Credit Risk Modeler - BFS

FORD is hiring.

Job Title: Credit Risk Modeler

- 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)

Qualifications for Internal Candidates

Skills/Knowledge required

- Master's 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