AVP - Automation - Credit Risk Analytics - SAS

Looking for female leaders having 9 - 12 years of experience in automation for credit risk analytics and having strong experience in SAS, VBA, Tableau.

GENERAL ROLE & RESPONSIBILITIES

Objectives:

Positions within Global Consumer Risk Management of the company for both Non Regulatory Decisions models & Regulatory (CCAR/DFAST/CECL) model performance monitoring, production forecast reporting and analytics for Unsecured portfolios.

Core Responsibilities:

This position within Global Consumer Banking will focus on automation of model performance monitoring, reporting and analytics for both Regulatory (CCAR/CECL) and Non Regulatory Decision Models for Unsecured products (e.g., Credit Cards). The responsibility includes but not limited to the following activities

- Establish and maintain automation framework to support the monitoring and management credit risk regulatory and non-regulatory models.

- Develop automation and build innovative solutions to reduce turnaround time for performance monitoring reports and deep dive analysis.

- Build and enforce best practices and process improvements to ensure streamlined monitoring process

- Hands on experience on various automation and dashboard tools [SAS, VBA, Tableau, R, Python, etc].

- Exposure to DisplayR, or working knowledge of R graphic packages is preferable. Strong working knowledge in SAS (preferably w Unix Environment Experience), VBA and Tableau is a must

- Identify and understand business automation needs and adapt to case expectations. Show ability to resolve complex issues across work streams

- Manipulation and analysis of large datasets from multiple sources, including proprietary agency analytics platforms

- Conduct QA/QC on all steps (e.g., macro-economy series, model output, etc.) required for model monitoring and production forecast reporting.

- Work closely with cross functional teams, including country/regions business stakeholders, model validation and governance teams, and model implementation team

- Train junior team members on technical expertise

- Expected to work with moderate supervision and guidance

- Individual Contributor

Education:

Advanced Degree (Bachelors required or Masters preferred) in Statistics, Computer Science, Operations Research, Economics, etc. MBA s should apply only if they are interested in career in specialized quantitative risk management discipline

Skillset

- Experience in developing end-to-end automation solution for reporting processes using SAS, Excel VBA, Tableau

- Stronghold on Visualization tool (or libraries) like SAS Visual Analytics, Tableau, Python, QlikView etc.

- Expert in working on SAS, Tableau, Python.

- Experience in Excel VBA

- Basic understanding of modeling processes (linear/ logistic regression, segmentation, decision tree, machine learning [Gradient Boosting, XG Boost, SVM, etc.], time series, linear/nonlinear optimization) would be desirable.

- Basic understanding of relevant metrics / KPIs to read & bring out insights on model performance tracking is preferable