Manager Quantitative Analytics Financial Services Risk Management (FSRM)

Manager Quantitative Analytics Financial Services Risk Management (FSRM)

Location: TBC

Languages: English (Mandatory) Arabic (Preferred)

Experience: 69 years

Industry Focus: Banking Insurance and Financial Institutions

Job Summary

We are looking for a Manager to join EYs Financial Services Risk Management (FSRM) team in Riyadh.

This role is designed for professionals with a strong background in mathematics statistics data science and programming who are passionate about applying these skills to risk management credit analytics and fraud detection.

You will work alongside experienced risk and regulatory professionals to build advanced analytical solutions from machine-learning models for early warning systems to AI-driven fraud analytics while progressively developing domain expertise across credit operational and market risk.

The position is ideal for candidates who see themselves evolving into quantitative risk leaders at the intersection of AI finance and regulation.

Key Responsibilities

Quantitative Modelling & Advanced Analytics

• Design and implement predictive and classification models using machine learning and statistical methods.
• Apply advanced algorithms (regression decision trees ensemble methods clustering NLP anomaly detection) to solve client problems in credit scoring fraud detection and portfolio analytics.
• Build and automate data pipelines for model training testing and validation using Python R SQL or equivalent.
• Translate analytical results into business insights and communicate findings to non-technical audiences.

Risk Analytics and Domain Application

• Support development and validation of credit risk models (PD LGD EAD) and IFRS 9 impairment frameworks.
• Assist in implementing model monitoring calibration and governance processes.
• Participate in projects focused on fraud risk analytics transaction monitoring and behavioral modelling.
• Contribute to data-driven operational and market risk initiatives including stress testing and scenario modelling.

Client Delivery and Collaboration

• Work with senior managers and directors to deliver analytics-driven risk solutions for leading banks and regulators.
• Collaborate across EY teams (Risk Technology and Data) to develop innovative digital risk offerings.
• Document methodologies validation results and technical processes in line with EY quality standards.

Innovation & Research

• Experiment with AI/ML techniques such as deep learning graph analytics and generative AI in risk-modelling contexts.
• Support development of EYs proprietary risk analytics tools and accelerators.
• Stay abreast of emerging trends in AI ethics explainability and regulatory expectations for model governance.

Skills and Attributes for Success

• Strong quantitative foundation in statistics econometrics or applied mathematics.
• Proficiency in Python (scikit-learn pandas TensorFlow PyTorch) R or SAS.
• Strong understanding of data structures feature engineering and model evaluation metrics (ROC AUC KS precision/recall etc.).
• Excellent analytical reasoning and ability to convert complex data problems into clear solutions.
• Curiosity to learn financial-risk domains (Basel IFRS 9 ICAAP) and apply technical models to real-world risk problems.
• Effective communicator who can bridge technical and business audiences.

To Qualify for the Role You Must Have

• Masters degree (or higher) in Statistics Mathematics Data Science Computer Science Quantitative Finance or a related discipline.
• 69 years of relevant experience in data science analytics or quantitative risk modelling.
• Hands-on programming and model-building experience (not just model use).
• Exposure to banking or financial-services data.

• Prior experience in consulting or financial institutions analytics teams.
• Familiarity with Basel III/IV IFRS 9 or credit/fraud risk frameworks.
• Experience using cloud-based analytics environments (Azure ML AWS SageMaker GCP AI Platform).
• Certification such as FRM CFA or a recognized data-science credential.

An intellectually curious technically strong professional who is excited to bridge data science and financial risk someone who codes confidently questions assumptions and wants to learn how quantitative innovation shapes regulatory and business decisions in the financial sector.