Credit Risk Modeler

Ford Motor Company is seeking a Credit Risk Modeler to develop credit risk models for Ford Credit team. The successful candidate will have knowledge in general math modeling, Machine Learning (ML), and related Operations Research (OR) and simulation techniques.

Responsibilities

The role involves supporting credit risk analytics activities across model development, independent validation, monitoring, and business reporting. Key responsibilities include:

• Conducting data extraction, reconciliation, quality checks, and analytical review using tools such as SQL, SAS, Python, Excel, and PowerPoint.
• Translating analytical findings into clear insights and communicating results to stakeholders.
• Maintaining clear documentation of analysis, validation observations, reporting logic, assumptions, and key findings.
• Assisting in the development and enhancement of IFRS 9 models, including PD, LGD, EAD, expected credit loss, and related risk estimation frameworks.
• Performing independent validation activities by reviewing model methodology, assumptions, input data, outputs, performance, and documentation.
• Analyzing portfolio trends, credit risk drivers, delinquency patterns, default behavior, and model performance indicators to support risk decision-making.
• Executing recurring business reports, dashboards, and management information packs accurately and within agreed timelines.

Qualifications

• Master's degree in Mathematics/Statistics/Economics/Engineering or a related discipline.
• Good exposure to ML techniques such as clustering, classification, decision trees, random forests, and related algorithms.
• Excellent problem-solving, communication, and data presentation skills.
• Programming skills in R, SAS, and Python.
• Fluency with Excel, PowerPoint, and Word.
• Strong written and oral presentation/communication skills.
• Experience with developing and implementing cloud-based analytical solutions in GCP or a similar setup.

Experience (Good to have)

• 3-5 years of experience in the banking and financial services industry.
• Experience in credit analytics and preferably in financial analytics, retail banking, mortgage, lending/liability products.
• Risk analytics, credit risk scorecard development, model validation, IFRS 9 validations, credit loss forecasting.