Deloitte Advisory Senior Consultant in Chicago, Illinois
Deloitte Transactions and Business Analytics LLP seeks an Advisory Senior Consultant in Chicago, IL.
Work You ll Do
Perform the development and validation of advanced or predictive analytic modeling using SAS, SPSS, Stata or R technologies; develop and validate portfolio-level models, including call report models of balance, gross charge-offs and net credit losses; develop and validate loan-level models, including Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD) and the quantitative reserve modeled component of estimates of Allowance for Loan and Lease Losses (ALLL); perform data quality control, develop database reports and user interfaces, and normalize relational data; lead the preparation and presentation of complex written and verbal materials, including reports, findings, presentations, and project planning; provide input with respect to practice technology initiatives and investments; and work on multiple project work streams, small engagement teams or as part of large, complex engagements.
Bachelor's (or higher) degree in Business Administration, Economics, Computer Science, Information Systems, Engineering, Finance, Accounting or related field (willing to accept foreign education equivalent).
Eighteen months of experience in credit risk analytics, modeling, and research with focus on stress testing and related regulatory requirements.
Experience must include 18 months of:
Developing scripts in SAS, R, STATA, Python and SQL Server to support advanced analytic development activities, including implementing econometric models, designing user-defined analytical functions, as well as developing and updating databases and
Performing IT information management for banking clients, including data delivery and regulatory reporting for Comprehensive Capital Analysis and Review (CCAR)/ Dodd-Frank Act Stress Test (DFAST), Dodd Frank Act, and Basel III.
Experience must include 12 months of:
Implementing statistical techniques, including clustering, correspondence, and factor analysis, utilizing software packages, including SAS, SPSS and R, and utilizing business intelligence tools, including Teradata, and big data tools, including HIVE and PIG, to handle large databases;
Utilizing statistical techniques, including clustering, ROC, VIF, Fisher score for variable selection/reduction, and logit regression for model development;
Utilizing machine learning algorithms and programming languages, including R, SAS, and Python, to develop mathematical models, including financial risk management models, for large banking organizations;
Developing and implementing Expected Loss (EL) credit risk models, including Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), and DFAST/CECL models;
Developing expected credit loss models for loan valuation and the quantitative reserve of Allowance for Loan and Lease Losses (ALLL) and Pre-Provision Net Revenue (PPNR) using external, internal and macro-economic data sources;
Conducting model validation incremental testing and statistical analysis on model methodology, data and model assumptions and limitations, parameter estimation, model outputs, model performance, residual diagnostics, in-sample and out-of-sample back-testing, and model sensitivity tests, using time series and panel data with statistical software, including SAS, R, Matlab, and Excel VBA; and
Performing model validation of credit and PPNR models by examining the conceptual soundness of model theory, the reliability of data sources, the validity of model assumptions, and the effectiveness of model performance testing as well as independently replicating model outputs, summary statistics, model testing results and visualizations.
Position requires approximately 80% travel.
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