Data Analytics Engineer

Key Responsibilities:

• Partner with AML and Financial Crimes teams to identify analytical opportunities that improve detection and reduce false positives.
• Develop and maintain automated analytics workflows to support model tuning, alert optimization, and investigative efficiency.
• Conduct statistical analysis to evaluate and enhance BSA/AML model performance, including scenario calibration and threshold testing.
• Build tools and scripts in Python to support ad hoc investigations, data profiling, and model diagnostics.
• Write complex SQL queries to extract, transform, and analyze large volumes of transactional and customer data.
• Collaborate with compliance, risk, and data science teams to ensure alignment with regulatory expectations and internal governance.
• Document methodologies and results in a clear, audit-ready format.

Required Qualifications:

• Bachelor s or master s degree in data science, Statistics, Computer Science, or a related field.
• 3+ years of experience in a data analytics or engineering role, preferably supporting AML, fraud, or financial crime teams.
• Advanced proficiency in SQL and Python for data analysis and automation.
• Strong foundation in statistical methods, including hypothesis testing, regression, and time series analysis.
• Experience working with AML data such as transaction monitoring alerts, customer risk scores, and case management systems.
• Ability to translate complex data into actionable insights for non-technical stakeholders.

Preferred Qualifications:

• Familiarity with AML systems (e.g., Actimize, SAS AML, Oracle FCCM).
• Understanding of BSA/AML regulatory requirements and model governance practices.
• Experience with scripting and automation tools (e.g., Jupyter, Airflow, Git).
• Exposure to model validation or audit processes in a regulated environment