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High due to fintech unicorn brand, mid-level ML title, metro location, and generalist ML requirements.
High because transaction monitoring, sanctions screening, and payments compliance experience are strongly preferred.
High due to explicit 3–5 years, production ML experience, and financial‑crime/domain expertise requirements.
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Develop, deploy, and monitor AI/ML models to detect financial crime risks including fraud and credit risk with compliance to data privacy and audit requirements.
Enhance and optimize rules and models for anti-money laundering and financial crime detection, including rules management and performance monitoring.
Collaborate across teams to ensure data quality, regulatory compliance, and translate technical findings into business insights for senior management.
Degree in Statistics, Mathematics, Data Science, Economics, or related quantitative field.
3-5 years of experience in data science, advanced analytics, or machine-learning roles.
Experience with financial services, fintech, payments, or consulting strongly preferred, especially relating to financial crime systems (e.g., transaction monitoring, sanctions screening).
Experience building and deploying ML models in production environments and supporting compliance operations or model governance.
Experienced data scientist with specific exposure to financial crime risk detection or compliance systems in financial services or fintech.
Capable of managing end-to-end AI/ML model lifecycle including model explainability, auditability, and compliance with regulatory standards.
Skillful at working cross-functionally with legal, product, and operations teams to drive data-driven risk solutions and improve compliance frameworks.