Design, develop, and deploy machine learning models to improve credit decisions and support business goals.
Collaborate with business leaders and cross-functional teams to create new data sources, improve modeling methodology, and apply models with risk management.
Operationalize models in production, monitor performance, ensure reliability, and maintain interpretability.
Promote software engineering best practices (test-driven development, code reviews, refactoring) and leverage the PyData stack (NumPy, pandas, scikit-learn).
Contribute to practical, end-to-end data science solutions and mentor peers as needed.
Requirements
PhD in a quantitative field with 1+ years of related experience; or BS/MS with 3+ years in related roles.
Experience designing, deploying, and managing supervised learning models in production.
Strong proficiency with PyData stack (NumPy, pandas, scikit-learn).
Familiarity with Spark, Kubernetes, Airflow, MLFlow; Chalk, BentoML, or DVC is a plus.
Design, develop, and deploy machine learning models to improve credit decisions and support business goals.
Collaborate with business leaders and cross-functional teams to create new data sources, improve modeling methodology, and apply models with risk management.
Operationalize models in production, monitor performance, ensure reliability, and maintain interpretability.
Promote software engineering best practices (test-driven development, code reviews, refactoring) and leverage the PyData stack (NumPy, pandas, scikit-learn).
Contribute to practical, end-to-end data science solutions and mentor peers as needed.
Requirements
PhD in a quantitative field with 1+ years of related experience; or BS/MS with 3+ years in related roles.
Experience designing, deploying, and managing supervised learning models in production.
Strong proficiency with PyData stack (NumPy, pandas, scikit-learn).
Familiarity with Spark, Kubernetes, Airflow, MLFlow; Chalk, BentoML, or DVC is a plus.
