Bank](https://jobsbylevel.com/companies/td-bank)
- AI level: AI Level 4 (score 89 out of 100)
- Location: Montréal, Québec
- Posted: November 12, 2026
- Apply: https://jobsbylevel.com/go/b77dfb33-cf49-4f3f-a017-dec94e271865?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=vp-quantitative-research-td-asset-management-at-td-bank-9123d6

## Prepare for this job

The job page lists 3 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/vp-quantitative-research-td-asset-management-at-td-bank-9123d6

## Description

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 Overview The 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

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Source: https://jobsbylevel.com/jobs/vp-quantitative-research-td-asset-management-at-td-bank-9123d6

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Level. https://jobsbylevel.com/jobs/vp-quantitative-research-td-asset-management-at-td-bank-9123d6.

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