CloudWalk - Operational Risk Specialist
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Requirements
• Strong knowledge of BACEN operational risk regulations (Res. BCB 265/2022, Res.4.557/2017, Circ.3.978/2020, and related standards). • Deep understanding of the payments ecosystem, including acquirer, sub-acquirer, and issuer operations. • Proven experience in fraud prevention and detection across the full fraud lifecycle — onboarding, monitoring, disputes, and recovery. • Expertise in developing and validating machine learning (ML) and AI models for fraud and risk management. • Programming and data analysis proficiency (Python, SQL, R) for scenario simulation, model validation, and automation. • Ability to design and deploy AI agents to automate risk management workflows. • Knowledge of IT and cybersecurity risk frameworks (NIST, COBIT, ISO 27001) and integration into operational risk practices. • Experience managing KRIs, RCSAs, and risk dashboards for management and regulatory reporting. • Analytical and critical thinker with the ability to translate data into actionable insights. • Hands-on and execution-oriented, capable of independently implementing and iterating solutions quickly. • Automation-driven mindset, always seeking ways to streamline processes using AI and technology. • Strong communication and storytelling, simplifying complex analyses for technical and non-technical audiences. • Collaborative team player, fostering strong partnerships across Data Science, Engineering, and Risk teams. • Attention to detail and accuracy, ensuring reliability in models, metrics, and reports. • Adaptable and proactive, thriving in a dynamic, data-intensive, and fast-growing fintech environment. • Ethical and transparent, ensuring compliance and integrity in all risk management activities.
Responsibilities
• Design, implement, and maintain the Operational Risk Management Framework in line with BACEN regulations. • Identify, assess, and monitor operational risk exposures, especially in fraud and IT-related processes. • Develop and validate AI/ML models for fraud detection, anomaly identification, and operational risk quantification. • Build and maintain AI agents to automate operational risk workflows — such as loss event tracking, KRI monitoring, and incident reporting. • Establish and monitor Key Risk Indicators (KRIs) and Risk and Control Self-Assessments (RCSAs) for ongoing risk tracking. • Collaborate with Data Science, Engineering, and Compliance teams to embed risk controls into product and process design. • Conduct scenario analyses and stress tests to evaluate operational resilience and inform management decisions. • Prepare clear and insightful risk reports and dashboards for senior management and regulatory audiences.
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