Consultant

The Credit Risk Consulting team partners with leading financial institutions across the US and Africa to solve complex lending, portfolio management, and risk strategy challenges through advanced analytics and data-driven decisioning. Role Overview and Core Responsibilities:

• Partner with banks, lenders, and financial institutions to identify, structure, and solve complex credit risk business problems.
• Develop and enhance application, behavioral, acquisition, collections, and account management scorecards that drive lending and portfolio decisions.
• Design and deploy predictive risk models using statistical techniques and Machine Learning (ML) methodologies.
• Utilize Python, SQL, and advanced analytics techniques to analyze large customer and portfolio datasets and generate actionable insights.
• Translate business requirements into analytical solutions and communicate recommendations to senior client stakeholders.
• Conduct model performance monitoring, validation, back-testing, and champion-challenger analysis to ensure model effectiveness.
• Collaborate with global consulting, analytics, and product teams to deliver high-impact client engagements across US and Africa regions.
• Support risk strategy development across customer lifecycle stages, including acquisition, portfolio management, collections, and fraud mitigation.
• Present findings, recommendations, and strategic insights to business leaders and client executives.
• Contribute to thought leadership, innovation initiatives, and development of next-generation risk analytics capabilities.

Required Knowledge and Experiences:

• 4+ years of experience in Credit Risk Analytics, Risk Consulting, Banking Analytics, or Financial Services Analytics, with demonstrated experience applying analytical solutions to business problems.
• Strong understanding of retail lending portfolios, credit risk frameworks, scorecard development, and customer lifecycle risk management.
• Experience developing and implementing statistical or predictive models to support credit decisioning and portfolio performance.
• Proven ability to work directly with business stakeholders and translate analytical findings into actionable business recommendations.
• Bachelor's or Master's degree in Statistics, Mathematics, Economics, Engineering, Computer Science, Data Science, or a related quantitative discipline, or equivalent professional experience.

Required Technical Skills:

• Strong Python programming skills for data analysis, model development, feature engineering, and automation.
• Advanced SQL expertise for data extraction, transformation, validation, and analysis of large datasets.
• Hands-on experience developing scorecards using Logistic Regression and other predictive modeling techniques.
• Experience applying Machine Learning (ML) techniques such as Gradient Boosting, Random Forest, XGBoost, or related methodologies within risk analytics environments.
• Proficiency with statistical analysis, model validation, model monitoring, and performance measurement techniques.