Join Deep Native as a Data Scientist in Toronto (Hybrid) and tackle financial crime at scale using up-to-date 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 clear, 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
🏢 Socket.dev
📍 Toronto