Conduct forensic analytics for anti-money laundering and financial crime monitoring. Analyze large transactional datasets with SQL and Alteryx, identify suspicious behaviors and emerging typologies, develop and validate detection scenarios, calibrate thresholds, perform lookbacks and segmentation studies, evaluate transaction networks, and support AML investigations, SAR narratives, and regulatory responses.
We are seeking a forensic analytics professional who is naturally curious, investigative, and hypothesis-driven. The successful candidate will be comfortable working directly with datasets containing millions of records, using SQL and Alteryx to explore data and uncover hidden behavioral patterns. They should demonstrate a proven ability to move beyond predefined requirements and independently discover emerging AML typologies, develop defensible detection logic, and enhance the organization's financial crime monitoring capabilities. This
role is hybrid based out of the New York or Washington D.C. location.
Responsibilities:
Requirements:
Responsibilities:
- Design new AML monitoring scenarios or detection models from concept through implementation
- Conduct lookback analyses, typology development, threshold calibration, segmentation studies, and alert effectiveness reviews
- Leverage SQL and Alteryx to perform forensic transaction analysis and identify previously unknown financial crime risks
- Identify and evaluate transaction networks, customer relationships, and behavioral patterns to uncover previously unidentified financial crime risks
- Utilize statistical analysis, anomaly detection techniques, behavioral profiling, and risk-based segmentation methodologies
- Provide supporting data analytics and documentation for SAR narratives, AML investigations, and regulatory responses
Requirements:
- Advanced degree in a related field (e.g., Data Science, Statistics, Finance)
- 5+ years of experience working with large datasets containing millions of records to analyze large-scale transactional, customer, or financial datasets in support of AML, financial crimes, fraud, or investigative analytics initiatives
- Demonstrated ability to identify suspicious behaviors, emerging typologies, hidden relationships, and anomalous transaction patterns through exploratory data analysis
- Experience developing, enhancing, and validating AML detection strategies, scenarios, models, or monitoring rules based on forensic review of transactional activity
- Strong understanding of money laundering methodologies, including structuring, layering, funnel accounts, mule activity, third-party transfers, rapid movement of funds, high risk counterparties, and other financial crime typologies
- Proven ability to transform investigative findings into defensible detection logic, thresholds, and risk indicators
- Advanced SQL skills with the ability to independently query, extract, manipulate, and analyze large datasets to uncover suspicious activity patterns and support detection model development
- Strong Alteryx experience, including workflow design, data preparation, aggregation, segmentation, statistical analysis, and investigative data exploration across large transaction populations
- Ability to independently formulate hypotheses, test suspicious activity indicators, and develop evidence-based recommendations for detection enhancements
- Strong communication skills with the ability to articulate complex analytical findings
- Experience designing new AML monitoring scenarios or detection models from concept through implementation preferred
- Experience conducting lookback analyses, typology development, threshold calibration, segmentation studies, and alert effectiveness reviews preferred
- Experience leveraging SQL and Alteryx to perform forensic transaction analysis and identify previously unknown financial crime risks preferred
- Familiarity with SAR narratives, AML investigations, regulatory expectations, and suspicious activity identification preferred
- Experience evaluating transaction networks, customer relationships, and behavioral patterns to uncover previously unidentified financial crime risks preferred
- Knowledge of statistical analysis, anomaly detection techniques, behavioral profiling, and risk-based segmentation methodologies preferred
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