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Strong employer brand and metro locations increase competition, but niche quant risk skill reduces candidate pool.
Strong financial services and regulatory focus limits transferability across industries.
Explicit 3-7 years, domain expertise in regulatory risk, ML, Python/SAS make filters stringent.
Lead end-to-end delivery of quantitative risk model development, validation, and reviews for AML, transaction monitoring, and risk scoring frameworks.
Design and implement data pipelines and analytical solutions using Python, SAS, and machine learning techniques to support model calibration, optimisation, and reporting.
Manage client relationships and translate complex model insights into actionable recommendations for senior stakeholders while overseeing junior team members' work.
3+ years of experience in financial institutions, regulatory bodies, or consulting firms focused on risk, compliance, and governance.
Strong experience with regulatory compliance, GRC frameworks, and non-financial risk; knowledge of UAE regulatory frameworks mandatory.
Proficiency in Python, SAS, scikit-learn, and statsmodels for data analysis and model development.
Work Experience Required: 3+ years in relevant domains as specified in the JD.
Experienced in managing quantitative risk modelling projects end-to-end, including technical and stakeholder engagement aspects.
Comfortable operating in regulated financial services environments with strong understanding of compliance and governance frameworks, preferably with UAE regulatory experience.
Able to bridge technical quantitative outputs and business strategy to communicate effectively with senior stakeholders and lead junior analysts.