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Data Scientist II
TD. 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 .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and optimizing machine learning models for fraud detection and risk management, with strong proficiency in Python and SQL. Capable of leading cross-functional initiatives and effectively communicating technical concepts to diverse stakeholders.
Highest-signal resume keywords
Machine Learning Model DevelopmentPython ProgrammingSQL ProficiencyFraud Analytics ExperienceModel Validation Expertise
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Statistical ModelingFeature EngineeringData VisualizationPredictive ModelingPerformance EvaluationHyperparameter TuningData PreparationExploratory AnalysisModel MonitoringRisk Metrics Analysis
Soft Skills
Presentation SkillsWritten CommunicationStakeholder ManagementIndependent ManagementCross-Functional Collaboration
Tools & Technologies
Azure DatabricksAzure Machine LearningMLOps PipelinesStreamlitFICO Platforms
Industry Keywords
Fraud DetectionIdentity VerificationCybersecurityFinancial CrimeDigital BankingModel GovernanceModel Risk ManagementGraph AnalyticsNetwork AnalysisAdvanced AI Techniques
Tech Stack
Tools & technologiesAzureCloudCyber SecurityPythonSQL
About the role
Key responsibilities & impact- 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, 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 fraud and risk metrics including capture rate, false-positive rate, lift, precision, recall, stability, and customer friction
- Build reproducible, documented analytical workflows using Python, SQL, and enterprise data platforms
- Support implementation and productionization with Engineering, FICO, MLOps, and platform teams
- Perform model monitoring, stability and segmentation analysis, performance diagnostics, and issue investigation
- 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 detection coverage improvements
- Prepare and deliver executive-ready presentations, technical documentation, reports, and business impact summaries
- Lead cross-functional discussions on data requirements, model scope, success criteria, risks, dependencies, timelines, implementation considerations, and next steps
- Contribute to internal modeling standards, reusable analytical frameworks, development practices, and proprietary fraud and authentication capabilities
Requirements
What you’ll need- 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, model risk, model governance, or independent model review processes is a preferred asset
- Experience in fraud analytics, authentication, cybersecurity, financial crime, identity verification, or digital banking is preferred
- Experience developing fraud, risk, or customer behaviour models using highly imbalanced datasets is preferred
- Experience with Azure Databricks, Azure Machine Learning, MLOps pipelines, Streamlit, FICO platforms, or other cloud-based analytics environments is preferred
- Experience supporting model implementation, production deployment, performance monitoring, and lifecycle management is preferred
- Familiarity with graph analytics, network analysis, or advanced AI techniques is an asset
- For Quebec roles: 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
Benefits
Comp & perks- Base salary
- Variable compensation
- Health and well-being benefits
- Savings and retirement programs
- Paid time off
- Banking benefits and discounts
- Career development
- Reward and recognition programs
- Regular career, development, and performance conversations
- Online learning platform
- Mentoring programs
- Training and onboarding sessions
- Interview accommodations, including accessible meeting rooms and captioning for virtual interviews