Lead Fraud Data Scientist

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

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  Develop and maintain fraud & credit risk scorecards (application and behavior) to support risk-based strategies across the customer lifecycl
• e.Leverage advanced analytics and machine learning to improve predictiveness and business outcomes from risk model
• s.Design and implement data-driven credit strategies that balance risk, profitability, and customer experienc
• e.Perform data extraction, preparation, and feature engineering using large structured datasets from internal and external source
• s.Apply statistical techniques including logistic regression, decision trees, and machine learning algorithms to build robust, interpretable model
• s.Conduct model performance evaluation, including KS, Gini, PSI, stability, lift analysis, and back testing to ensure model accuracy and consistenc
• y.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 strategie
• s.Provide analytical support in model calibration, cut-off analysis, stress testing, and scenario analysi
• s.Participate in internal and external model validation exercises, audits, and regulatory reviews by preparing detailed documentation and response
• s.Continuously enhance models by incorporating alternative data, new modeling techniques, and automation tool
• s.Lead and mentor a team of analysts, providing guidance, setting priorities, and ensuring high-quality delivery of fraud risk models and scorecard

s.
Qualificatio

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