Data Scientist

Пълно описание

We are on a mission to bring subprime lending to the Fintech 2.0 horizon!
Our way forward is to provide sustainable, scalable and embedded financial solutions to a wider range of customers that are often excluded by the traditional banks!
We are looking for a passionate Data Scientist to join our Risk Function and take ownership of the full lifecycle of our consumer credit risk models.
You will develop, calibrate and maintain scorecards and IFRS 9 models, monitor performance, and ensure full documentation and regulatory compliance.
This is a high-impact role where you will help shape the entire risk modelling framework of a highly ambitious financial institution.
Risk Modelling & Analytics
Develop, calibrate, and maintain consumer credit risk models, including:
Application scorecards
Behavioral scorecards
Risk segmentation and affordability models
Build and maintain PD, LGD, and EAD models for IFRS 9 Expected Credit Loss (ECL) calculations.
Use advanced ML methods (e.g., CatBoost, gradient boosting) where suitable for challenger models.
IFRS 9 Modelling
Maintain and enhance the IFRS 9 ECL model, including staging logic, macroeconomic overlays, and model calibration.
Support the monthly and quarterly provisioning cycles with high-quality analytics.
Model Monitoring & Validation
Perform regular model monitoring: Gini/ROC, KS, calibration, PSI/CSI, back-testing.
Conduct annual validation and coordinate external validator/auditor reviews.
Identify model weaknesses and propose improvements or recalibrations.
Documentation & Governance
Maintain complete model documentation (methodology, data, assumptions, validation results).
Ensure compliance with internal Risk Policies, IFRS 9 guidance, and audit requirements.
Prepare model performance updates for the Risk Committee.
Data & Systems
Work with Data Engineering to improve data pipelines and ensure data quality.
Build reproducible analytical workflows in Python/R, SQL.
Support deployment of models into production systems and decision engines.



2+ years of experience in credit risk modelling, data science, or advanced analytics.
Strong programming skills in Python (mandatory) plus solid SQL.
Experience and good understanding of tree-based models, boosting, sampling techniques, deep learning
Hands-on experience building:
Logistic regression scorecards with WOE/IV
PD/LGD/EAD models
IFRS 9 provisioning models is a plus
Strong understanding of model monitoring metrics: Gini, ROC, KS, PSI, calibration curves.
Education
Bachelor’s or Master’s degree in Statistics, Mathematics, Data Science, Econometrics, Finance, Physics or similar.
Soft Skills
Excellent analytical and problem-solving skills.
Strong attention to detail and documentation discipline.
Ability to work cross-functionally (Risk, Finance, IT, Product).
Good communication skills in English.
A future in a dynamic company passionate about innovation and fintech services
Potential growth based on performance
Opportunity to experiment with and apply innovative scoring methodologies
Very friendly, supportive and non-conflict working environment