Manager, IFRS9 Modelling, Enterprise Stress Testing - 10 Month Contract

Scotiabank; Canada; On-site; CAD 90,000 - 130,000; Full time

Job summary

Join a leading financial institution in Canada as a Manager in IFRS 9 Modelling, where you will lead the development of credit risk models for retail portfolios. This role offers a chance to work with advanced machine learning techniques while collaborating with various stakeholders. You will thrive in an inclusive environment that rewards performance and provides ample career advancement opportunities.

Benefits

Competitive rewards including performance bonus

Employee share program

Pension matching

Health benefits from day one

Career development opportunities

Inclusive and collaborative environment

Qualifications

• Advanced degree required in a related field.
• Experience with predictive modelling and machine learning.
• Strong communication skills and ability to learn quickly.

Responsibilities

• Develop credit risk models for IFRS 9 provisioning and reporting.
• Document models and collaborate with stakeholders.
• Integrate models into analytics for stress testing.

Skills

Predictive modelling

Machine learning

Programming

Communication

Adaptability

Education

Advanced degree in Economics, Finance, Statistics, Mathematics, Physics, Engineering

Tools

Python

R

Linux/UNIX

Git

pyspark/sparklyr

Data visualization tools

Job description

Requisition ID: #

Join a purpose-driven, winning team committed to results in an inclusive and high-performing culture.

As Manager, IFRS 9 Modelling, Enterprise Stress Testing, you will contribute to the success of IFRS 9 credit risk modelling, analytics, and reporting for Canadian retail and/or small business portfolios. You will ensure the execution and delivery of specific goals, plans, and initiatives to support the team’s business strategies and objectives, while ensuring compliance with regulations, policies, and procedures.

You will report to a Senior Manager or Director and be a key member of the team developing models such as probability of default (PD), loss given default (LGD), and others used for credit risk estimation in Canadian lending products. Utilizing a modern machine learning stack, including open-source tools, you will handle model development, documentation, implementation, and maintenance. Collaboration with stakeholders and partners across validation, compliance, audit, and retail provisions will be essential.

Key Responsibilities:

1. Develop credit risk models predicting PD, LGD, EAD for IFRS 9 provisioning and reporting.
2. Document all models and processes, working with validation teams for timely approval.
3. Support integration of models into IFRS 9 and stress testing analytics.
4. Collaborate with stakeholders and tech teams to implement and test models in UAT and production environments.
5. Consider risk appetite and culture in daily activities and decisions.
6. Maintain effective operations and controls to meet operational, compliance, AML/ATF, and conduct risk obligations.

Qualifications:

• Advanced degree in Economics, Finance, Statistics, Mathematics, Physics, Engineering, or related field.
• Experience with predictive modelling, machine learning, and programming (Python or R with pandas or dplyr).
• Strong communication skills for stakeholder engagement and data presentation.
• Ability to learn new concepts quickly and adapt to changing environments.
• Experience with Linux/UNIX, version control (Git), and domain expertise in retail credit risk and IFRS 9 is an asset.
• Additional assets include experience with pyspark/sparklyr, data visualization tools like matplotlib, seaborn, or ggplot2.

What’s in it for you?

• An inclusive, collaborative environment that fosters creativity and success.
• Competitive rewards including performance bonus, employee share program, pension matching, and health benefits from day one.
• Career development and progression opportunities.

Location: Toronto, Ontario, Canada

Scotiabank is committed to diversity and inclusion. If accommodations are needed during the recruitment process, please inform our Recruitment team. Candidates must apply online. Only shortlisted candidates will be contacted.