Senior Data Science Expert

About Commercial Bank Of Qatar Commercial Bank, founded in 1975 and headquartered in Doha, plays a vital role in Qatar’s economic development by offering a range of personal, business, government, international and investment services. We believe in empowering our employees, providing them with opportunities for growth and professional development. By Joining us, you’ll be part of a workplace culture that fosters innovation, supports work-life balance, and encourages you to reach your full potential. Join us in shaping the future of banking! Job Summary • Responsible for developing advanced machine learning models for banking use cases including pricing optimization, customer propensity modelling, and recommendation systems. • This senior role requires deep expertise in statistical modelling and machine learning combined with substantial banking and financial services domain knowledge . • The position focuses on translating complex business problems in areas such as Risk, Finance, Retail Banking, and Wholesale Banking into actionable ML solutions. • Will leverage their understanding of banking products, regulatory requirements, and financial metrics to build models that drive measurable business value. • The role requires a balance of domain expertise and technical capability, with sufficient programming skills in Python and SQL to develop and deliver working prototypes that can be transitioned to production. Key Accountabilities • ML Model Development • Design and develop machine learning models for pricing optimization, including dynamic pricing, rate optimization, and fee structures. • Build propensity models for customer behavior prediction including churn, cross-sell, upsell, and product adoption. • Develop recommendation systems for personalized product offerings, next-best-action, and customer engagement. • Banking Domain Application • Apply deep banking domain knowledge to frame business problems as ML solutions with measurable outcomes. • Partner with Risk, Finance, and business units to identify high-value modeling opportunities. • Ensure models incorporate relevant regulatory requirements, risk considerations, and business constraints. • Analysis & Insights • Conduct exploratory data analysis to identify patterns, relationships, and modeling opportunities in banking data. • Translate model outputs into actionable business recommendations and insights. • Develop model performance metrics aligned with business KPIs and financial outcomes. • Create data visualizations and reports for stakeholder communication. • Prototyping & Delivery • Develop working prototypes in Python demonstrating model functionality and business value. • Create clear documentation of model methodology, assumptions, limitations, and use cases. • Collaborate with ML Engineers and AI Engineers to transition prototypes to production systems. • Stakeholder Collaboration • Partner with business stakeholders to understand requirements and validate model outputs. • Present model results, methodology, and recommendations to senior management. • Contribute to model governance, validation, and documentation requirements. • Ensure compliance with data policies, ethical standards, and regulatory requirements. Key Competencies • Machine Learning & Statistics • Expert knowledge of supervised and unsupervised learning techniques for classification, regression, and clustering. • Deep experience with pricing models, propensity modeling, and recommendation systems. • Strong foundation in statistical analysis, hypothesis testing, and experimental design. • Familiarity with deep learning frameworks (TensorFlow, PyTorch) for advanced use cases. • Banking Domain Expertise • Comprehensive understanding of banking products (Retail or Corporate business), services, and customer lifecycle. • Knowledge of Risk functions including credit risk, market risk, and operational risk frameworks. • Understanding of Finance functions including P&L drivers, cost allocation, and profitability analysis. • Familiarity with regulatory requirements affecting model development (IFRS 9, Basel, etc.). • Technical Skills • Python for data analysis and model development (pandas, scikit-learn, XGBoost, etc.). • SQL – Advanced user (Stored Procedures, Window functions, Temp Tables, Recursive Queries). • Experience with data visualization and reporting tools. • Familiarity with Git (GitHub/GitLab) for version control. • Basic understanding of Spark for large-scale data processing. • Awareness of MLOps practices and model deployment concepts (MLflow, TFX). • Communication & Collaboration • Ability to translate complex analytical concepts into business language for non-technical stakeholders. • Strong presentation skills for executive-level communication. • Experience working with cross-functional teams across business and technology. • Agile methodologies (Kanban, Scrum) experience. Qualifications & Experience • Master's degree in Finance, Economics, Statistics, Mathematics, or quantitative field strongly preferred. • 8+ years of experience in data science or quantitative analysis roles. • Minimum 5 years of experience in banking or financial services industry is mandatory. • Proven track record of delivering ML models in pricing, propensity, or recommendation domains. • Background in Risk, Finance, or quantitative functions within banking preferred. • Experience with model validation, governance, and regulatory requirements in financial services. • Professional certifications in Risk (FRM, PRM) or Finance (CFA) are a plus. Why Commercial Bank? • Best Digital Bank in the Middle East 2024 by World Finance and Best Mobile Banking App in the Middle East 2024 by Global Finance. • An Innovation-Driven, Digital-First Environment where employees work with the latest tools and technologies to redefine banking • Opportunities for Global Partnerships & International Exposure, connecting employees with global networks and perspectives. • A focus on Employee Well-being & Work-Life Balance, ensuring a healthy and supportive environment for all team members • Competitive Compensation & Benefits that ensure our employees are rewarded for their dedication and performance • A strong Commitment to Diversity, Equity & Inclusion, fostering a culture that values every individual’s unique perspective. At Commercial Bank, we don’t just offer careers, We shape futures by pioneering digital transformation in Qatar’s banking sector, blending digital-first approach to redefine banking through innovative solutions. Disclaimer We appreciate your interest in joining CBQ! Please note that only selected candidates will be contacted for further steps in the hiring process. This job posting is for informational purposes only, and CBQ reserves the right to modify, withdraw, or close it at any time without notice.

ML Model Development • Design and develop machine learning models for pricing optimization, including dynamic pricing, rate optimization, and fee structures. • Build propensity models for customer behavior prediction including churn, cross-sell, upsell, and product adoption. • Develop recommendation systems for personalized product offerings, next-best-action, and customer engagement. Banking Domain Application • Apply deep banking domain knowledge to frame business problems as ML solutions with measurable outcomes. • Partner with Risk, Finance, and business units to identify high-value modeling opportunities. • Ensure models incorporate relevant regulatory requirements, risk considerations, and business constraints. Analysis & Insights • Conduct exploratory data analysis to identify patterns, relationships, and modeling opportunities in banking data. • Translate model outputs into actionable business recommendations and insights. • Develop model performance metrics aligned with business KPIs and financial outcomes. • Create data visualizations and reports for stakeholder communication. Prototyping & Delivery • Develop working prototypes in Python demonstrating model functionality and business value. • Create clear documentation of model methodology, assumptions, limitations, and use cases. • Collaborate with ML Engineers and AI Engineers to transition prototypes to production systems. Stakeholder Collaboration • Partner with business stakeholders to understand requirements and validate model outputs. • Present model results, methodology, and recommendations to senior management. • Contribute to model governance, validation, and documentation requirements. • Ensure compliance with data policies, ethical standards, and regulatory requirements.