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.