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RM
Supervisor, Centralized Scheduling
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- Full-time
Lead or participate in process improvement initiatives that enhance scheduling efficiency, data accuracy, and service quality.
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DataVisor

Senior Data Scientist - Fraud Detection

Toronto, ON, Canada

$120,000 to $150,000 CAD / year · Full-time

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Job details

Pay: $120,000 to $150,000 CAD / year
Job type: Full-time
Apply by: Apr 25, 2027
Posting language: English
Posted: Oct 2, 2026

Original job description

About DataVisor

DataVisor is the world's leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's fraud and anti-money laundering (AML) solutions scale infinitely and enable organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine, and investigation tools work together to provide significant performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering total cost of ownership compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.

Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results-driven. Come join us!

Position Overview

We are looking for a Senior-Level Data Scientist to join our Fraud Detection team - someone equally comfortable building production ML models and getting hands-on with individual fraud cases. This is a dual-track role: you'll develop the machine learning systems that catch fraud at scale, and you'll personally lead investigations into how specific fraud attacks happened, reconstructing attacker behavior and turning case-level findings into trend reports and detection improvements. This is a great opportunity to grow your skills in a fast-paced, data-driven environment while making a real, visible impact in the fight against fraud.

Key Responsibilities

Machine Learning & Model Development

End-to-End Model Development: Lead the full lifecycle of fraud detection features and models, from ideation and data exploration to prototyping, productionizing, and monitoring
Advanced Feature Engineering: Develop highly predictive features from complex, large-scale, multi-dimensional data, including user behavior, device intelligence, network graphs, and transaction records
Large-Scale Data Processing: Work with massive, noisy, and imbalanced datasets (billions of events) using tools like Spark, SQL, and our proprietary AI platform
Agentic AI & Automation: Leverage agentic AI to automate analytic pipelines and develop reusable skill tools that accelerate fraud investigation, feature generation, and reporting workflows

Live Fraud Investigation & Reporting

Lead investigations into complex fraud cases across identities, accounts, devices, and transaction surfaces. Reconstruct attacker sequences and hypothesize actor intent and tooling
Produce clear, evidence-backed technical reports and case studies for product, engineering, operations, legal, and executive stakeholders
Generate fraud trend reports for customers, synthesizing case-level findings and aggregate data into narratives customers can act on

Requirements

Qualifications

Master's or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.

3+ years of applied experience in fraud detection, cybersecurity, or a related adversarial/high-velocity risk domain (fintech, consumer payments, banking, SaaS, marketplace risk, or security research)
Solid understanding of both classic machine learning models (Logistic Regression, Gradient Boosting, etc.)
Hands-on experience with the machine learning lifecycle in a production environment
Investigator mindset: demonstrated skill in pattern synthesis, hypothesis testing, and triaging signal from noise in ambiguous, adversarial cases - not just building and monitoring models
Strong programming skills in Python (must-have) and proficiency with SQL; experience with PySpark is a significant plus
Experience with large-scale data tools (Spark, Hadoop, etc.) and cloud platforms (AWS, GCP, Azure)
Excellent communication skills - able to explain complex, ambiguous, or technical behavior clearly to both technical and non-technical audiences, including customers and executives
Professional proficiency in written and spoken English, with the ability to collaborate effectively in a global, cross-functional team

Benefits

Base salary range: $120,000-$150,000, commensurate with experience
PTO, Stock Options, Health Benefits

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More about this job

Responsibilities

Develop, productionize, and monitor fraud detection models and features using large-scale, complex data, and apply agentic AI to automate analytic and investigation workflows. Lead investigations into fraud cases, reconstruct attacker behavior, and produce evidence-based case studies and customer trend reports.

Requirements

Requires a master's or PhD in a quantitative field and at least three years of applied experience in fraud detection, cybersecurity, or a related adversarial risk domain. Candidates need production machine learning experience, strong Python and SQL skills, familiarity with large-scale data tools and cloud platforms, an investigative mindset, and excellent communication skills.

Skills

Fraud Detection
Machine Learning
Feature Engineering
Fraud Investigation
Python
SQL
PySpark
Apache Spark
Large-Scale Data Processing
Data Analysis
Pattern Recognition
Hypothesis Testing
Agentic AI
Technical Reporting
Cloud Platforms
Communication

Benefits

Paid Time Off
Stock Options
Health Benefits

Visa sponsorship

Not detected in the job text

Keywords

Fraud Detection
Cybersecurity
Machine Learning
Logistic Regression
Gradient Boosting
Python
SQL
PySpark
Apache Spark
Hadoop
AWS
GCP
Azure
Agentic AI
Feature Engineering
Device Intelligence
Network Graphs
Transaction Records
Fraud Investigation
Pattern Synthesis
Hypothesis Testing
Imbalanced Datasets
Large-Scale Data Processing
Production Machine Learning
Risk Management
Fintech
Consumer Payments
Banking
SaaS
Marketplace Risk

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