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KPMG US

Senior Associate, Financial Crimes, Data Analytics

KPMG US, Tallahassee, Florida, us, 32318

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Senior Associate, Financial Crimes, Data Analytics

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KPMG US . KPMG Advisory practice is currently our fastest growing practice. We are seeing tremendous client demand, and looking forward we do not anticipate that slowing down. In this ever-changing market environment, our professionals must be adaptable and thrive in a collaborative, team-driven culture. At KPMG, our people are our number one priority. With a wealth of learning and career development opportunities, a world-class training facility and leading market tools, we make sure our people continue to grow both professionally and personally. If you\'re looking for a firm with a strong team connection where you can be your whole self, have an impact, advance your skills, deepen your experiences, and have the flexibility and access to constantly find new areas of inspiration and expand your capabilities, then consider a career in Advisory. KPMG is currently seeking a

Senior Associate, Financial Crimes, Data Analytics

to join our Advisory Services practice. Responsibilities

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 Perform analysis to carry out BSA/AML risk assessments, model valuations and audits related to financial crimes Contribute to the design and implementation of data quality, governance, and model risk management frameworks Qualifications

Minimum three years of recent experience performing 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, computer science, statistics, math or related quantitative field of study or Masters of Business Administration (MBA) from an accredited college or university Proficient in programming languages such as SQL, Python, SAS 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 Excellent communication and report writing skills 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. KPMG LLP will not sponsor applicants for U.S. work visa status for this opportunity KPMG LLP and its affiliates and subsidiaries (“KPMG”) complies with all local/state regulations regarding displaying salary ranges. If required, ranges are determined based on factors such as skills, responsibilities, experience, degrees, certifications, and market considerations. Our Total Rewards package includes a variety of medical and dental plans, vision coverage, disability and life insurance, 401(k) plans, and a suite of well-being benefits. Details about benefits are available on the KPMG US Careers site at Benefits & How We Work. KPMG is an equal opportunity employer. We recruit on a rolling basis. All qualified applicants will be considered for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, citizenship status, disability, protected veteran status, or any other category protected by applicable laws. Los Angeles County applicants: material job duties and Fair Chance Act considerations apply as described on the postings. Job details

Seniority level: Mid-Senior level Employment type: Full-time Job function: General Business

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