AML Data Scientist - Python & ML for Transaction Monitoring

BeachHead Inc.; Toronto; On-site; CAD 120,000 - 150,000; Full time

Job summary

BeachHead Inc. is seeking an experienced AML Developer/Data Scientist to lead analytical initiatives within transaction monitoring systems, focusing on model development and scenario optimization. You will work with internal teams to mitigate financial crime risks and enhance detection capabilities.

The role emphasizes building predictive models using Python, PySpark, Databricks, Azure, and SQL, with collaboration across global AML teams and a focus on AML/ATF scenarios and data-driven solutions.

Qualifications

• Extensive experience building predictive models with machine learning techniques.
• Proficiency in Python, PySpark, Databricks, Azure and SQL.
• Strong analytical skills with the ability to explore and interpret data.
• Ability to apply statistical techniques and data mining methods to solve business problems.
• Excellent written and verbal communication skills.
• Strong interpersonal skills and ability to collaborate in teams.
• Self-motivated, organized and able to manage multiple priorities independently.

Responsibilities

• Lead analytical activities related to AML transaction monitoring and model development.
• Maintain and optimize AML transaction monitoring scenarios and analytical queries.
• Tune performance of AML/ATF scenarios and monitor ML model execution.
• Document compliance with audit, regulatory, and internal standards.
• Collaborate with global AML teams to resolve monitoring issues.
• Stay updated on data mining, ML, and AML-related innovations.
• Develop and implement improvements to models to enhance detection accuracy.
• Provide insights to improve processes and identify operational improvements.

Skills

Predictive modeling

Python

PySpark

Databricks

Azure

SQL

Data analysis

Communication

Tools

Databricks

Azure

Tableau

Java

Scala

Job description

BeachHead Inc. is seeking an experienced AML Developer/Data Scientist to lead analytical initiatives within transaction monitoring systems, focusing on model development and scenario optimization. You will work with internal teams to mitigate financial crime risks and enhance detection capabilities.

The role emphasizes building predictive models using Python, PySpark, Databricks, Azure, and SQL, with collaboration across global AML teams and a focus on AML/ATF scenarios and data-driven solutions.