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Tier-1 bank, metro location, mid-level generalist title and broad technical requirements drive high competition.
Core data skills are transferable, but specialized financial crime and AML expertise increases domain specificity.
Minimum experience, mandatory fraud domain knowledge and technical skills create moderate screening rigor.
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Use advanced analytics and statistical methods to identify and prevent fraudulent activity within the customer portfolio.
Gather, organize, and analyze data from multiple sources, creating reports, dashboards, and visualizations to communicate insights to stakeholders.
Ensure compliance with model, risk, and responsible AI standards while aligning with risk governance, regulatory processes, and technology teams on analytics platforms and data feeds.
Bachelor's degree in Computer Science, Statistics, Mathematics, or related quantitative field.
Minimum 3 years of relevant experience in data science, analytics, or fraud risk management.
Proficient in Python and SQL; experience with visualization tools like Tableau or PowerBI.
Understanding of financial crime (fraud) risk and analytical/statistical methods; knowledge of ETL pipelines and database querying.
Experienced in financial crime and fraud risk analytics within banking or related sectors.
Skilled in collaborating with senior leadership, governance, compliance, technology, and operations teams.
Capable of independently driving analytics projects that align with regulatory and risk governance requirements.