Data Scientist, Credit Risk

November 5

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KOHO

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Fintech • eCommerce • Banking

KOHO is a financial technology company that provides Canadians with tools to manage their money effectively and enhance their lifestyle. Offering a single platform, KOHO allows users to spend smarter with a prepaid Mastercard that offers up to 5% cash back, grow their savings with competitive interest rates, facilitate borrowing with easy cash advances, and build credit history. The app also features budgeting tools, payment splitting, and various customizable plans to suit individual needs, emphasizing convenience and financial wellness for its users.

201 - 500 employees

Founded 2014

💳 Fintech

🛍️ eCommerce

🏦 Banking

📋 Description

• Design and develop statistical and machine learning models for credit risk parameters (PD, EAD, LGD) across lending products including credit card, line of credit, overdraft, BNPL, etc. • Execute full model development lifecycle from data exploration and feature engineering through validation and deployment • Implement advanced modelling techniques including regression, classification, ensemble methods, and deep learning algorithms • Conduct model performance monitoring, champion-challenger testing, and regulatory compliance validation • Collaborate with Risk Management, Credit, and Product teams to translate business requirements into technical specifications • Create automated dashboards, reports, and ad-hoc analyses to support strategic business decision-making • Document model methodology, results, and insights • Lead model refresh initiatives and back-testing procedures to maintain predictive accuracy and performance

🎯 Requirements

• 5+ years of experience in predictive modelling with demonstrated collaboration across data science, engineering, and product teams • Proven experience developing credit risk models (PD, EAD, LGD) for consumer lending products including credit card, line of credit, overdraft, BNPL, etc. • Expert proficiency in Python and SQL with hands-on experience in feature engineering, model development, validation, and performance analysis • Strong knowledge of statistical modelling techniques, machine learning algorithms, and model deployment in production environments • Experience with MLOps platforms (Sagemaker) • Track record of measuring and optimizing business outcomes of machine learning models in live production systems • Excellent written and verbal communication skills with ability to present complex technical concepts to non-technical stakeholders • Experience with regulatory frameworks and model risk management practices in financial services • Bachelor's or Master's degree in Statistics, Mathematics, Economics, Computer Science, or related quantitative field • Passion for applying data science to improve financial products and enhance customer financial outcomes.

🏖️ Benefits

• Competitive compensation & equity • Generous vacation + Wellness days + Flex Days + holiday closure • Remote-first environment + coworking support + yearly all hands retreat • Access to coaching & growth programs • Parental top-up & leave policies • Comprehensive health benefits • Power-up budgets for books, home office setup, phone & internet, AI tools, and professional development