Note: The job is a remote job and is open to candidates in USA. K2 Integrity is seeking a forensic analytics professional who is naturally curious and investigative. The successful candidate will work with large datasets to uncover hidden behavioral patterns and enhance the organization's financial crime monitoring capabilities.
Responsibilities
- Analyze transactions, accounts, customer profiles, alerts, and third-party data to identify suspicious patterns, anomalies, and emerging risks
- Write and optimize complex SQL queries and Python scripts to extract, manipulate, and analyze large financial datasets hands-on
- Design and implement financial crime detection models, scenarios, and rule sets tailored to AML typologies
- Conduct root cause analyses on financial crime incidents to improve detection accuracy and prevention strategies
- Build and maintain ETL pipelines to ingest, clean, and validate data from multiple sources
- Develop clear, compelling dashboards and reports in Power BI to support investigator decision-making and stakeholder reporting
- Apply AI/ML techniques including supervised and unsupervised models, anomaly detection, and NLP — to enhance detection efficiency and surface emerging risks
- Leverage graph analytics to map and analyze relationships between entities, accounts, and transactions
- Translate forensic data analyses into findings and recommendations that enable effective, timely investigations
- Mentor junior analysts and contribute to building overall team capability
Skills
- 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
- Experience conducting lookback analyses, typology development, threshold calibration, segmentation studies, and alert effectiveness reviews
- Experience leveraging SQL and Alteryx to perform forensic transaction analysis and identify previously unknown financial crime risks
- Familiarity with SAR narratives, AML investigations, regulatory expectations, and suspicious activity identification
- Experience evaluating transaction networks, customer relationships, and behavioral patterns to uncover previously unidentified financial crime risks
- Knowledge of statistical analysis, anomaly detection techniques, behavioral profiling, and risk-based segmentation methodologies
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