Risk Analyst Expert

Role Overview: 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. Contract Details: - 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.