Senior Data Analyst at K2 Integrity
United States
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. <ul id="isPasted"><li>Advanced degree in a related field (e.g., Data Science, Statistics, Finance)</li><li>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</li><li>Demonstrated ability to identify suspicious behaviors, emerging typologies, hidden relationships, and anomalous transaction patterns through exploratory data analysis</li><li>Experience developing, enhancing, and validating AML detection strategies, scenarios, models, or monitoring rules based on forensic review of transactional activity</li><li>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</li><li>Proven ability to transform investigative findings into defensible detection logic, thresholds, and risk indicators.</li><li>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</li><li>Strong Alteryx experience, including workflow design, data preparation, aggregation, segmentation, statistical analysis, and investigative data exploration across large transaction populations</li><li>Ability to independently formulate hypotheses, test suspicious activity indicators, and develop evidence-based recommendations for detection enhancements</li><li>Strong communication skills with the ability to articulate complex analytical findings</li><li id="isPasted">Experience designing new AML monitoring scenarios or detection models from concept through implementation preferred</li><li>Experience conducting lookback analyses, typology development, threshold calibration, segmentation studies, and alert effectiveness reviews preferred</li><li>Experience leveraging SQL and Alteryx to perform forensic transaction analysis and identify previously unknown financial crime risks preferred</li><li>Familiarity with SAR narratives, AML investigations, regulatory expectations, and suspicious activity identification preferred</li><li>Experience evaluating transaction networks, customer relationships, and behavioral patterns to uncover previously unidentified financial crime risks preferred</li><li>Knowledge of statistical analysis, anomaly detection techniques, behavioral profiling, and risk-based segmentation methodologies preferred</li></ul> <ul id="isPasted"><li>Analyze transactions, accounts, customer profiles, alerts, and third-party data to identify suspicious patterns, anomalies, and emerging risks</li><li>Write and optimize complex SQL queries and Python scripts to extract, manipulate, and analyze large financial datasets hands-on</li><li>Design and implement financial crime detection models, scenarios, and rule sets tailored to AML typologies</li><li>Conduct root cause analyses on financial crime incidents to improve detection accuracy and prevention strategies</li><li>Build and maintain ETL pipelines to ingest, clean, and validate data from multiple sources</li><li>Develop clear, compelling dashboards and reports in Power BI to support investigator decision-making and stakeholder reporting</li><li>Apply AI/ML techniques including supervised and unsupervised models, anomaly detection, and NLP — to enhance detection efficiency and surface emerging risks</li><li>Leverage graph analytics to map and analyze relationships between entities, accounts, and transactions</li><li>Translate forensic data analyses into findings and recommendations that enable effective, timely investigations</li><li>Mentor junior analysts and contribute to building overall team capability</li></ul>
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