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Data Scientist II

Toronto, ON, Canada
Montreal, QC, Canada

$81,600 - $115,200 CAD · Full-time

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Job details

Pay: $81,600 - $115,200 CAD
Job type: Full-time
Apply by: Oct 31, 2026
Experience: 3-5+ years of experience in data science, machine learning, statistical modeling, or advanced analytics.
Posting language: English
Posted: Oct 27, 2026

Original job description

Work Location:

Montréal, Quebec, Canada

Hours:

37.5

Line of Business:

Analytics, Insights, & Artificial Intelligence

Pay Details:

$81,600 - $115,200 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

The Data Scientist II will be responsible for developing, validating, and optimizing internal fraud and authentication models that support TD's objective of expanding proprietary risk capabilities and reducing reliance on external vendor models. The role covers the end-to-end model development lifecycle, including data discovery, feature engineering, experimentation, model evaluation, documentation, business impact assessment, and productionization support.

This is a hands-on development role with significant responsibility for presenting results and communicating with technical and non-technical stakeholders. The successful candidate will partner with Strategy, Engineering, Product, Data, Model Validation, and Risk teams to translate business challenges into scalable analytical solutions.

Key Responsibilities

Develop, validate, and optimize machine learning and statistical models for fraud detection, authentication, identity verification, account opening, account takeover, scam detection, and related risk use cases.

Lead end-to-end model development activities, including data discovery, exploratory analysis, data preparation, feature engineering, experimentation, model training, hyperparameter tuning, performance evaluation, and business impact assessment.

Evaluate internal models against vendor models and existing business strategies, using appropriate fraud and risk metrics such as capture rate, false-positive rate, lift, precision, recall, stability, and customer friction.

Build reproducible and well-documented analytical workflows using Python, SQL, and enterprise data platforms, and support implementation and productionization with Engineering, FICO, MLOps, and platform teams.

Perform model monitoring, stability and segmentation analysis, performance diagnostics, and issue investigation to identify degradation, data gaps, and remediation actions.

Prepare model documentation and analytical evidence for Model Validation, Model Risk Management, governance reviews, approvals, and lifecycle management.

Analyze customer, authentication, and fraud behaviour to identify emerging trends, risk gaps, feature opportunities, and areas where internal models can improve detection coverage.

Prepare and deliver executive-ready presentations, technical documentation, reports, and business impact summaries for technical and non-technical audiences.

Lead cross-functional discussions to align stakeholders on data requirements, model scope, success criteria, risks, dependencies, timelines, implementation considerations, and next steps.

Contribute to continuous improvement of internal modeling standards, reusable analytical frameworks, development practices, and proprietary fraud and authentication capabilities.

Qualifications Required

Undergraduate or graduate degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.

3-5+ years of experience in data science, machine learning, statistical modeling, or advanced analytics.

Strong hands-on proficiency in Python, SQL, machine learning, statistical analysis, feature engineering, and data visualization.

Experience developing and evaluating predictive models in a business or production-oriented environment.

Strong presentation, written communication, and stakeholder management skills.

Ability to explain technical methods, model results, limitations, and business impact to different audiences.

Ability to independently manage development activities across multiple initiatives in a fast-paced environment.

Preferred / Strong Assets

Previous experience with Model Validation (MV), model risk, model governance, or independent model review processes.

Experience in fraud analytics, authentication, cybersecurity, financial crime, identity verification, or digital banking.

Experience developing fraud, risk, or customer behaviour models using highly imbalanced datasets.

Experience with Azure Databricks, Azure Machine Learning, MLOps pipelines, Streamlit, FICO platforms, or other cloud-based analytics environments.

Experience supporting model implementation, production deployment, performance monitoring, and lifecycle management.

Familiarity with graph analytics, network analysis, or advanced AI techniques is an asset.

modeling capabilities.

