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Principal Data Scientist - Credit Risk/Lending @ Toast

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 Principal Data Scientist - Credit Risk/Lending

Job Description

  • 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 right
ingredients*
  • 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
Bonus
ingredients*
:
  • Passion for research and curiosity that calls you to go beyond good enough to create something innovative and exciting
  • Self starter who loves to dig into different kinds of data to solve business problems and can communicate their findings to cross-functional stakeholder

Job Classification

Industry: Software Product
Functional Area / Department: Data Science & Analytics
Role Category: Data Science & Machine Learning
Role: Data Scientist
Employement Type: Full time

Contact Details:

Company: Toast
Location(s): Bengaluru

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Keyskills:   Product engineering GIT orchestration Machine learning SCALA Data quality test driven development Business strategy SQL Python

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Toast

Toast empowers restaurants of all sizes to build great teams, increase revenue, improve operations, and delight guests. We pair our deep understanding of the restaurant industry with powerful cloud based software and restaurant-grade hardware to deliver an intuitive, all-in-one platform, across poin...