Senior Data Analyst

• 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. Show more