Join Deep Native as a Data Scientist in Toronto (Hybrid) and tackle financial crime at scale using modern transaction monitoring techniques. Your expertise will help convert vast transaction data into actionable insights.

This role involves analyzing large-scale data to identify patterns related to money laundering and suspicious activities. You will build detection logic for anti-money laundering (AML) practices, ensuring data integrity and quality across multiple layers. With responsibilities spanning from feature engineering to model validation, you will play a key role in combatting fraud in the fintech sector.

Key Responsibilities:
• Analyze transaction and account data to detect anomalies
• Construct and optimize AML detection frameworks




• Engineer features and datasets using Azure Databricks
• Translate legacy rules into transparent, testable code
• Monitor model performance and ensure data quality

Requirements:
• Strong proficiency in Python, SQL, and PySpark
• Experience in feature engineering on large datasets
• Solid background in statistics and model validation
• Comfortable in regulated environments with documentation
• Excellent communication with non-technical stakeholders

Utilize your data science skills to drive effective solutions against financial crime in the fintech landscape.
#J-18808-Ljbffr

📌 Data Scientist for fintech in Toronto (Ontario)
🏢 Socket.dev
📍 Ontario