About the Company

We are a financial technology company developing data-driven lending and credit assessment solutions for European markets. Our platform combines financial data, statistical modelling, and modern machine learning to support responsible lending decisions, improve risk assessment, and deliver efficient digital credit services.

Role Overview

We are looking for a Machine Learning Engineer with expertise in credit scoring and predictive risk modelling. In this role, you will develop and maintain models that estimate creditworthiness, predict default risk, and support lending decisions throughout the customer lifecycle.

Working closely with credit risk analysts, data scientists, product managers, and software engineers, you will translate complex financial data into reliable models that support accurate, explainable, and responsible credit decisions.

Key Responsibilities

• Develop and deploy machine learning models for credit scoring, probability of default estimation, and borrower risk segmentation.
• Build predictive models using customer financial information, repayment histories, credit bureau data, and other lawfully available indicators.
• Apply classification, regression, and statistical modelling techniques to assess credit risk.
• Perform feature selection, feature engineering, data quality assessment, and exploratory data analysis.
• Evaluate model performance using appropriate metrics, including ROC-AUC, precision, recall, calibration, and population stability measures.
• Analyse model stability, data drift, and changes in borrower risk characteristics over time.
• Develop model validation and monitoring processes to support ongoing risk management.
• Collaborate with credit risk specialists to translate lending policies and business requirements into technical solutions.
• Implement explainability techniques to support transparent credit decisions and appropriate adverse-decision explanations.
• Ensure models are developed and maintained in accordance with applicable data protection, consumer credit, and model governance requirements.
• Document model assumptions, validation results, limitations, and deployment procedures.

Requirements and Qualifications

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Economics, Data Science, or a related discipline.
• At least 3 years of experience in machine learning, predictive analytics, credit risk, or statistical modelling.
• Strong Python skills and practical experience with pandas, NumPy, scikit-learn, XGBoost, or LightGBM.
• Solid understanding of classification models, probability calibration, feature selection, and model validation.
• Experience working with SQL databases and large-scale financial or customer datasets.
• Understanding of credit risk concepts, including probability of default, loss given default, exposure at default, and credit risk segmentation.
• Experience with model explainability tools such as SHAP or comparable techniques.
• Familiarity with MLflow, model deployment, cloud platforms, and production monitoring.
• Knowledge of credit risk governance, GDPR, and responsible AI principles is an advantage.
• Strong problem-solving skills and the ability to communicate technical findings to non-technical stakeholders.

What We Offer

• Competitive compensation aligned with skills and experience.
• Hybrid working opportunities in Estonia.
• The opportunity to build and improve production credit scoring models.
• Access to modern analytics infrastructure and collaborative engineering practices.
• Professional training and opportunities to deepen expertise in financial risk modelling.
• An international working environment with cross-functional collaboration.
• The opportunity to contribute to responsible lending and data-driven financial products.

Application Instructions

Please submit your CV detailing your experience with machine learning, credit scoring, risk analytics, or predictive modelling. Candidates are encouraged to highlight relevant projects, model validation methods, and experience deploying models into production environments.

Use machine learning to make credit risk assessment more accurate, transparent, and responsible.

Are you interested in this position?

Apply by clicking on the “Apply Now” Button below!

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