Lead the development and implementation of advanced predictive models, to assess credit risk, optimize lending growth, and improve overall portfolio performance.
Will actively engage in code reviews and comprehensive data quality assessments, ensuring that all development data pipelines and code meet the highest standards of reliability, accuracy, and readiness for seamless production deployment.Collaborate closely with cross-functional teams, including Risk Management, Product, Engineering, and Business Strategy, to identify opportunities for leveraging data-driven insights to support growth and risk mitigation initiatives.
Design, develop, and maintain innovative credit risk and lending growth strategies that align with the organizations objectives, while ensuring compliance with regulatory requirements and industry best practices.
Monitor and analyze the performance of existing models and strategies, identifying areas for improvement and implementing data-driven refinements as needed.
Present complex data-driven insights and recommendations to stakeholders in a clear, concise, and actionable manner, while fostering a data-driven culture within the organization.
Mentor and guide junior data scientists, fostering a collaborative environment and promoting the growth and development of the data science team.
Stay current with the latest trends and developments in data science, fintech, and credit risk management, continually exploring new methods and technologies to enhance the organizations capabilities.

Do you have the rightingredients*

Advanced degree in quantitative field such as Data Science, Statistics, Mathematics, Financial Engineering or related discipline
10+ years of experience in data science, with a focus on credit risk modeling in retail lending space (consumer or small business) and lending growth strategies, preferably within the fintech lending space.
Proficiency in machine learning, AI, and statistical modeling techniques, with a strong track record of developing and deploying successful predictive models in a business setting.
Familiarity with the US model risk management policies such as SR 11-7 that govern the lending models is strongly preferred.
Strong proficiency in Python and SQL and experience with data science libraries and tools such as: Spark, Scala, scikit-learn, Tensorflow, PyTorch, XGBoost etc.
Familiarity with standard software engineering practices and tools including object-oriented programming, test-driven development, CI/CD, git, task orchestration (Airflow) and AWS tooling
Strong understanding of credit risk management, lending practices, and regulatory requirements within the financial industry.
Excellent communication and presentation skills, with the ability to articulate complex data-driven insights to both technical and non-technical stakeholders.
Strong leadership skills, with experience mentoring and guiding junior data scientists

Principal data Scientist

Principal Data Scientist - Credit Risk/Lending