Data Scientist (Credit Risk)

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).

Responsibilities:

• 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.

Must Have: 

• 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

Nice to have:

• Exposure to underwriting strategies and how models are leveraged for underwriting
• Experience deploying machine learning models in a production environment and integrating them into SaaS infrastructure
• Familiarity with data from monoline lenders or fintechs
• Experience with cloud-based platforms such as AWS or GCP
• Experience with alternative data or Non-Prime lending