Lead Fraud Data Scientist

Job Description

About us : Network International is the largest Financial Technology company in Middle East and Africa. Payments is our core business where we provide services in more than 50 countries – UAE, Jordan, South Africa, Egypt are some of our key markets. Apart from payments, we provide services on Data and Insights, Lending, Insurance, Risk Solutions, etc. Our core customers are businesses at every scale and segment, though recently we are growing in direct to consumer card segment as well.

This role of a Lead Fraud Data Scientist will be responsible for the end-to-end development of credit risk scorecards. This is a hands-on role, leading a team as well as requiring strong technical and statistical modeling expertise, with a focus on building predictive models using advanced data analytics tools. The role involves working with large-scale datasets, applying statistical techniques, and deploying models that support credit decisioning and portfolio management strategies.

The ideal candidate will be passionate about modeling, experienced in handling real-world noisy data, and able to translate data-driven insights into actionable credit risk tools.

Responsibilities

• Develop and maintain fraud & credit risk scorecards (application and behavior) to support risk-based strategies across the customer lifecycle.
• Leverage advanced analytics and machine learning to improve predictiveness and business outcomes from risk models.
• Design and implement data-driven credit strategies that balance risk, profitability, and customer experience.
• Perform data extraction, preparation, and feature engineering using large structured datasets from internal and external sources.
• Apply statistical techniques including logistic regression, decision trees, and machine learning algorithms to build robust, interpretable models.
• Conduct model performance evaluation, including KS, Gini, PSI, stability, lift analysis, and backtesting to ensure model accuracy and consistency.
• Document the entire model development lifecycle (MDLC) in compliance with internal model governance and regulatory requirements (e.g., IFRS9, Basel).
• Collaborate with risk policy, credit underwriting, and collections teams to ensure models are effectively translated into business strategies.
• Provide analytical support in model calibration, cut-off analysis, stress testing, and scenario analysis.
• Participate in internal and external model validation exercises, audits, and regulatory reviews by preparing detailed documentation and responses.
• Continuously enhance models by incorporating alternative data, new modeling techniques, and automation tools.
• Lead and mentor a team of analysts, providing guidance, setting priorities, and ensuring high-quality delivery of fraud risk models and scorecards.
  
  

• Bachelors or Master’s degree in Statistics, Mathematics, Computer Science, Data Science, Economics, or a related quantitative field.
• 9+ years of hands-on experience in credit risk modeling, with a strong focus on scorecard development in banking or consumer lending.
• Strong programming skills in SAS (preferred), Python, R, and SQL for model building, data preparation, and validation.
• Deep understanding of statistical modeling techniques such as logistic regression, WOE/IV transformation, scorecard binning, and variable selection.
• Experience in developing models for retail lending products such as personal loans, credit cards, auto loans, or mortgages.
• Familiarity with model governance processes, documentation standards, and regulatory requirements (e.g., Basel, IFRS9, CB guidelines).
• Ability to work independently on data sourcing, feature creation, model training, and performance evaluation.
• Experience working with data visualization and reporting tools (e.g., Power BI, Tableau) is a plus.
• Strong communication and documentation skills to explain model logic to stakeholders, reviewers, and non-technical teams.