Credit Risk Modeller / Risk Data Scientist @ Antal
(práce v Polsku)

Credit Risk Modeller / Risk Data Scientist @ Antal

Polsko

What We’re Looking For

• 3+ years of experience as a Machine Learning Engineer / Data Scientist in a financial or regulated environment.
• Strong understanding of traditional credit risk modelling techniques (logistic regression, scorecards, survival analysis, Markov chains).
• Hands-on experience with ML algorithms such as XGBoost, LightGBM, Random Forest, or tabular deep learning.
• Proficiency in Python (e.g., scikit-learn, xgboost, lifelines, SHAP, pandas) and SQL.
• Familiarity with credit risk regulatory frameworks (e.g., IFRS 9, Basel III, Expected Credit Loss).
• Strong communication skills and ability to explain technical concepts to non-technical stakeholders (e.g., Risk, Compliance, Audit).

Credit Risk Modeller / Risk Data Scientist

? Location: Cracow, Poland – Hybrid model: 2 days in the office, 3 days remote
? Employment Type: [Full-time / B2B]
? Industry: Financial Services / Risk / Machine Learning

About the Role

Join a dynamic team responsible for developing advanced credit risk models that directly impact business decisions and financial security. This is a great opportunity to combine data science, predictive modelling, and real-world regulatory applications in a fast-paced environment.

Why Join Us?

• Work on high-impact projects that shape risk decisions at scale.
• Collaborate with an international, cross-functional team of experts.
• Access modern tools, large datasets, and a supportive learning environment.
• Hybrid work model: enjoy flexibility with 2 days in the office and 3 days remote per week.

Interested?

Send us your CV or LinkedIn profile. We’ll be happy to tell you more about the role and team!

,[Building predictive models for credit scoring, credit deterioration, and time-to-default (e.g., logistic regression, scorecards, survival models, Markov chains)., Performing data exploration and feature engineering based on behavioural and transactional credit data (payments, delinquencies, account activity)., Ensuring model performance, robustness, and interpretability (PSI, SHAP, monitoring)., Collaborating with data engineering teams to implement models into production (batch or real-time scoring)., Participating in model documentation, audits, and validation processes.] Requirements: Machine Learning, Deep learning, Python, scikit-learn, pandas, SQL, IFRS, Communication skills, Audit

Kategorie

artificialIntelligence

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