12 days old
2018-01-092018-02-16

Credit Risk Analytics Cons 2

Bengaluru, KA 560043
  • Job Code
    22691BR-1


Wells Fargo EGS (India) Private Limited :
Wells Fargo India Solutions Pvt. Ltd., a wholly owned subsidiary of Wells Fargo & Company (NYSE: WFC), was set up in August 2006 in Hyderabad. WFIS has over 8000 team members and continues to grow with a second center in Bangalore and third center in Chennai.
Knowledge Services:
Knowledge Servicesis akey component of WFIS - Emerging Services. Knowledge Services brings a Team Member-based approach to the international talent pool focusing on analytical requirements. Knowledge Services (KS), encompasses roles that deliver insights and analysis based on skills, experience and judgment. Team Members exercise this judgment under general direction versus rule-driven processing. Processes and services performed in this area tend to be based on skills, domain knowledge and intellectual capability.

About the team:
The commercial modeling within the CaPM COE will focus on the creation of best-in-class statistical models to support the Wholesale/Commercial portfolios. The modeling teams build a myriad of model suites that support key functions including stress testing, BASEL, RWA and allowance for both domestic and foreign loans. It includes Model conceptualization and design, Estimation of Stress test models, Documentation, Issue resolution and Ad-hoc analysis.
Primary role of this analyst is to build and maintain Basel credit models for the home lending, auto, credit card, personal loans, business lines and loans, and student loan products


Job Description:

Duties may include, but not limited to:

  • Develop and document data and modeling processes for credit model parameters used in Retail Credit BASEL Modeling
  • Work closely with onshore team and other resources to understand and enhance the data and modeling process behind existing models and forecast, address data and model issues/questions.
  • Underlying portfolio data research and analytics using strong programming skills
  • Adhere to audit and model validation governance to ensure data and modeling process are in compliance with policy and are working as intended, address model validation and regulatory feedback issues
  • Coherently support analysis to modeling teams and other users such as model validation, audit
  • Support ad hoc analytic projects
  • Ability to work overlap hours with US team
  • Split shift 12:30-9:30 or 1:30-10:30 pm



Required Qualifications:
  • Over all experience around 3-6 years in similar role
  • M.A. College Degree or Higher in applied mathematics, statistics, engineering, physics, accounting, finance, economics, econometrics, computer sciences, or business/social and behavioral sciences with a quantitative emphasis.





  • 3+ years advanced programming exposure in SAS including SQL
  • 3+ years of commercial or consumer credit experience
  • 3+ years of process definition and documentation experience in credit risk modeling/BASEL modeling


Desired Qualifications:

  • Ability to prioritize work, meet deadlines, achieve goals and work under pressure in a dynamic and complex environment
  • Detail oriented, results driven, and has the ability to navigate in a quickly changing and high demand environment while balancing multiple priorities
  • Understanding of bank regulatory data sets and other industry data sources
  • Exposure to commercial banking products and modeling in this area
  • Ability to identify and manage complex issues and negotiate solutions within a geographically dispersed organization
  • Ability to research and report on a variety of issues using problem solving skills
  • Ability to interact with integrity and a high level of professionalism with all levels of team members and management
  • Ability to make timely and independent judgment decisions while working in a fast-paced and results-driven environment


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Categories

  • Engineering
  • Financial Services
  • Retail / Wholesale

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Credit Risk Analytics Cons 2

Wells Fargo
Bengaluru, KA 560043

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Bengaluru, KA
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