Who We Are:

TD is one of the world's leading global financial institutions and is the fifth largest bank in North America by branches/stores. Every day, we strive to make every interaction, product, and experience remarkably human and refreshingly simple for over 27 million households and businesses in Canada, the United States and around the world. More than 95,000 TD colleagues bring their skills, talent, and creativity to foster deeper relationships, ensure disciplined execution, and build a simpler, faster banking experience. TD is deeply committed to being a leader in client experience, that is why we believe that all colleagues, no matter where they work, are client facing. Together, we are reimagining what banking can be for our clients, colleagues and communities.

Our Total Rewards Package
Our Total Rewards package reflects the investments we make in our colleagues to help them and their families achieve their financial, physical, and mental well-being goals. Total Rewards at TD includes a base salary, variable compensation, and several other key plans such as health and well-being benefits, savings and retirement programs, paid time off, banking benefits and discounts, career development, and reward and recognition programs. Learn more

Additional Information:
We’re delighted that you’re considering building a career with TD. Through regular development conversations, training programs, and a competitive benefits plan, we’re committed to providing the support our colleagues need to thrive both at work and at home.

Please be advised that this job opportunity is subject to provincial regulation for employment purposes. It is imperative to acknowledge that each province or territory within the jurisdiction of Canada may have its own set of regulations, requirements.

Colleague Development

If you’re interested in a specific career path or are looking to build certain skills, we want to help you succeed. You’ll have regular career, development, and performance conversations with your manager, as well as access to an online learning platform and a variety of mentoring programs to help you unlock future opportunities.

If you’re passionate about helping clients and building deep, lasting relationships, TD offers diverse career paths where you can grow your expertise and make a meaningful impact.

We're committed to your success and foster a respectful workplace where diverse perspectives are valued, everyone has fair opportunities to grow, and you can unlock your full potential to achieve your career goals. Here at TD, we hire and develop the best.

Training & Onboarding
We will provide training and onboarding sessions to ensure that you’ve got everything you need to succeed in your new role.

Interview Process
We’ll reach out to candidates of interest to schedule an interview. We do our best to communicate outcomes to all applicants by email or phone call.

Accommodation
Your accessibility is important to us. Please let us know if you’d like accommodations (including accessible meeting rooms, captioning for virtual interviews, etc.) to help us remove barriers so that you can participate throughout the interview process.

We look forward to hearing from you!

Language Requirement (Quebec only):

Maîtrise d’une langue autre que le français pour offrir du soutien ou traiter avec des employés ou des collègues qui ont besoin de services et de soutien dans une langue autre que le français.

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More about this job

Responsibilities

The Data Scientist II will develop, validate, and optimize machine learning models for fraud detection and authentication to enhance risk capabilities. This role involves managing the end-to-end model lifecycle, from data discovery and feature engineering to productionization and stakeholder communication.

Requirements

Candidates must hold an undergraduate or graduate degree in a quantitative field and possess 3-5+ years of experience in data science or statistical modeling. Strong proficiency in Python, SQL, and predictive model development in a business environment is required.

Skills

Python
SQL
Machine learning
Statistical modeling
Data science
Feature engineering
Fraud detection
Authentication models
Data visualization
Predictive modeling
Stakeholder management
Model validation
Risk management
Azure Databricks
MLOps
Cybersecurity

Benefits

Health and well-being benefits
Savings and retirement programs
Paid time off
Banking benefits and discounts
Career development
Reward and recognition programs

Visa sponsorship

Not detected in the job text

Keywords

Data Science
Machine Learning
Statistical Modeling
Python
SQL
Fraud Detection
Authentication
Risk Management
Feature Engineering
Azure Databricks
Azure Machine Learning
MLOps
FICO
Model Validation
Data Visualization
Cybersecurity
Financial Crime
Identity Verification
Predictive Analytics
Model Governance
Streamlit
Graph Analytics
Network Analysis
Artificial Intelligence
Business Impact Assessment
Data Discovery
Hyperparameter Tuning
Performance Evaluation
Stakeholder Management
Analytical Workflows

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