Quantitative Risk Analyst: Small Business Credit Modeling

NewLane Finance; United States; On-site; USD 64,491 - 105,949; Full time

Benefits offered by this job

Medical, dental, and vision coverage

401(k) plan

Flexible spending accounts

Paid parental leave

Job summary

NewLane Finance is looking for a skilled individual to assist in credit and risk modeling, leveraging data to optimize credit strategies. The role requires strong quantitative and analytical skills to develop complex commercial credit models.

The candidate will engage with various analytics projects using emerging technologies while ensuring compliance with regulatory guidance. Prospective applicants should possess a solid background in statistical analysis and coding, specifically in Python or similar languages.

Qualifications

• Bachelor’s degree in a quantitative discipline or equivalent experience.
• 3+ years of commercial small business credit modeling experience.
• Experience in Consumer Lending statistical modeling/analytics.
• 2+ years in coding with Python, PySpark or equivalent.
• Demonstrated experience with SAS.

Responsibilities

• Assist in constructing Credit Decision Scorecards.
• Develop a framework for data collection and processing.
• Plan and execute analytics on large data sets.
• Validate credit default rates and make recommendations.
• Work with sales management on risk-based pricing strategies.

Skills

Commercial small business credit modeling

Statistical modeling

Quantitative analysis

Coding in Python/PySpark

Communication

Education

Bachelor’s degree in Mathematics/Statistics, Operations Research, Economics, or Finance

4+ years’ experience in Risk or Finance

Tools

SAS

Job description

NewLane Finance is looking for a skilled individual to assist in credit and risk modeling, leveraging data to optimize credit strategies. The role requires strong quantitative and analytical skills to develop complex commercial credit models.

The candidate will engage with various analytics projects using emerging technologies while ensuring compliance with regulatory guidance. Prospective applicants should possess a solid background in statistical analysis and coding, specifically in Python or similar languages.