Senior Data Scientist, Credit Risk & Model Validation

Enova; Chicago (IL); Hybrid; USD 140,000 - 210,000; Full time

Benefits offered by this job

Health insurance

PTO & paid holidays off

Sabbatical program

Summer hours

Paid parental leave

401(k) matching + Roth option

Job summary

Enova is seeking a Lead Data Scientist in Chicago, IL to lead model development, validation, and governance for credit risk models. You will mentor teammates, communicate complex quantitative concepts, and collaborate with cross-functional partners to drive data-driven decisions.

The role emphasizes independent validation, production readiness, and continuous improvement of risk analytics methodologies, with hybrid in-office expectations.

Qualifications

• Strong knowledge of statistical models and risk management methodologies.
• Advanced Python programming for data analysis.
• Experience with relational databases such as SQL.
• Experience in developing or validating advanced credit risk models.
• 5+ years in quantitative analysis in financial services and leading analytics projects.
• Ability to explain quantitative concepts to technical and non-technical stakeholders.

Responsibilities

• Lead and perform model validation of credit risk models.
• Prepare model validation reports and documentation.
• Provide technical guidance and mentorship to team members.
• Interact with stakeholders to gather information and provide effective challenge.
• Support model releases to production.
• Monitor model performance dashboards and build alerts.
• Drive improvements to validation methodologies, processes, and governance.
• Conduct ad hoc analyses for risk management, marketing and operations.

Skills

Statistical modeling

Python

SQL

Data analysis

Model validation

Mentorship

Communication

Tools

Python

SQL

Job description

Enova is seeking a Lead Data Scientist in Chicago, IL to lead model development, validation, and governance for credit risk models. You will mentor teammates, communicate complex quantitative concepts, and collaborate with cross-functional partners to drive data-driven decisions.

The role emphasizes independent validation, production readiness, and continuous improvement of risk analytics methodologies, with hybrid in-office expectations.