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Added 9 mins ago
GD
Co-op Winter 2027 - Systems Integration, Verification, and Validation - 8-12 months
General Dynamics Missions System International
Ottawa, ON
- Internship
Experience with scripting or programming languages such as Python, PowerShell, or Java.
Added 9 mins ago
GD
Co-op Winter 2027 – System of Systems Integration, Verification and Validation – 4-8 months
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Added 1 hour ago
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RISC Foundation (Risk and Insurance Studies Centre)

WINTER 2027 INTERNSHIP - Quantitative Analyst Intern (Master’s/PhD)

Toronto, ON, Canada

Contract

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Job details

Job type: Contract
Apply by: Oct 23, 2026
Posting language: English
Posted: Oct 9, 2026

Original job description

***PLEASE READ THE JOB POSTING CAREFULLY AND SUBMIT YOUR FULL APPLICATION THROUGH EMAIL***

APPLICATION INFORMATION

Apply to this role by sending the below information to info@therisc.ca, with the subject “Winter 2027 Internship - Quantitative Analyst Intern”:

Your latest resume;
Official transcripts (if applicable);
Cover letter addressing the “Qualifications” section below (2 pages max.);
Names and contact details of 2 references who have directly supervised your work. Reference letters will not be accepted.

The deadline to apply for this position is October 23, 2026. Please note that an in-person interview in the Toronto office may be required.

NOTE: Incomplete applications will not be considered. Applications that do not meet the “Requirements” section will not be considered. Only selected candidates will be contacted.

YOUR ROLE

We are seeking a Winter 2027 intern for the role of Quantitative Analyst Intern at our partner organization, a leading Canadian-based financial services firm, in their Model Validation and Analytics Unit. You will be interning at a leading Canadian-based financial services company with a long history of helping clients achieve lifetime financial security and live healthier lives.

In this role, you will support the validation and assessment of mathematical, financial, and actuarial models, applying strong quantitative, analytical, and problem-solving skills. The ideal candidate is technically strong, learns quickly, and can creatively apply financial knowledge and modelling skills to assess models incorporating financial engineering, risk management, and statistical techniques.

RESPONSIBILITIES

Assist in validating existing and new models in compliance with the company’s Model Risk Policy, standards, and guidelines.
Develop and implement various testing such as benchmark model, sensitivity and stress testing, or any other statistical testing to assess accuracy, completeness, and robustness of the model.
Evaluate the performance and conceptual soundness of machine learning and Generative AI models where applicable.
Communicate model review results and key findings to the managers and model owners.
Prepare validation scope documents, testing approaches, test results, findings documents, and validation reports.
Interact with model owners and model developers to gather necessary data, clarify model methodologies and resolve identified model issues.
Support other ongoing research, projects, and continuous improvement initiatives within Corporate Model Risk Management unit as needed.

QUALIFICATIONS

Must be a graduate-level student from the following universities: University of Waterloo, University of Toronto, York University or Western University.
Currently pursuing a Master's degree or a higher degree in a quantitative field such as Mathematics, Statistics, Mathematical Finance, Financial Engineering, or a related field.
Strong understanding of financial mathematics, risk management concepts, and statistical modelling.
Solid working knowledge of machine learning models and techniques including their application in risk management and finance.
Proficiency in at least one programming language (Python, MATLAB, or R).
Strong Excel skills, including hands-on experience with VBA programming.
Excellent problem-solving skills and ability to work independently and collaboratively.
Strong verbal and written communication skills, adept at explaining technical concepts clearly.
Ability to analyze complex models and datasets with attention to detail.
Ability to work under tight deadlines and with multiple priorities.

Work hours:

Full-time

Duration:

4 months, with possibility of extension

Work mode:

Hybrid

Application deadline:

October 23, 2026

How to apply:

Please refer to the instructions at the beginning of the job posting. Only full applications meeting the "Qualifications" criteria sent via email will be considered.

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More about this job

Responsibilities

Validate existing and new mathematical, financial, actuarial, machine learning, and generative AI models through benchmarking, sensitivity and stress testing, and other statistical assessments. Document and communicate validation methods, results, and findings, collaborate with model owners and developers, and contribute to research and continuous improvement initiatives.

Requirements

Applicants must be graduate-level students at the University of Waterloo, University of Toronto, York University, or Western University, pursuing a master's degree or higher in a quantitative field. They need knowledge of financial mathematics, risk management, statistical modeling, and machine learning, proficiency in Python, MATLAB, or R, strong Excel and VBA skills, and effective analytical, communication, and teamwork abilities.

Skills

Model Validation
Quantitative Analysis
Financial Mathematics
Risk Management
Statistical Modeling
Machine Learning
Generative AI
Python
MATLAB
R
Excel
VBA Programming
Sensitivity Testing
Stress Testing
Problem-Solving
Technical Communication

Visa sponsorship

Not detected in the job text

Keywords

Quantitative Analysis
Model Validation
Model Risk Policy
Financial Mathematics
Actuarial Models
Financial Models
Risk Management
Statistical Modeling
Machine Learning
Generative AI
Financial Engineering
Benchmark Models
Sensitivity Testing
Stress Testing
Statistical Testing
Model Performance
Conceptual Soundness
Python
MATLAB
R
Microsoft Excel
VBA
Validation Reports
Data Analysis

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