Senior Data Scientist, Fraud Detection — ML at Scale (PTO, Stock)

DataVisor; Toronto; Presencial; CAD 168.000 - 210.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

PTO

Stock Options

Health Benefits

Descripción de la vacante

DataVisor in Toronto is seeking a Senior-Level Data Scientist to join the 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.

Formación

• Master's or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.
• 3+ years of applied experience in fraud detection, cybersecurity, or related adversarial/high-velocity risk domains.
• Strong programming skills in Python and proficiency with SQL; experience with PySpark is a significant plus.

Responsabilidades

• Lead end-to-end model development for fraud detection features, from ideation to production and monitoring.
• Develop highly predictive features from large-scale data, including user behavior, device intelligence, and transaction records.
• Work with massive datasets using Spark and SQL and leverage cloud platforms (AWS, GCP, Azure).
• Lead investigations into complex fraud cases and produce evidence-backed reports for stakeholders.

Conocimientos

Python

SQL

PySpark

Educación

Master's or PhD in Computer Science or related quantitative field

Herramientas

Spark

Hadoop

AWS

GCP

Azure

Descripción del empleo

DataVisor in Toronto is seeking a Senior-Level Data Scientist to join the 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.