Ebury
FinTech
HeadofModelRisk&DataQuality
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“Head of Model Risk & Data Quality at Ebury. Skills: Model Risk Management Framework Development, Data Quality Improvement, Regulatory Compliance, Risk Assessment. Develop and maintain a well-designed model risk management framework. Guiding and advising teams on model development, validation, and implementation processes”
Industry & Context.
Understand complex mathematical models and statistical analyses; Identify potential risks and inefficiencies; Think strategically about how Ebury’s business is exposed to model risks and what preventative measures can be taken
What They're Looking For.
Must Have
Background in quantitative fields such as statistics, mathematics, or quantitative finance, Experience of working with various types of risk, including operational, financial, and market risk, Good understanding of industry regulations and standards related to risk management
Nice to Have
Certified Risk Manager: Certifications like FRM (Financial Risk Manager), PRM (Professional Risk Manager), or CRM (Certified Risk Manager) could be beneficial.
What You'll Do.
Develop and maintain a well-designed model risk management framework
Guiding and advising teams on model development
and implementation processes
Reviewing and evaluating validations of models
Monitoring model performance over time
Providing reporting and intelligence concerning our models to Risk Committees and Ebury Executives
Ensuring adequate documentation for all model development and validation activities
Keeping abreast with changing regulations pertaining to model risk and ensuring compliance
Providing training and raising awareness on model risk issues
Leading efforts to improve the data quality and monitoring of data feeding into each model
How You'll Work.
Team & Collaboration
Guiding and advising teams in different parts of our organisation including Finance, Operations, Analytics, Decision Science, Sales & Marketing; Liaising with the Data Teams
Communication Scope
Communicate complex concepts and risk assessments to stakeholders and top-level management
Full Job Description
Ebury helps ambitious businesses unlock global growth, and we take the same approach with our people. We encourage innovation and movement, collaboration and problem-solving, and foster an environment where everyone can feel they belong, are valued, supported and empowered to succeed. If you’re a collaborator who wants to help transform how businesses operate globally, get in touch - we’d love to discuss how Ebury can accelerate your career so you can shape the future. Head of Model Risk Management & Data Quality Ebury Madrid Office - Hybrid: 4 days in the office, 1 day working from home per week What is Model Risk Management? In our context, models refer to the algorithms and analytical tools we use to make informed financial decisions. These models play a crucial role at Ebury in helping us serve and protect our clients effectively and meet our regulatory obligations. However, there is always a level of inherent risk associated with the use of these models. Model risk management involves identifying, assessing, and mitigating these risks. Why Do We Need a Model Risk Management at Ebury? As we deliver solutions to our clients we understand the importance of accuracy, reliability, and trust in our operations. Our clients rely on us to provide them with the best possible financial solutions, and our models play a significant role in ensuring we meet their expectations. The Model Risk Management Programme is designed to address potential risks associated with the use of these models and provide a structured approach to managing and mitigating them. What you will do: Develop and maintain a well-designed model risk management framework that meets our fast-paced and dynamic company’s needs and regulatory requirements. Guiding and advising teams in different parts of our organisation including Finance, Operations, Analytics, Decision Science, Sales & Marketing on model development, validation, and implementation processes. Reviewing and evaluating validations of models in
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