Credit Risk Modeler

About Us
Ford Motor Company is one of the leading Automobile and Mobility Company. Ford is into shaping Great products, Strong Business and more importantly building a better world. The Global Data Insights and Analytics (GDI&A) department at Ford Motor Company is looking for motivated and talented individuals to develop credit risk models for Ford Credit team.

Potential candidates should have knowledge in general math modelling, Machine Learning (ML) and related Operations Research (OR) and simulation techniques. Candidates should have hands-on experience in developing quantitative models. They should possess the ability to translate a business problem into an analytical problem, recommend, implement and validate quantitative models, and generate/deliver insights to stakeholders. Preference would be given to candidates that are intellectually curious, innovative thinkers, and have demonstrated ability to solve problems independently.

Roles & Responsibilities

• Develop and validate credit risk models
• Using SAS, R, Python for model building and model validation
• Continual enhancement of statistical techniques and their applications in solving business objectives
• Compile and analyze the results from modeling output and translate into actionable insights
• Prepare PowerPoint presentations and document preparation for the entire credit risk modeling process
• Collaborate, Support, Advise and Guide in development of the models
• Acquire and share deep knowledge of data utilized by the team and its business partners
• Participate in global conference calls and meetings as needed and manage multiple customer interfaces
• Execute analytics special studies and ad hoc analyses
• Evaluate new tools and technologies to improve analytical processes
• Set own priorities and timelines to accomplish projects (accountability for project deliverables)

Skills/Knowledge required

• Masters in Finance, Financial Engineering, Analytics or Mathematics, Computer Science, Statistics, Industrial Engineering, Operations research, or related field.
• Good understanding of Probability of Default (PD), LGD and EAD modeling technique.
• Very good understanding of Predictive modeling techniques and their application.
• Knowledge of Credit life cycle
• Statistics and machine learning techniques.
• Conducted and applied statistical methodologies including linear regression, logistic regression, ANOVA/ANCOVA, CHAID/CART, cluster analysis
• Team player and collaboration skills.
• Programming skills in R, SAS, and PYTHON.
• Fluency with Excel, PowerPoint and Word
• Strong written and oral presentation / communication skills must have the ability to convey complex information simply and clearly
• Experience with developing and implementing cloud based analytical solutions in GCP or similar set up.

Qualifications

• Ph.D. or Masters in Mathematics/Statistics/Economics/Engineering or any other related discipline or a track record of performance that demonstrate this ability
• Practical applications of mathematical modeling, Operations Research and Machine Learning techniques
• Good exposure to ML techniques such as Clustering/classification/decision trees, Random forests, Support vector machines, Deep Learning, Neural networks, Reinforcement learning, and related algorithms
• Demonstrated knowledge in credit and/or market risk measurement and management
• Excellent problem solving, communication, and data presentation skills
• Proficient with SAS, SQL
• Familiarity with any of R, Python, Alteryx, GCP suite.
• Experience with any of Qlikview, Tableau

Experience:

• 3 - 5 Years exposure in Banking & Financial Services industry
• Candidate should have worked in Credit Analytics (Mandatory) and preferably in Financial Analytics, Retail bank, Mortgage, Lending / liability product
• Risk Analytics, Credit Risk Scorecard Development, Model Validation, IFRS 9 Validations, Credit Loss Forecasting