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Data Scientist – Credit Risk and Fraud
JOB SUMMARY
Roles
Job details
This is a remote position.
We are looking for a Data Scientist to join an enterprise decision intelligence platform within a global banking environment.
The role focuses on credit risk and fraud prevention across multiple international markets, supporting real-time and batch decisioning in production banking systems.
The platform combines large-scale structured data processing, machine learning models, and GenAI orchestration layers.
It operates at significant scale under strict latency, availability, and regulatory
requirements
and is continuously expanded with new models, data sources, and reasoning components. ResponsibilitiesDesign and maintain credit risk and fraud detection modelsPerform feature engineering on large structured financial datasetsTrain, validate, and optimise machine learning models for production useMonitor model performance and implement continuous improvementsCollaborate with ML engineers on deployment, tracking, and lifecycle managementIntegrate model outputs into LangChain and LangGraph orchestration pipelinesEnsure model explainability, robustness, and regulatory complianceSupport documentation and governance
requirements
in a regulated environment
Requirements
Strong hands-on experience in Data Science and applied Machine LearningProficiency in Python and common data science libraries (Pandas, NumPy, scikit-learn)Experience with gradient boosting frameworks such as XGBoost or LightGBMStrong SQL skills and experience working with large datasetsExperience with PySpark or distributed data processingExperience with MLflow for experiment tracking and model managementUnderstanding of production model lifecycle and monitoring practicesAbility to work in regulated or risk-sensitive environmentsFluent English for professional collaborationNice to haveExperience in credit risk, fraud detection, or financial servicesExposure to LangChain and LangGraph for orchestration of analytical outputsExperience integrating ML models into real-time decision systemsUnderstanding of model interpretability and explainability frameworks
Benefits
Solid, competitive salaryWork in a multinational environment on international projectsComprehensive healthcareLong-term B2B contract with a stable project pipelineRemote work model.
We are looking for a Data Scientist to join an enterprise decision intelligence platform within a global banking environment.
The role focuses on credit risk and fraud prevention across multiple international markets, supporting real-time and batch decisioning in production banking systems.
The platform combines large-scale structured data processing, machine learning models, and GenAI orchestration layers.
It operates at significant scale under strict latency, availability, and regulatory
requirements
and is continuously expanded with new models, data sources, and reasoning components. ResponsibilitiesDesign and maintain credit risk and fraud detection modelsPerform feature engineering on large structured financial datasetsTrain, validate, and optimise machine learning models for production useMonitor model performance and implement continuous improvementsCollaborate with ML engineers on deployment, tracking, and lifecycle managementIntegrate model outputs into LangChain and LangGraph orchestration pipelinesEnsure model explainability, robustness, and regulatory complianceSupport documentation and governance
requirements
in a regulated environment
Requirements
Strong hands-on experience in Data Science and applied Machine LearningProficiency in Python and common data science libraries (Pandas, NumPy, scikit-learn)Experience with gradient boosting frameworks such as XGBoost or LightGBMStrong SQL skills and experience working with large datasetsExperience with PySpark or distributed data processingExperience with MLflow for experiment tracking and model managementUnderstanding of production model lifecycle and monitoring practicesAbility to work in regulated or risk-sensitive environmentsFluent English for professional collaborationNice to haveExperience in credit risk, fraud detection, or financial servicesExposure to LangChain and LangGraph for orchestration of analytical outputsExperience integrating ML models into real-time decision systemsUnderstanding of model interpretability and explainability frameworks
Benefits
Solid, competitive salaryWork in a multinational environment on international projectsComprehensive healthcareLong-term B2B contract with a stable project pipelineRemote work model.
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Madiff United States
