Senior Credit Modeling & ML Engineer - Remote

Synchrony; Charlotte (NC); Hybrid; USD 115,000 - 200,000; Full time

Job summary

Synchrony is seeking a hands-on ACM Model Development professional to build and validate customer account management models. You will work across data collection, cleansing, model development, and validation with strong collaboration with Model Risk Management.

The role emphasizes practical analytics in big data environments, including Python, AWS, and ML techniques, with opportunities to influence regulatory documentation and model risk understanding.

Qualifications

• Bachelor’s degree in Statistics, Mathematics, Data Science, or a related quantitative field.
• 4+ years of hands-on model development/validation in financial services, credit risk, or fraud analytics.
• Experience with Python, Spark, AWS SageMaker, H2O, and SAS.

Responsibilities

• Perform hands-on data collection, data cleansing, and model development to support ACM model projects.
• Partner with Model Risk Management on model validations, annual reviews, and ongoing assessments.
• Analyze model performance and explain model behavior to business stakeholders.
• Utilize advanced ML techniques to perform data mining for customer risk analysis and profiling.
• Prepare and maintain comprehensive model documentation required to meet regulatory and compliance standards.
• Lead automation initiatives using AWS, Python, Tableau, and related tools.
• Build expertise in Synchrony’s data warehouse and analytics ecosystem.

Skills

Python

AWS

Machine Learning

GenAI/AI

Big data

Communication

Education

Bachelor’s degree in Statistics/Mathematics/Data Science

Master’s or MBA with analytic focus

Tools

Python

Spark

AWS SageMaker

H2O

SAS

Job description

Synchrony is seeking a hands-on ACM Model Development professional to build and validate customer account management models. You will work across data collection, cleansing, model development, and validation with strong collaboration with Model Risk Management.

The role emphasizes practical analytics in big data environments, including Python, AWS, and ML techniques, with opportunities to influence regulatory documentation and model risk understanding.