Details:

DESCRIPTION-

The ideal Candidate will be a Credit Risk Statistician / Modeler to support the analytical, reporting and data needs of a young, exciting and well-funded start up. 

MINIMUM EDUCATION/EXPERIENCE REQUIRED-

Bachelor’s degree required in a highly technical/mathematical discipline (i.e. statistics, mathematics, quantitative analysis, economics, computer science, etc) . . . plus 5 years’ experience in credit risk management or other quantitative financial analysis. 

IDEAL QUALIFICATIONS

•The Candidate will be required to use data mining, predictive modeling and statistical techniques to solve business problems.

•Strong expertise with SAS / R for data manipulation , practical experience with ARIMA, ARIMAX.

•Knowledge of at least one of the following: credit loss forecasting models (NCO, PD, LGD), PPNR, time series, (OLS) linear regression models and statistical sampling techniques.Familiarity with SR11-07 regulatory guidance for Model Risk Management and previous experience doing model validation.

•Understanding of CCAR, DFAST (stress testing models), Basel, AML and fraud models, and ALLL models is a plus.•Knowledge of financial services.

•The candidate will be required to develop their statistical modeling skills in areas such as segmentation analysis, logistic regression, decision trees, and multivariate analysis.

•Ability to use analytics in a collaborative effort across functions to derive optimum solutions to business problems.

•Demonstrated problem solving skills.

•Demonstrated ability to effectively communicate (written and verbal) technical and analytical information to a variety of interested parties at all levels.

•Experience data mining large databases

RESPONSIBILITIES

•Extensive knowledge of credit risk databases, credit bureau to provide data and analytical support to senior management team.

•Perform analysis of data using statistical analysis tools including SQL, R, and Excel, and present results and recommendations to management.

•Identify deviations from forecast/expectations and explain variances.Identify risk and/or opportunities.

•Identify opportunities to leverage statistical solutions to business problems.The Statistician must be an excellent communicator (both spoken and written forms) and be able to effectively foster interactive collaboration and discussions with other team members.He must be able to present analysis and conclusions in a logical and clear manner.

Min. Qualification:

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Skills Required:

data mining, decision trees, excel, forecasting, linear regression, logistic regression, predictive modeling, r, sas, sql, statistics, time series