Risk Analyst - Expert

We are seeking a highly accomplished Risk Analyst - Expert for an enterprise-level contract opportunity based in Toronto. In this role, you will take on a premier analytical and predictive modeling capacity within commercial and wholesale credit risk streams, specializing in designing, building, and deploying advanced machine learning models.

As a principal risk analyst, you will bridge the gap between large-scale financial datasets, cloud computing technologies, and core credit risk strategies. Operating within a high-performing risk management environment, you will lead the end-to-end model development lifecycle—from data extraction and feature engineering through to model validation, deployment, and ongoing performance monitoring. This position demands a quantitative authority who can apply statistical algorithms, machine learning techniques, and predictive analytics to mitigate credit losses and optimize risk-weighted revenue across commercial and wholesale portfolios.

Location: Toronto, ON

Contract Duration: 12-months Contract and potential for extension

Hybrid role - 4 days in office

Advantages
High-Impact Quantitative Scope: Develop, monitor, and deploy advanced machine learning and predictive credit risk models.

Modern Big Data & Cloud Stack: Advance technical expertise leveraging PySpark, SQL, Hadoop, AWS S3, and Python.

Cross-Functional Influence: Collaborate directly with adjudication leads and line-of-business partners to ensure business-sound model adoption.

End-to-End Model Ownership: Lead feature engineering, automated scoring, independent model validation, and audit resolution.

Responsibilities
Source, extract, clean, validate, and analyze high-volume structured data across multi-source platforms to quantify borrower behavior patterns.

Build, monitor, tune, and deploy advanced statistical, credit scoring, machine learning, and surveillance models.

Engineer feature farms and automate underlying credit modeling pipelines to enable efficient, end-to-end model scoring and deployment.

Engage with cross-functional stakeholders, risk adjudicators, and business leads throughout the model development lifecycle to ensure models align with business logic.

Prepare comprehensive technical model documentation, clean source code repositories, presentation decks, and ongoing model monitoring reports.

Address and resolve model findings raised by independent model validation teams, internal audit, and routine performance monitoring.

Utilize version control frameworks (GitHub) and command-line interfaces to maintain scalable codebases.

Adapt quickly to evolving business demands, delivering ad-hoc quantitative analysis and strategic risk insights to leadership.

Qualifications
Core Technical & Quantitative Requirements
Education: Undergraduate degree in Computer Science, Finance, Mathematics, Statistics, Economics, or a related quantitative field. (Master's degree is considered an asset).

Credit Risk Modeling Tenure: Minimum 5 years of progressive working experience in dedicated credit risk modeling, quantitative analysis, or predictive analytics roles.

Big Data & Cloud Fluency: Hands-on experience ingesting, merging, processing, and aggregating large-scale datasets using SQL, Hadoop, PySpark, and AWS S3 environments.

Python Development: Strong Python programming skills for end-to-end model scoring, implementation, and workflow automation.

Statistical & ML Mastery: Deep understanding and practical application of advanced statistical methods, time-series analysis, and machine learning algorithms for classification and regression tasks.

Version Control & Environment: Proficiency with GitHub for code sharing/version control and working within UNIX command-line environments.

Preferred Assets
Advanced Languages & Frameworks: Exposure to R, Java, SAS, or generative AI (GenAI) use cases within commercial and retail lending.

Regulatory Modeling Exposure: Prior experience developing models for IFRS 9, stress testing, or regulatory capital measurement.

Soft Skills & Strategic Attributes
Analytical & Conceptual Thinking: Superior systematic thinking and problem-solving skills to derive valid inferences from complex financial datasets.

Stakeholder Management & Impact: Proven ability to influence outcomes, build strong cross-functional partnerships, and present technical findings clearly to diverse audiences.

Summary
If you're interested in the "Risk Analyst - Expert" role based in Toronto, we encourage you to apply online at www.randstad.ca.
Only qualified candidates will be contacted for the next steps. We look forward to hearing from you!

Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity-seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non-binary/gender non-conforming; Indigenous or Aboriginal Peoples; persons with disabilities (visible or invisible) and; members of visible minorities, racialized groups and the LGBTQ2+ community.

Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to accessibility@randstad.ca to ensure their ability to fully participate in the interview process.