K2 Integrity

Senior Data Analyst

United States · REMOTE · FREELANCE
Publiée le 30 juillet 2026 · Candidature traitée sur le site de l’entreprise
AML-AnalyticsFinancial-Crime-AnalyticsForensic-AnalyticsFraud-AnalyticsSenior-Data-AnalyticsSenior-Level-Data-AnalystSenior-Staff-Data-AnalystSenior-Analytics-AnalystSenior-Data-Analyst-JobsSenior-BI-and-Data-AnalystSenior-Data-Analyst-RolesSenior-BI-And-Analytics-Analyst

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 contractor position is a remote role in the USA. 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 Originally posted on Himalayas

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