Senior Associate, Financial Crimes Data Analytics

Overview

KPMG Advisory practice is currently our fastest growing practice. We are seeing tremendous client demand, and we expect that to continue. At KPMG, our people are our number one priority with learning and career development opportunities, world-class training, and leading market tools to support growth both professionally and personally. If you are looking for a firm with strong team connection where you can be your whole self, have an impact, advance your skills, deepen your experiences, and have the flexibility to explore new areas of inspiration, consider a career in Advisory.

KPMG is currently seeking a Senior Associate, Financial Crimes, Quantitative Analyst to join our Advisory Services practice.

Note: This description reflects typical responsibilities for this role and is not an offer of employment. Salary ranges and benefits are described separately where applicable.

• Develop, calibrate, and validate statistical, machine learning, and artificial intelligence models used to detect and prevent financial crimes, including fraud, money laundering, and sanctions violations
• Assess and monitor the performance of quantitative models through back testing, benchmarking, and statistical analysis
• Analyze large and complex datasets to uncover patterns, anomalies, and trends indicative of illicit financial activities
• Provide quantitative support for risk assessments, regulatory reporting, and periodic audits related to financial crimes
• Contribute to the design and implementation of data quality, governance, and model risk management frameworks

• Minimum three years of recent experience specialized in quantitative analysis for financial crime detection, leveraging advanced statistical methods and data modeling techniques
• Bachelor's degree from an accredited college or university is required with preference given to data science, statistics, math or related quantitative field of study; MBA from an accredited college or university is preferred
• Proficient in programming languages such as Python and R to build, validate, and implement models for transaction monitoring, anomaly detection, and fraud analytics; experienced with data visualization tools such as Tableau or Power BI
• Skilled in risk assessment, data-driven decision making, and extracting actionable insights from complex datasets
• Ability to analyze complex datasets and communicate actionable insights to diverse audiences
• Experience applying machine learning or artificial intelligence techniques within financial crime risk management
• Must be authorized to work in the U.S. without the need for employment-based visa sponsorship now or in the future

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