This is a remote position.

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  Design and maintain credit risk and fraud detection models
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  Perform feature engineering on large structured financial datasets
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  Train, validate, and optimise machine learning models for production use
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  Monitor model performance and implement continuous improvements
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  Collaborate with ML engineers on deployment, tracking, and lifecycle management
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  Integrate model outputs into LangChain and LangGraph orchestration pipelines
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  Ensure model explainability, robustness, and regulatory compliance
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  Support documentation and governance requirements in a regulated environment

Requirements

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  Strong hands-on experience in Data Science and applied Machine Learning
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  Proficiency in Python and common data science libraries (Pandas, NumPy, scikit-learn)
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  Experience with gradient boosting frameworks such as XGBoost or LightGBM
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  Strong SQL skills and experience working with large datasets
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  Experience with PySpark or distributed data processing
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  Experience with MLflow for experiment tracking and model management
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  Understanding of production model lifecycle and monitoring practices
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  Ability to work in regulated or risk-sensitive environments
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  Fluent English for professional collaboration

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  Experience in credit risk, fraud detection, or financial services
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  Exposure to LangChain and LangGraph for orchestration of analytical outputs
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  Experience integrating ML models into real-time decision systems
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  Understanding of model interpretability and explainability frameworks

Benefits

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  Solid, competitive salary
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  Work in a multinational environment on international projects
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  Comprehensive healthcare
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  Long-term B2B contract with a stable project pipeline
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  Remote work model