Senior Analyst

Twitter Facebook
Location
Salt Lake City, UT
Job Type
Direct Hire
Date
Mar 16, 2017
Job ID
2462348

Senior Analyst, Model Risk Management – Salt Lake City, Utah – Relocation is available for this role.

The Company is headquartered in Salt Lake City, Utah and is an FDIC insured, Utah state chartered bank.  The Company is a leading national provider of online consumer and small business loans made in partnership with finance companies, OEMs, retailers and financial technology companies.

Job Summary - The Senior Analyst – Model Risk Management position will report to the VP Model Risk Management for the Bank and will support all Credit related initiatives undertaken.

Primary Responsibilities
Develop and implement statistical and machine learning based quantitative models used in underwriting strategies, loss forecasting and capital calculations
Develop and drive analysis of portfolio health through modeling as well as valuation techniques to drive best investment decisions
Develop alternative model approaches to assess model design and advance future capabilities
Review and assess model validation plans and processes
Assess the risk model methodologies, outputs, and processes and work with various Industry leading Fintech lending partner and retail partners associated with the bank
Understand technical issues in econometric and statistical modeling and apply these skills toward assessing model risks and opportunities
Communicate clearly and concisely both verbally and through written communication via model validation reports and presentations
Required Skills and Experience
Bachelor’s degree in Statistics, Economics, Mathematics, Industrial Engineering, Operations Research, Financial Engineering, Physics, Engineering or Computer Science
At least one to two years’ experience in quantitative analysis with statistics or data mining
Prior consumer lending based modeling exposure preferred
Proficiency with statistical and data software languages and packages including SAS, R , Knowledge Seeker and E Miner
Familiarity with machine learning techniques such as GBM, Random Forest and ensembles of multiple nested regression techniques
Strong verbal and written communication skills.