• Import, structure, and validate data from multiple formats; perform data quality and sanity checks.
• Conduct exploratory analysis, statistical testing, segmentation, and variable assessment.
• Develop, validate, and monitor statistical and machine learning models using approved CRIF methodologies.
• Assess model performance and stability using relevant measures such as GINI, KS, AUC, IV, PSI, and CSI.
• Translate analytical findings into practical business insights and recommendations.
• Automate recurring data preparation, analysis, monitoring, and reporting activities.
• Prepare clear technical documentation, model reports, implementation specifications, and presentations.
• Present results to internal and client stakeholders, explaining technical findings to non-specialist audiences.
• Support model implementation, testing, knowledge transfer, and post-deployment monitoring.
• Manage assigned project activities independently and deliver accurate outputs within agreed timelines.
Knowledge / Skills:
• Strong analytical, statistical, and problem-solving skills with high attention to detail.
• Sound knowledge of regression, segmentation methods, decision trees, random forests, and other predictive techniques.
• Proficiency in at least one analytical language or platform: Python, SAS, R, or PySpark.
• Good working knowledge of SQL, Excel, PowerPoint, and Word.
• Ability to interpret model results and communicate them clearly to business and technical stakeholders.
• Strong written and verbal communication, interpersonal, time-management, and multitasking skills.
• Ability to work independently, learn quickly, and meet established deadlines.
Preferred Qualifications:
• Bachelor's or Master's degree in Statistics, Mathematics, Economics, Operations Research, Engineering, Data Science, Computer Science, or a related quantitative field.
• Relevant hands-on experience in data analytics, predictive modeling, or data science projects.
• Experience in banking, credit risk, collections, fraud, telecom, insurance, or related sectors is preferred.
• Fluency in English and Arabic.
