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About the Role
The Data Scientist II will develop, validate, and optimize internal fraud and authentication models to support TD's objective of expanding proprietary risk capabilities and reducing reliance on external vendor models. This role encompasses the end-to-end model development lifecycle, including data discovery, feature engineering, experimentation, model evaluation, documentation, business impact assessment, and productionization support. It is a hands-on development role requiring presentation of results and communication with technical and non-technical stakeholders, partnering with various teams to translate business challenges into scalable analytical solutions.
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.
Requirements
- 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.
- 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.
- 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.
Skills
- Python
- SQL
- Machine learning
- Statistical analysis
- Feature engineering
- Data visualization
- Graph analytics
- Network analysis
- Advanced AI techniques
Location
- Montréal, Quebec, Canada
Work Type
- Full-time
Experience Level
- 3-5+ years
Education Level
- Undergraduate or graduate degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.
Salary/Compensations
- $81,600 - $115,200 CAD
Benefits
- Health and well-being benefits
- Savings and retirement programs
- Paid time off
- Banking benefits and discounts
- Career development
- Reward and recognition programs
- Training programs
- Online learning platform
- Mentoring programs
About the Company
- 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.
Equal Opportunity
- 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.
- 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.
- We will provide training and onboarding sessions to ensure that you’ve got everything you need to succeed in your new role.
- 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.
- 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.