Join Deep Native as a Data Scientist in Toronto (Hybrid) and tackle financial crime at scale using contemporary 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 settings with documentation
Excellent communication with non-technical stakeholders Utilize your data science skills to drive effective solutions against financial crime in the fintech landscape.

📌 Data Scientist For Fintech In Toronto
🏢 Socket.dev
📍 Toronto