Senior Data Analyst

• 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

Originally posted on Himalayas