Senior Manager, Modeling and Analytics

About the Role

Key Responsibilities

• Lead the development of models, other estimation approaches and critical tool calculators for estimating and assess the model risk to confirm model appropriateness and capability for a designated portfolio.
• Develop and maintain strong credit risk models and communicate decisions regarding the models to the business to ensure transparency and understanding of models and model risks.
• Analyze and present insights into the model results to inform management overlays in the reporting process.
• Manage relationships with model validation, owners, and users, influencing and advising on model risk-related matters.
• Recommend business priorities, advise on resource requirements, and develop roadmaps for strategic execution.
• Manage resources and lead the execution of strategic initiatives to deliver on business and financial goals.
• Drive change management programs including readiness assessments, planning, stakeholder management, execution, evaluation, and sustainment of initiatives.
• Act as a subject matter expert on relevant regulations and policies, interpret new regulations, and assess impacts to the validation framework.
• Connect technical issues with business considerations to ensure comprehensive risk understanding.

Requirements

• An MBA or MSc or PhD in quantitative fields such as statistics, applied mathematics, financial mathematics, data science, actuarial sciences, engineering, or related fields.
• A minimum of 7 years of experience in model development and/or validation within a financial institution, particularly in the credit risk area.
• Experience in credit risk models, including Stress Testing, Provisioning, loss forecasting, AIRB and Adjudication/Account Management.
• Proficiency in programming languages such as Python, R, and SAS (Python preferred).
• Proficiency in working with relational databases - SQL or Oracle.
• Knowledge of Big data tools and Cloud platforms (AWS/Azure) is a plus.
• Strong analytical, critical thinking, and problem-solving skills.
• Excellent verbal and written communication skills.
• Ability to manage ambiguity and work collaboratively across teams.

Nice to Have

• Certifications such as CFA/FRM (helpful but not mandatory).
• Experience with machine learning, data visualization, data wrangling, and data preprocessing.
• Experience with regulatory capital and stress testing.

Qualifications

• Typically 7+ years of relevant experience and post-secondary degree in related field or an equivalent combination of education and experience.
• Seasoned professional with a strong industry knowledge base.

Benefits & Perks

• Health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans.
• Performance-based incentives, discretionary bonuses, and other perks.
• Total compensation varies based on role specifics, experience, and location.