Lead Data Scientist (Fintech)

TL;DR

Lead Data Scientist (Fintech): Shaping hirify.global’s next-generation consumer-level credit scoring and portfolio valuation models with an accent on real-time PD (Probability of Default) model development using statistical and ML approaches. Focus on integrating models into frameworks for underwriting and economic return optimization, ensuring compliance with regulatory and fairness standards.

Location: Stockholm

Salary: kr 752,814 SEK - kr 843,151 SEK

Company

hirify.global is a company in fintech domain.

What you will do

• Shape hirify.global’s next-generation consumer-level credit scoring and portfolio valuation models.
• Design and maintain real-time PD models using statistical and ML approaches.
• Develop calibration frameworks and ensure compliance with regulatory and fairness standards.
• Explore novel methodologies, including LLMs for explainability and feature engineering.
• Translate modeling insights into strategic credit policies and business value.
• Mentor junior team members and contribute to hirify.global’s long-term modeling vision.

Requirements

• 5+ years’ experience in credit risk modeling for consumer lending, credit cards, or BNPL.
• Deep proficiency in PD model development and validation, with strong knowledge of calibration techniques.
• Advanced Python and SQL skills; familiar with XGBoost, scikit-learn, pandas, MLFlow.
• Experience with explainability frameworks such as SHAP, LIME, PDP.
• Ability to communicate technical concepts clearly and influence cross-functional decisions.
• Familiarity with real-time modeling and current trends in ML and credit analytics.

Nice to have

• Hands-on experience using LLMs to extract features from unstructured data (e.g., customer communications, credit applications).
• Knowledge of integrating third-party credit bureau data into production models.
• Understanding of champion/challenger model frameworks and A/B testing infrastructure.
• Exposure to loan-level economic modeling, including cost-of-capital and loss metrics.