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