Senior Risk Analyst

Haventree Bank•Toronto, ON

About The Position

Haventree Bank is a private Canadian Schedule 1 bank specializing in alternative mortgage programs and insured GIC deposits. We help hardworking Canadians from coast-to-coast achieve homeownership by offering flexible mortgage solutions. Our insured GIC deposits offer competitive rates and are available through a variety of wealth management platforms. Headquartered in Toronto, Ontario, Haventree Bank (Haventree) is a mission driven alternative mortgage lender. The name Haventree is representative of the bank’s mission to help its customers find a place of refuge and to lay down new roots for the future. Haventree exists to be a catalyst of financial security and upward mobility for Canadians who are underserved by the traditional financial system.

Requirements

A Master’s degree in a quantitative field (e.g., Statistics, Actuarial Science, Data Science, Financial Engineering, or Analytics), or a Bachelor’s degree in data science, machine learning, or a related field requiring rigorous statistical knowledge and techniques.
5+ years of hands-on experience in risk analytics, credit risk modelling, or a related quantitative role within financial services.
Strong experience with statistical and financial modelling methodologies (PD, LGD, EAD, ECL, stress testing).
Working knowledge of regulatory frameworks including IFRS 9, Basel, ICAAP, and OSFI guidelines (e.g., B-15, E-23).
Hands-on experience with cloud platforms like AWS, including working with data lakes and scalable data processing tools such as Databricks.
Proficiency with analytics and automation tools (e.g., Alteryx, Knime, Python, R).
Knowledge of credit risk or insurance frameworks and regulatory requirements (e.g., Basel, IFRS 9, IFRS 17, ICAAP), with the ability to apply actuarial science concepts to credit and other emerging risk domains
Proficiency with tools like Alteryx and Dataiku for automating analytics workflows and enabling machine learning applications.
Experience in geospatial data analysis, including working with QGIS and integrating raster and vector data to solve real-world business problems, such as climate risk assessment and geographic optimization.
Familiarity with data visualization tools (e.g., Tableau, Power BI) or libraries (e.g., Matplotlib, ggplot2) to effectively communicate insights.
Experience in synthesizing data, including generating synthetic datasets for model development.
Exceptional analytical and problem-solving skills, with the ability to translate complex business problems into actionable technical solutions.
Ability to independently research new techniques and implement solutions using diverse resources.
Excellent communication skills, with the ability to articulate their findings and recommendations clearly to both technical and non-technical stakeholders.

Responsibilities

Own the end-to-end ECL process including model development and maintenance of PD, LGD, and EAD models, qualitative and quantitative overlay analysis, and IFRS 9 compliance.
Develop executive-level reporting, analyze results, and present outcomes to Senior Management.
Develop, execute, and maintain stress testing frameworks for credit risk, climate risk, and other emerging risk scenarios as part of the annual ICAAP cycle. Communicate outcomes and participate in meetings with internal executives and external stakeholders.
Monitor climate risk events that may impact the Bank's portfolio. Conduct geospatial and scenario-based analysis using geographic risk and macroeconomic data to quantify physical and transition risk exposures and inform lending and operational strategy.
Support the calibration of risk appetite limits by product and risk exposure, providing data-driven recommendations that align with the Bank's Risk Appetite Framework.
Provide effective oversight and challenge of financial risk indicators received from Treasury, including interest rate risk, liquidity risk, and capital risk metrics, ensuring accuracy and alignment with the Bank's risk appetite.
Leverage advanced analytics platforms (e.g., Alteryx, Dataiku, Python, Knime) to automate workflows, analyze large and complex datasets, and extract actionable insights focused on risk mitigation and financial performance.
Monitor, validate, and fine-tune existing models and methodologies to ensure ongoing accuracy, fairness, and regulatory compliance.
Independently explore and implement cutting-edge methodologies from academic research, open-source communities (e.g., GitHub, Kaggle), and industry best practices to drive team innovation and stay ahead of trends.
Contribute to the preparation of regulatory submissions and risk disclosures (e.g., OSFI returns, B-15 climate disclosures, Pillar 3) by working with stakeholders to provide data, analysis, and quality assurance.
Partner with Risk Management, Treasury, Finance, IT, and Business Operations to align model outputs and risk insights with strategic goals. Present findings clearly to both technical and non-technical audiences.