Senior Data Scientist, Credit Risk & ML Modeling (Hybrid)

STRATIVIS; United States; Hybride; USD 130 000 - 180 000; Plein temps

Avantages offerts par ce poste

Medical, dental, vision

Hybrid work environment

Casual culture

Transit access to downtown Chicago

Bike room and gym on site

Résumé du poste

STRATIVIS is seeking a Sr. Data Scientist to join a fast-growing team in a data-driven environment. You will develop and productionize models, engineer features, and translate business needs into analytical solutions, influencing data-driven decisions across the organization.

The role emphasizes collaboration with a data science team, ongoing skill development, and opportunities to deploy and monitor models in production within a consumer lending context.

Qualifications

• Bachelor’s degree in Mathematics, Statistics, Physics, Biostatistics, Engineering or equivalent experience.
• Masters or Ph.D. preferred.
• 3 years of experience in data science.
• Experience in credit risk/ consumer lending industry is preferred.

Responsabilités

• Build, implement and maintain linear and nonlinear models.
• Perform feature engineering on new and existing datasets.
• Translate business needs into data science problems and definitions.
• Create datasets and analyses to solve business problems with actionable insights.
• Contribute to strategy and direction of data science projects.
• Complete ad-hoc analyses to improve data quality and team effectiveness.

Connaissances

Credit risk experience

Problem-solving

Cross-functional collaboration

Communication of analyses

Time management

Self-starter

Independent project execution

Adaptability

Formation

Bachelor’s degree in Mathematics/Statistics/Physics/Biostatistics/Engineering

Masters or Ph.D. preferred

Description du poste

STRATIVIS is seeking a Sr. Data Scientist to join a fast-growing team in a data-driven environment. You will develop and productionize models, engineer features, and translate business needs into analytical solutions, influencing data-driven decisions across the organization.

The role emphasizes collaboration with a data science team, ongoing skill development, and opportunities to deploy and monitor models in production within a consumer lending context.