Hybrid Quantitative Risk Modeler

Huntington Bancshares, Inc.

Charlotte (NC)

On-site

USD 90,000 - 140,000

Full time

4 days ago
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Job summary

Huntington Bancshares, Inc. in Charlotte, NC, is seeking Quantitative Risk Modeling Analysts to develop consumer and commercial credit, PPNR, loan origination and portfolio management models; analyze portfolio performance; monitor existing models and report results.

The role requires a Master’s degree in a quantitative field and 1+ years of experience with SQL, SAS, R and Python, plus ML and data mining. PhD and CCAR/CECL knowledge are preferred.

Qualifications

  • Master’s degree in quantitative field (mathematics, statistics, economics, engineering, finance, physics).
  • 1+ years of experience in statistical modeling using SQL, SAS, R and Python.
  • 1+ years of experience in machine learning and data mining.
  • 1+ years of experience with data visualization tools (Tableau preferred) and MS Excel.

Responsibilities

  • Develop consumer and/or commercial credit, PPNR, loan origination and portfolio management models.
  • Analyze credit portfolio performance data and conduct ongoing model monitoring.
  • Analyze and report monitoring results and complete ad-hoc analytics.
  • Collaborate with teams across governance, audit/compliance and validation projects.
  • Research new modeling methodologies and techniques and work with deadlines.

Skills

Analytical thinking
Communication skills
Independent work

Education

Master’s degree in quantitative field
PhD in quantitative field

Tools

SQL
SAS
R
Python
Tableau
Excel

Job description

Huntington Bancshares, Inc. in Charlotte, NC, is seeking Quantitative Risk Modeling Analysts to develop consumer and commercial credit, PPNR, loan origination and portfolio management models; analyze portfolio performance; monitor existing models and report results.

The role requires a Master’s degree in a quantitative field and 1+ years of experience with SQL, SAS, R and Python, plus ML and data mining. PhD and CCAR/CECL knowledge are preferred.

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