technologies-expected :

responsibilities :

Develop predictive models for credit scoring, loan deterioration, and time-to-default (e.g., logistic regression, scorecards, survival models, Markov chains).
Perform data exploration and feature engineering using behavioral and transactional credit data (payments, delinquencies, account activity).
Ensure model performance, robustness, and interpretability (e.g., PSI, SHAP, monitoring).
Collaborate with Data Engineering teams to deploy models into production pipelines (batch or real-time scoring).
Contribute to model audit, validation, and documentation processes.

requirements-expected :

3+ years of experience as an ML Engineer / Data Scientist in a financial or regulated environment.
Strong understanding of traditional risk modeling techniques: logistic regression, scorecard models, survival analysis, Markov chains.
Hands-on experience with ML algorithms such as XGBoost, LightGBM, Random Forest, or deep tabular models.
Proficiency in Python (e.g., scikit-learn, xgboost, lifelines, SHAP, pandas) and SQL.
Familiarity with regulatory requirements in credit risk (e.g., ECL, IFRS 9, Basel III).
Strong communication skills and ability to explain model logic to non-technical stakeholders (e.g., risk, compliance, audit).

offered :

We are open to the employment form according to your preferences
Work with experienced and engaged team, willing to learn, share knowledge and open for growth and new ideas
Hybrid working system
Mindbox is a dynamically growing IT company, but still not a large one – everybody can have a real impact on where we are going next
We invest in developing skills and abilities of our employees
We have attractive benefits and provide all the tools required for work f.e.computer
Interpolska Health Care, Multisport, Warta Insurance, training platform (Sages)

benefits :

responsibilities :