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Added 35 mins ago
M
Senior Data Analyst I, Product
Mistplay
Toronto, ON
- Full-time
The Senior Data Analyst I, Product within Mistplay's Data department will play a crucial role in enhancing Mistplay's business performance through insightful analysis and driving analytics…
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- Internship
Reporting to the Director, Investment Management, the Junior Analyst, Investment Management is responsible for a variety of cross-functional responsibilities including assisting with the…
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- Full-time
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Modelling / Forecasting Senior Analyst (Quants)
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- Full-time
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Head of National Accounts & Institutional Sales, Vanguard Canada
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Compliance Business Oversight Analyst II (5535)
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Participate in personal performance management and development activities, including cross training within own team
Added 8 hours ago
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Senior Financial Analyst/ ERP Support Analyst (1 Year Contract)
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The City of Markham is seeking a Senior Financial Analyst/ERP Support Analyst within the Financial Planning Division of the Finance Department. Reporting to the Senior Manager, this dual…

Modelling / Forecasting Senior Analyst (Quants)

Toronto, ON, Canada

Full-time

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Job details

Job type: Full-time
Apply by: Oct 15, 2026
Experience: 2+ years relevant experience.
Posting language: English
Posted: Oct 6, 2026

Original job description

Work Location:

Toronto, Ontario, Canada

Hours:

37.5

Line of Business:

Analytics, Insights, & Artificial Intelligence

Pay Details:

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:

Department Overview:

The Model Validation (MV) group is a centralized model risk management function within the Bank. The MV team is responsible for the vetting and approval of complex mathematical and statistical models used in credit lending, business operations, and stress testing. By ensuring an objective and independent evaluation of models, the model validation function is critical to the effective measurement and management of risk across the TD Bank Group.

Job Description:

Perform validation of all models deemed in-scope by the bank-wide Model Risk Policy. These models are used in the Bank for a variety of purposes, including scoring credit risk (acquisitions and account management) and Economic and Regulatory Capital (e.g. Probability of Default, Exposure at Default, Loss Given Default), conducting stress testing, detecting fraud behaviors, marketing retail products, and projecting Pre-Provision Net Revenue (PPNR). These models may use traditional statistical methodologies and/or AI/ML approaches.
Develop independent benchmarks for use in the validation of the models listed above. These benchmark models could be traditional supervised learning methods, unsupervised learning, or machine learning algorithms. Assess the appropriateness of the model for its specific use, reasonableness of the model assumptions and the accuracy of the model implementation.
Prepare detailed reports describing the mathematical underpinnings of the model, validation techniques employed, test results obtained, and any model limitations noted.
Prepare management summaries highlighting the outcome of the validation process for each model and outlining recommendations for approval or further improvements.
Establish and maintain productive working relations with internal model development groups and external vendors who have developed customized models for TD.
Play a key role in ensuring the appropriate use of risk models. Identify the need to implement new models/techniques for risk management as industry standards evolve and regulatory requirements change.
Stay current in knowledge of credit risk management methodologies, predictive modeling, and statistical analysis.

Requirements and/or qualifications:

Strong statistical background and excellent analytical and problem-solving skills with a graduate degree in one or more of the following areas: statistics, economics, finance, mathematics, computer science, or engineering.
Experience with predictive model development, credit scoring, and/or AI/ML modeling
Hands-on experience with Python for statistical analysis and modeling or SAS is a must (Python preferred)
Familiarity with retail banking products, customer behaviors, and credit risk management is a definite asset
Experience with liquidity forecasting and liquidity stress testing models is a major plus
Familiarity with AI productivity tools such as Microsoft Copilot and GitHub Copilot is a plus
Excellent verbal and written communication skills
Good time management and multitasking skills
Quick learner who grasps new concepts and techniques quickly
Must be a good team player

Specific Educational Requirements / Accreditations:

Graduate degree in Statistics, Actuarial Sciences, Econometrics, Computer Science, or other quantitative fields.
2+ years relevant experience.

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 Package
Our 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 more

Additional 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 & Onboarding
We 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):

Sans Objet

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More about this job

Responsibilities

The analyst will perform independent validation of complex mathematical and statistical models used for credit risk, stress testing, and financial forecasting. They will also develop benchmark models and prepare detailed reports outlining model assumptions, limitations, and recommendations for management.

Requirements

Candidates must possess a graduate degree in a quantitative field such as statistics, mathematics, or computer science, along with at least 2 years of relevant experience. Proficiency in Python or SAS and a strong background in predictive modeling or credit risk are required.

Skills

Statistical analysis
Predictive modeling
Credit risk management
Python
SAS
Machine learning
AI modeling
Stress testing
Model validation
Data analysis
Problem-solving
Communication skills
Time management
Multitasking
Financial modeling
Risk management

Benefits

Health and well-being benefits
Savings and retirement programs
Paid time off
Banking benefits and discounts
Career development
Reward and recognition programs

Visa sponsorship

Not detected in the job text

Keywords

Model Validation
Credit Risk
Stress Testing
Python
SAS
Machine Learning
AI
Predictive Modeling
Statistical Analysis
Risk Management
Retail Banking
Liquidity Forecasting
Microsoft Copilot
GitHub Copilot
PPNR
Regulatory Capital
Probability of Default
Exposure at Default
Loss Given Default
Supervised Learning
Unsupervised Learning
Quantitative Analysis
Financial Institutions
Data Analytics
Model Risk Policy

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