Data Scientist, Credit Risk Strategy (RESL) (ATH 5274)

TD Bank Group; Toronto; Sur place; CAD 97 000 - 137 000; Plein temps

Résumé du poste

TD Bank Group in Toronto, Ontario, is seeking a hands-on Data Scientist for risk management. This role leads strategic credit risk initiatives within the RESL portfolio, using data, analytics, and emerging technologies to strengthen risk decision-making and oversight.

You will work with SQL, Python, and complex data, develop dashboards, support audits, and help define future-state processes that balance innovation with prudent risk discipline.

Qualifications

• Bachelor's degree from an accredited university in mathematics, statistics, engineering, computer science, data science.

Responsabilités

• Independently source, prepare, validate, and analyze complex data using SQL and Python.
• Investigate credit risk questions, identify trends, and assess risk implications.
• Translate analysis into evidence-based recommendations and actions.
• Develop and maintain monitoring, reports, dashboards, and visualizations.
• Provide data support for testing, audit, and risk reviews.

Connaissances

SQL

Python

Data analysis

Formation

Bachelor's degree in mathematics, statistics, engineering, computer science, data science

Description du poste

Work Location

Toronto, Ontario, Canada

Hours

37.5

Line of Business

Risk Management

Pay Details

$96,900 - $136,800 CAD

TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.

As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.

Job Description

Role Overview

This role leads strategic credit risk initiatives within the RESL portfolio, leveraging data, analytics, and emerging technologies to strengthen credit risk management, decision‑making, and oversight. The role is well suited for someone who is naturally curious, enjoys solving complex problems, and can work comfortably with complex data to uncover meaningful risk insights and translate them into practical actions.

The role requires strong hands-on analytical capabilities to support a range of risk management activities, including deep-dive analysis, ongoing monitoring and reporting, and data-driven support for testing, audit, and other risk reviews. Beyond analytics, the individual will contribute to strategic and transformation initiatives that leverage new technologies to enhance credit risk processes, controls, and decision‑making. This requires the ability to quickly understand existing business processes, decision frameworks, policies, data, and control requirements, identify opportunities for improvement, and help define practical future‑state solutions.

Success in this role requires a combination of strong analytical thinking, adaptability, and sound and prudent risk judgment. The individual should be a quick learner who is comfortable navigating ambiguity, learning and applying new technologies, and connecting perspectives across risk, business, data, and technology. They are expected to challenge assumptions and constructively question the status quo, while balancing innovation with appropriate risk discipline and translating complex ideas into clear, practical, and executable outcomes.

Ideal Candidate Profile

The ideal candidate is a hands-on, analytically strong data scientist who can independently work with complex data, solve ambiguous problems, and turn analysis into meaningful risk insights and practical actions. They combine strong SQL and Python capability with sound risk judgment, intellectual curiosity, adaptability, and clear communication. They can quickly understand unfamiliar processes, policies, decision frameworks, data, and controls; apply new technologies to real business problems; and independently lead cross-functional work from problem definition through implementation.

Key Accountabilities

Data Analysis & Risk Insights

• Independently source, prepare, validate, and analyze complex data using SQL and Python.
• Investigate complex credit risk questions, test hypotheses, identify trends and emerging issues, and determine their risk implications.
• Translate analysis into clear, evidence-based recommendations and actions.
• Develop and maintain accurate, timely, and decision-useful monitoring, reports, dashboards, and visualizations.
• Provide data and analytical support for testing, audit, and other risk reviews.

Risk Strategy & Transformation

• Assess current processes, decision frameworks, policies, data, and controls to identify specific gaps and improvement opportunities.
• Translate business and risk needs into clear requirements and practical future-state solutions that leverage data, analytics, AI, and emerging technologies.
• Evaluate proposed solutions from a credit risk perspective, including decision impacts, data requirements, control gaps, and governance considerations.
• Define a practical path from current state to implementation and support delivery through testing and rollout.

Project Leadership & Stakeholder Management

• Independently lead complex work from problem definition through analysis, recommendation, and implementation, with accountability for quality, timelines, and outcomes.
• Work effectively across risk, business, data, regulatory reporting, operations, and technology teams to deliver results.
• Communicate findings, trade-offs, risks, and recommendations clearly to technical and non-technical stakeholders, including senior leadership.
• Use evidence and sound judgment to influence decisions and constructively challenge existing approaches.

Required Qualifications

• Bachelor's degree from an accredited university in mathematics, statistics, engineering, computer science, data science,