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Strong Tier-1 brand, metro location, mid-level generalist analytics role, and broad SQL/Python skillset increase competition.
Core analytics skills are transferable, but AML/payments domain knowledge increases industry specificity.
Requires specific analytical tools and ~2 years experience, so moderate filtering on skills and experience.
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Perform data analytics to support Financial Crime Risk workstreams, focusing on Anti-Money Laundering (AML) in a regulatory environment.
Manage and analyze large volumes of structured and unstructured data to generate insights and optimize AML risk while enhancing customer experience.
Collaborate with Compliance, Technology, and PMO teams to define AML risk strategies and ensure data accuracy and quality in reporting and monitoring.
Preferred degree in quantitative fields such as Finance, Statistics, Economics, Mathematics, or Engineering.
Preferred minimum 2 years of relevant work experience in data analytics or related fields.
Strong experience with analytical tools including SQL, Hive, Python, R, and Excel.
Work Experience Required: Not explicitly mentioned in the JD; Preferred 2 years indicated.
Demonstrates strong analytical skills with the ability to solve unstructured problems using data in a fast-changing, results-driven environment.
Experience or understanding of AML, credit risk strategy, or payments industry is advantageous.
Able to communicate effectively across technical and non-technical stakeholders with attention to detail and commitment to accuracy.