• Bachelor's/Master’s/Ph.D degree in a quantitative field, economics, business, engineering, or equivalent
• 5+ years of experience collaborating with data, engineering, and business teams
• Sound statistical knowledge: descriptive, hypothesis testing, probability distributions, correlation analysis, sampling techniques, supervised and unsupervised machine learning
• Understand the fundamentals of scorecard development (weight of evidence, information value)
• Exposure to the regulatory requirements (OSFI, Basel, EBA) for retail risk modeling
• Curiosity around how lending works, patterns seen in data and resilience to keep exploring and finding the right solution
• Exposure to measuring business outcomes of machine learning models in production is highly valued
• Extensive model validation experience
• Experience working with retail lending products
• Proficiency in predictive credit risk modeling
• Ability to be hands-on: data transformations, analysis, modeling and documentation
• Passion for applying data science in financial services, with innovative ideas for enhancing customers' financial lives
• Exceptional analytical and problem-solving abilities
• Proficient in SQL and Python

Desirable

• We are expanding our Risk Team as we develop world-class financial products, and we need a Data Scientist to elevate our data operations
• The Data Scientist will be tasked with identifying the most impactful solutions to key challenges, whether through pragmatic analysis, predictive modeling, or unsupervised learning methods
• This role provides extensive exposure to the data-driven decision-making needs of a high-growth organization, with a strong focus on achieving concrete outcomes and meeting key performance indicators (KPIs)
• Design and develop statistical credit risk models (PD, EAD and LGD) for an array of retail lending products
• Perform feature engineering from both traditional credit risk and alternative data sources
• Assist with model deployment / implementation, monitoring, and recalibrations as required
• Lead complex insights projects to find patterns in data and forecast future scenarios
• Be an organized multi-tasker with demonstrated expertise using tools to transform and prepare large sets of data for analysis
• Create dashboards and conduct ad-hoc analysis to address urgent data-related inquiries
• Draft detailed documentation of results, insights, and methodology on a project-by-project basis
• Coordinate and maintain communication with multiple stakeholders
• Present models/analysis in Credit Committee and seek approval from business owners and leadership teams
• Be a representative of the risk modeling function, continuously driving the visibility and value in the business