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Senior Data Analyst

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Remote

Senior Data Analyst

K2 Integrity

Senior Data Analyst

Skills

SQL QueriesData ScienceAlteryxData Analysis

20 days ago

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Important information


Contract type:

Freelance / Contract

Location:

United States

Work mode:

Remote

Published on:

7 October 2026

What they need


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

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