Fraud Risk Manager

As the AML and Fraud Risk Manager, you will play a pivotal role in designing Skydo's AI-driven risk intelligence architecture across onboarding, transaction monitoring, fraud detection, chargebacks, and collections. This is a mid-senior-level role that combines risk strategy, data science, and compliance expertise. The ideal candidate will bring a deep understanding of AML/CFT risk, fraud typologies, model development, and data analytics in financial services or payments. You will work closely with the heads of Risk and Compliance, Engineering, and Product teams to design frameworks, predictive models, and automated decisioning engines that reduce financial crime exposure while enabling business growth.

The core responsibilities for the job include the following:

Merchant Fraud Risk Strategy:

• Define the end-to-end merchant fraud risk strategy across onboarding, payment authorization, settlements, and refunds.
• Develop risk segmentation models and merchant-level risk bands (low/medium/high) using behavioral and transactional insights.
• Establish frameworks for synthetic merchant detection, mule network identification, money laundering, and misuse of legitimate accounts.
• Continuously refine Skydo's risk appetite framework for merchant fraud exposure.

Data Science and Predictive Modeling:

• Partner with Data Science to build ML models for: Merchant onboarding risk scoring and early fraud detection.
• Predictive transaction risk models using network graph analysis and behavioral clustering.
• Fraud typology prediction covering first-party, third-party, card-not-present (CNP), refund abuse, ATO (Account Takeover), and synthetic merchant fraud.
• Chargeback and refund abuse prediction.
• Design feature libraries (velocity checks, device fingerprinting, anomaly detection).
• Lead model validation, back-testing, and production rollout with measurable impact metrics (TPR, FPR, recall).

Fraud Prevention Systems and Implementation:

• Translate model outputs into real-time decisioning rules and integrate with Skydo's fraud detection engine.
• Partner with Product and Engineering to embed adaptive fraud controls in merchant onboarding and transaction flows.
• Build mechanisms to identify, classify, and monitor fraud patterns across the lifecycle
• Define feedback loops from case outcomes to continuously train and improve model performance.
• Collaborate with external data vendors and API partners to enhance risk visibility.

Analytics, Reporting, and Governance:

• Build fraud performance dashboards, MIS, and early warning indicators.
• Analyze fraud losses, false positives, and merchant lifecycle performance to refine strategy.
• Present insights and recommendations to leadership and board-level risk committees.
• Own the risk calibration framework, ensuring accurate alignment between model predictions and business outcomes.

Cross-functional Leadership and Collaboration:

• Partner with Compliance, Finance, and Product to align fraud and AML controls with regulatory and operational policies.
• Provide strategic inputs during product launches, bank partner discussions, and systems audits.
• Mentor analysts and cross-functional teams on fraud analytics, typologies, and detection methodologies.
• Play an active role in scaling analytical capabilities, vendor integrations, and automation projects.

Requirements:

• Proven ability to design, implement, and optimize analytical risk models.
• Prior experience with card or payments ecosystems (e. g., chargebacks, merchant fraud).
• Exposure to risk modeling tools like R, Python, SAS and large-scale data pipelines.
• Knowledge of statistical modeling, machine learning, and predictive analytics techniques.
• Deep understanding of AML/CFT frameworks, FATF standards, and fraud typologies.
• Strong problem-solving, stakeholder management, and communication skills.
• Quantitative academic background (Engineering, Statistics, Economics, or Data Science) from a Tier-1 institute.
• 3+ years of experience in fraud risk, AML, or data-driven risk management in fintech, banking, or payments.
• Analytical thinker with high curiosity and data orientation.
• Balances business enablement with risk control rigor.
• Thrives in fast-paced, startup environments with extreme ownership.