Responsibilities

• 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.
• Solid software engineering fundamentals (TDD, code reviews, refactoring).
• Pragmatic, generalist data scientist focused on practical solutions; strong collaboration skills.
• Bonus: fintech/consumer lending experience; experience building end-to-end predictive models; interest in time-series neural networks.
• USD 120,000 – 135,000 base annua

Responsibilities

• 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.
• Solid software engineering fundamentals (TDD, code reviews, refactoring).
• Pragmatic, generalist data scientist focused on practical solutions; strong collaboration skills.
• Bonus: fintech/consumer lending experience; experience building end-to-end predictive models; interest in time-series neural networks.
• USD 120,000 – 135,000 base annua

Правовое

• Конфиденциальность
• Условия
• Cookies

Правовое

• Конфиденциальность
• Условия
• Cookies