Data Scientist II
TD Bank is hiring a Data Scientist II in Montreal, Canada. It pays CAD 82k-CAD 115k a year and Level rates it ; you can apply on TD Bank.
Level's read on this job
- On Level's scale, this data scientist role is Builds AI ●●●●: the job is building AI systems.
- The posting mentions Databricks, Python and SQL among its tools and skills.
- Level classes it as a mid-level role.
- TD Bank has 1,700+ other open roles on Level.
- Posted in the last few days.
AI in this role
Data Scientist developing, validating, and optimizing internal fraud detection and authentication machine learning models.
Work Location:
Montréal, Quebec, CanadaHours:
37.5Line of Business:
Analytics, Insights, & Artificial IntelligencePay Details:
$81,600 - $115,200 CADTD 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.How we rate this
Data Scientist II at TD Bank rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in data scientist jobs at this level
Pay
- Typical data scientist job
- $165k
Use the typical data scientist figure as a reference when pay comes up. See data scientist jobs
Questions you could be asked
- How do you monitor a model once it's live, and how do you know it needs retraining?
- How do you decide that one model's output is better than another's for a given task?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where fraud detection was part of your work. What did you do?
- Tell me about a project where feature engineering was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: ML Ops, AI Evaluation, Machine learning, Fraud Detection, and Feature Engineering. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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