Lead Data Scientist –Credit Risk Modeler (Hybrid)

Key Skills

Data Analysis, Data science techniques, Effective communication skills, Finance, Java Programming, Leadership Skill, Oracle, Python Programming, R Programming, SAS, SQL, Statistics

Job Description

The Risk Modeling Utility (RMU) group within Personal Banking & Wealth Management (PBWM) is looking to add experienced quantitative analyst at Vice President level to join the retail Basel Model Development team. The team is responsible for development of the unexpected credit loss models, risk-weighted asset calculation used in determining the capital requirement or capital adequacy ratio for Citi's entire retail credit portfolios.

With minimal oversight from senior staff, you would be responsible for the following:

Responsibilities:

• Development of advanced retail Basel risk parameter models(PD, LGD, EAD) as well as segmentation models.
• Development of methodologies, algorithms and diagnostic tools for testing model robustness, stability and performance.
• Perform reliability analysis and quality control of modeling data and model results.
• Develop and maintain technical documentation for risk methodologies and applications; including project plans, model descriptions, mathematical derivations, data analysis, process and quality controls.
• Participate in the implementation of analytical tools by reporting functions, and the migration of models to the production environment.
• Engage with risk managers, business clients and partners in the analysis and interpretation of results, incorporating their feedback as appropriate into models.
• Provide timely and accurate responses to clients, senior management and regulators. 
• Participate in discussions with model validation, internal and external audits and regulatory reviews.
• Assist in the preparation and delivery of training materials, presentations and reports on credit risk analytics for technical and non-technical audiences.

Qualifications:

• Excellent knowledge and understanding of a variety of model development and validation testing techniques covering risk models, including but not limited to linear regression models, logistic regression, generalized additive models, decision and regression trees, information gain and related segmentation statistical tools. Previous familiarity with models such as default, delinquency, loss severity modelling, PD, LGD, EAD models etc. is preferred.
• With 5+ years of relevant model development experience in Finance or related field.
• Relevant and recent experience with statistical analysis, modeling techniques and numerical implementations (Python, R, Java, SAS, or Oracle SQL).
• Sound problem-solving skills, ability to think creatively.
• Ability to meet deadlines for product deliverables in a timely, proactive and entrepreneurial manner.
• Working knowledge of credit data reliability analysis, quality controls and data processing.
• Experience in developing and maintaining detailed technical documentation for models, model validation, project plans and processes. 
• Strong interpersonal skills and the ability to foster a collaborative environment.
• Excellent written and verbal communication skills, and ability to discuss technical issues with clients, peers, auditors, regulators and senior management.

Education:

• Master or Ph.D. degree in Economics, Finance, Statistics, Analytics or any quantitative fields