Expert Data Scientists on Credit Scoring Job Tezza Business Solutions

• Lead and implement the end-to-end development of credit scorecards, from problem definition to deployment
• Conduct data exploration, cleaning, transformation, and feature engineering.
• Apply statistical modeling and machine learning techniques (i.e, logistic regression, decision trees, ensemble methods, neural networks) for scorecard development.
• Perform model validation, back-testing, and monitoring to ensure stability, accuracy, and fairness.
• Collaborate with risk, credit, and technology teams to integrate models into production systems.
• Document methodology, assumptions, and outcomes for monitoring/governance.
• Train and mentor internal staff on scorecard use, monitoring, and governance

• Fully developed credit scorecard models for retail, SME, or corporate lending (as applicable).
• Detailed model documentation (methodology, validation results, monitoring framework).
• Deployment-ready model integrated into production environment.
• Model performance tracking framework (e.g., KS, Gini, ROC AUC, stability index, population stability).
• Knowledge transfer to internal data science/risk teams

• Stakeholder Management: key stakeholders that the position holder will need to liaise/work with to be successful in this role.
• Internal: Data Engineers, IT, Credit, Operations, Customer Experience, Business Performance function, Business Support Services

• Bachelor's degree in Statistics, Mathematics, Computer Science, Machine Learning, Economics, or any other related quantitative field. Working experience of the equivalent is also acceptable for this position.
• Masters in Data Science/Math or any Quantitative discipline is an added advantage
• Professional:
• Big Data or Data Science certification from recognized institutions
• Cloud and/or AI certifications from recognized institutions

• Must have a minimum of 6 years with proven experience in credit risk modeling and scorecard development in a banking or financial services environment.
• Hands-on experience with scorecards I.e. application, behavioral and collection scorecards
• Strong knowledge of statistical and machine learning methods (logistic regression, decision trees, gradient boosting, neural networks).
• Proficiency in Python, SQL or SAS, R.
• Prior experience in model governance and validation
• Demonstrated ability to work independently and deliver within tight timelines.

• Communication Skills: The Data Scientist will be required to explain advanced statistical content to senior data scientists and relevant stakeholders.
• Have the ability to translate and tailor this technical content into applicable business material with clear recommendations and insights relevant to the audience at hand.
• Interpersonal Skills: ability to work effectively in a group/collaborative setting, be result oriented, be highly analytical, be a strategic and creative thinker, have superior organizational skills, have a strong attention to details, have an ability to work on multiple projects and meet tight deadlines, have exceptional problem-solving skills, and remain calm and composed in times of stress and uncertainty.
• People Skills: people person, demonstrating an ability to create and maintain strong, meaningful, and lasting relationships with others. He must also be a confident but friendly and approachable individual who will inspire confidence and trust in his seniors and key stakeholders, leading them to give credit to his insights and judgments.

Corporate Staffing