- Erfahrung
- 5–7 Jahre
- Gehalt
- —
- Stellenangebote
- 1
- Veröffentlicht
- vor 5 Stunden
- Arbeitsmodus
- Arbeiten von zu Hause
- Ausbildung
- Bachelor-Abschluss
- Teilnahmeberechtigung
- Candidates with a bachelor’s or master’s degree in a quantitative discipline and 5–7 years of experience in credit risk modeling for lending are eligible to apply.
- Wieder aufnehmen
- Bewerbung erforderlich
Stellenbeschreibung
Role overview
In this senior individual contributor role, you will shape the data science approach for unsecured personal lending by building, checking, and improving predictive credit risk models. Your work will sit at the intersection of data science, risk, and business, helping strengthen lending decisions and refine overall risk strategy.
Key responsibilities
- Design and develop predictive credit risk models for lending, including PD, LGD, EAD, propensity-to-default, and credit score models.
- Analyze internal and external datasets in depth and create meaningful features that improve model quality.
- Test, back-test, validate, and continuously monitor existing models to maintain accuracy and stay aligned with regulatory expectations.
- Work with business and operations stakeholders to embed model outputs into lending strategy and decisioning workflows.
- Prepare and maintain clear documentation covering model development, validation, and governance requirements.
- Share insights on portfolio risk movements, early warning signals, and the key factors driving losses.
- Support junior analysts and help foster a strong analytical and modeling culture across the team.
Required qualifications and experience
You should hold a bachelor’s or master’s degree in Statistics, Mathematics, Economics, Data Science, or another quantitative discipline, along with 5 to 7 years of experience in credit risk modeling in a lending setting. Strong hands-on expertise in statistical modeling, machine learning, and predictive analytics is expected, including techniques such as logistic regression, decision trees, gradient boosting, and neural networks. Practical programming ability in Python, R, or SAS is required, and SQL is essential. You should also have a solid grasp of credit risk concepts, portfolio management, and lending workflows, plus strong problem-solving, communication, and stakeholder management skills.
Preferred experience
- Experience working with cloud-based data platforms or modern analytics tools.
- Exposure to alternative data sources in credit risk modeling.
- Familiarity with model governance and audit procedures in regulated financial organizations.
- Experience with model validation approaches and compliance requirements in Canada (OSFI) or similar international standards.
What the role offers
- A competitive base salary along with performance bonus and equity participation.
- The opportunity to work in a fast-growing startup where your contribution directly affects product outcomes and P&L.
- Flexible working arrangements, including hybrid and remote options.
- A learning budget, health coverage, and a team environment built around data and innovation.
Work arrangement
This position supports flexible work options, including hybrid and remote setups.