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Added 1 min ago
Lead Quant Analyst - Model Risk Management
Hallmark Global Solutions Ltd
ON · Ontario, Canada
Full-time
Job Description- Lead Quant Analyst - Model Risk Management
Added 1 min ago
Enterprise Architect
Nutrien
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Full-time
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SAP Technical Lead / Manager – S/4HANA Deployment
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Added 1 hour ago
CTO | VC-backed AI | Relocate to Barcelona | €120–150k + equity
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Then Bert leaves the day-to-day and remains an advisor and mentor. Other senior technical leaders around Ink are available for mentorship too.

DataVisor

Senior Data Scientist - Fraud Detection

Remote, Canada

Pay: Base salary range: $120,000–$150,000, commensurate with experience.
Job type: Full-time
Workplace: Remote
Posting language: English
Posted: Sep 23, 2026

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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.

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.

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

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More about this job

Responsibilities

Lead the full lifecycle of fraud detection machine learning models and develop predictive features from large-scale datasets. Conduct hands-on investigations into complex fraud cases to reconstruct attacker behavior and generate actionable trend reports.

Requirements

Requires a Master's or PhD in a quantitative field and at least 3 years of applied experience in fraud detection or cybersecurity. Candidates must possess strong Python and SQL skills, along with an investigator mindset for pattern synthesis and signal detection.

Skills

Machine Learning
Fraud Detection
Python
SQL
PySpark
Data Exploration
Feature Engineering
Big Data
Agentic AI
Cybersecurity
Statistical Analysis
Data Modeling
Pattern Synthesis
Hypothesis Testing
Technical Reporting

Benefits

Pto
Stock Options
Health Benefits

Visa sponsorship

Not detected in the job text

Keywords

Data Scientist
Fraud Detection
Machine Learning
SaaS
AML
Unsupervised Learning
Big Data
Spark
Python
SQL
PySpark
AWS
GCP
Azure
Feature Engineering
Agentic AI
Cybersecurity
Fintech
Data Exploration
Predictive Modeling
Logistic Regression
Gradient Boosting
Network Graphs
Device Intelligence
Risk Management

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