Senior Analyst, Model Development & Validation

Quantum Technology Recruiting Inc. (QTR); Toronto; On-site; CAD 70,000 - 90,000; Full time

Job summary

A financial services recruiting firm in Toronto is seeking a Senior Analyst to manage the lifecycle of credit risk and loss forecasting models. The candidate will need to develop and validate statistical models, ensuring compliance with regulations. The ideal applicant has at least 2 years of experience, strong skills in SAS, SQL, and Python, and the ability to communicate insights effectively. This role emphasizes collaboration with business stakeholders and a commitment to quality work.

Qualifications

• Minimum 2 years of experience in predictive modeling and analytics in financial services.
• Proficient in SAS, SQL, and Python with large datasets.
• Strong grasp of statistical and machine learning techniques.

Responsibilities

• Design, develop, and validate risk and marketing models.
• Collaborate with stakeholders for model implementation.
• Conduct model validations and present findings.

Skills

Predictive modeling

Statistical analysis

Machine learning

SAS

SQL

Python

Communication skills

Problem-solving

Education

2+ years experience in analytics

Tools

Excel

Altair Knowledge Studio

Job description

Overview

Reporting to the Manager of Modeling & Validation, this role plays a key part in the full lifecycle of credit risk and loss forecasting models. The Senior Analyst will be responsible for developing, validating, implementing, and monitoring statistical and machine learning models, ensuring compliance with governance frameworks and industry standards. This position requires strong technical expertise in predictive modeling, a solid understanding of risk management, and hands-on experience working with large datasets in SAS, SQL, and Python.

Key Responsibilities

• Design, develop, and validate risk and marketing models using techniques such as logistic regression, decision trees, survival analysis, and advanced machine learning methods (e.g., bagging, boosting).
• Collaborate with stakeholders to ensure smooth implementation and deployment of models into production systems.
• Conduct thorough model validations to identify risks across assumptions, data inputs, methodologies, and processes; provide actionable recommendations for risk mitigation.
• Present validation findings to model owners and governance groups, influencing decisions on model updates or remediation strategies.
• Build and maintain model monitoring processes to ensure ongoing accuracy and reliability.
• Prepare and update technical documentation in alignment with regulatory requirements and internal standards.
• Partner with external vendors, including credit bureaus and modeling consultants, to meet data and analytical needs.
• Stay current with emerging statistical techniques, machine learning advancements, and best practices in model development.
• Provide expert advice and guidance to leadership, translating analytical insights into business strategies.
• Effectively communicate results and recommendations to both technical and non-technical stakeholders using clear narratives and visualization tools.

Qualifications

• Minimum 2 years of experience in predictive modeling and analytics within the financial services sector, ideally focused on credit risk.
• Proficiency in SAS, SQL, and Python with proven experience manipulating and analyzing large datasets.
• Strong grasp of statistical and machine learning techniques (regression, clustering, ensemble methods, etc.) and their application to real-world business problems.
• Familiarity with model development, validation, monitoring, and governance processes.
• Expertise with Microsoft Excel (advanced formulas, pivot tables) and database management tools (e.g., Microsoft Access or similar).
• Knowledge of credit risk management, financial products, and regulatory frameworks (e.g., IFRS9, Basel) is highly desirable.
• Experience with cloud-based analytics platforms (e.g., AWS) or tools like Altair Knowledge Studio is an advantage.
• Exceptional communication skills and the ability to collaborate effectively across technical and business teams.
• Strong organizational skills with the ability to manage multiple priorities in a fast-paced environment.
• High attention to detail, adaptability, and commitment to delivering high-quality work.