Decision Scores Quantitative Modeler – Unsecured Products

Description:

• This Position is within Global Consumer Risk Management of Citi for Risk Decision Scores model development for the Unsecured portfolios. (e.g., credit cards, installment loans, ready credit etc.)

In this role, you will

• Build Account Management Risk Models using traditional and Machine Learning techniques.
• Develop these models in compliance with the Risk modeling policies and procedures.
• Leverage a variety of technologies such as SAS, R, Python, H2O, Spark, and more to extract the value out of the data.  
• Deliver on all phases of development, from design through training, testing, validation, and implementation.
• Work with Technology, Risk policy and Governance teams to deliver decision risk models in the market
• Practice your presentation and articulation skills to translate the complexity of your work to all types of audience

We will be thrilled to have someone who is

• Curious –challenges status quo, questions what they see and looks for answers when something is not intuitive
• Has attention to details – knows when something does not add up and is not right. Has attention to details
• Has ability to communicate results to diverse audiences

Qualifications:

• Advanced Degree (Masters required, PhD preferred) in Statistics, Applied Mathematics, Operations Research, Economics, MBA (Finance), or other highly quantitative discipline
• 5+ years’ experience in performing quantitative analysis, statistical modeling, loss forecasting, loan loss reserve modeling, and particularly econometric modeling of consumer credit risk stress losses
• At least 3 years’ experience in developing credit risk/marketing scorecard with at least 1 year experience leading a risk/marketing model development or a credit policy using segmentation
• Sound knowledge of statistical modeling concepts and industry best practices; experience with econometric and statistical modeling or risk models
• Excellent quantitative and analytic skills; ability to derive patterns, trends, and insights
• Experience with analytical or data manipulation tools (e.g. SAS, SQL, R, Python, Spark) Proficient with MS Office suite
• Consistently demonstrates clear and concise written and verbal communication skills
• Self-motivated and detail oriented
• Experience working in Big data environments; Intellectual curiosity to stay abreast of technological advances