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Aspire is a Series C fintech platform building a financial operating system for global founders and businesses. The company combines banking, software, and automation to help businesses manage finances across borders, backed by Y Combinator, Peak XV, and Lightspeed.
The Data Team at Aspire drives strategic decision-making across Product, Operations, and Finance. Within this team, the Fraud & AML Analytics function protects customer integrity and platform security by partnering with Risk, Compliance, and Transaction Screening teams to build detection systems and analytical frameworks.
As Assistant Analytics Manager – Fraud & AML, you will lead financial crime analytics initiatives, blending data science, BI engineering, and domain expertise. Key responsibilities include:
• Own fraud and AML metrics and reporting: Design and maintain KPIs covering fraud rates, false positive rates, AML alert volumes, SAR filing rates, and detection model performance.
• Build scalable data infrastructure and ML models: Develop data pipelines, dashboards, and machine learning models providing real-time visibility into fraud trends, transaction screening outcomes, and typology patterns.
• Drive detection analytics: Develop and refine rule-based and ML-assisted detection models for transaction fraud, account takeover, and AML typology identification.
• Support new control launches: Lead analytical setup for new fraud/AML controls and product risk features, ensuring metrics and monitoring are in place from day one.
• Enable self-serve analytics: Partner with Compliance and Operations teams to respond to ad-hoc investigation needs and build tooling that accelerates case review workflows.
• Streamline with automation: Automate recurring compliance and risk reporting processes to free analyst capacity for higher-value work.
• Collaborate cross-functionally: Work with Product, Risk, Operations, and Finance teams to translate regulatory and business requirements into analytical solutions.
• Track and optimize costs: Support cost tracking for fraud losses and AML program operations, generating management reports for senior stakeholders.
• Mine insights for impact: Identify emerging fraud typologies, money laundering patterns, and anomalies before they become material risks.
Required qualifications: Bachelor's degree in business, finance, economics, computer science, statistics, or related field. 4–8 years of experience in fraud analytics, AML/financial crime, risk data science, or related fintech/financial services roles. Hands-on experience with AML or fraud detection systems, including transaction monitoring rules, model validation, and SAR/STR reporting workflows.
Technical skills: Advanced SQL (complex queries, window functions, large-scale transactional datasets). Python proficiency (Pandas, NumPy, Scikit-learn) for data transformation, anomaly detection, and scenario modeling. Data modeling and metrics engineering experience. BI and dashboard design capabilities.
