Data Scientist – Credit Risk and Fraud
Location not identified
- Pay
- Pay not listed
- Work setting
RemoteLocation limits apply — work setting source
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.
Read the full posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingDesign and maintain credit risk and fraud detection models
Perform feature engineering on large structured financial datasets
Train, validate, and optimise machine learning models for production use
Monitor model performance and implement continuous improvements
Integrate model outputs into LangChain and LangGraph orchestration pipelines
From the employer’s posting
Design and maintain credit risk and fraud detection models
Perform feature engineering on large structured financial datasets
Train, validate, and optimise machine learning models for production use
Monitor model performance and implement continuous improvements
Integrate model outputs into LangChain and LangGraph orchestration pipelines
What you’ll bring
All qualificationsCore experience
- Strong hands-on experience in Data Science and applied Machine Learning
- Proficiency in Python and common data science libraries (Pandas, NumPy, scikit-learn)
- Strong SQL skills and experience with large datasets
- Experience with gradient boosting (XGBoost or LightGBM)
- Experience with PySpark or distributed data processing
Qualification wording
Strong hands-on experience in Data Science and applied Machine Learning
Proficiency in Python and common data science libraries (Pandas, NumPy, scikit-learn)
Strong SQL skills and experience working with large datasets
Experience with gradient boosting frameworks such as XGBoost or LightGBM
Experience with PySpark or distributed data processing
Tools in this posting
- Python
- SQL
- MLflow
- LightGBM
- NumPy
- pandas
- PySpark
- scikit-learn
- XGBoost
Source — Tool mentions in context
- Strong hands-on experience in Data Science and applied Machine Learning - Proficiency in Python and common data science libraries (Pandas, NumPy, scikit-learn) - Experience with gradient boosting frameworks such as XGBoost or LightGBM
- Experience with gradient boosting frameworks such as XGBoost or LightGBM - Strong SQL skills and experience working with large datasets - Experience with PySpark or distributed data processing
- Experience with PySpark or distributed data processing - Experience with MLflow for experiment tracking and model management - Understanding of production model lifecycle and monitoring practices
- Proficiency in Python and common data science libraries (Pandas, NumPy, scikit-learn) - Experience with gradient boosting frameworks such as XGBoost or LightGBM - Strong SQL skills and experience working with large datasets
- Strong SQL skills and experience working with large datasets - Experience with PySpark or distributed data processing - Experience with MLflow for experiment tracking and model management
Benefits in the posting
Full benefits wording- Comprehensive healthcare
- Remote work model
From the employer’s posting.
Posting record
Report an errorOpen 4 monthsWe first saw this posting 148 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jun 3, 2026 · 148 days ago
- Recorded sightings
- 194
- Last seen by us
- Oct 29, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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Job description
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.
Responsibilities
- Design and maintain credit risk and fraud detection models
- Perform feature engineering on large structured financial datasets
- Train, validate, and optimise machine learning models for production use
- Monitor model performance and implement continuous improvements
- Collaborate with ML engineers on deployment, tracking, and lifecycle management
- Integrate model outputs into LangChain and LangGraph orchestration pipelines
- Ensure model explainability, robustness, and regulatory compliance
- Support documentation and governance requirements in a regulated environment
Requirements
- Strong hands-on experience in Data Science and applied Machine Learning
- Proficiency in Python and common data science libraries (Pandas, NumPy, scikit-learn)
- Experience with gradient boosting frameworks such as XGBoost or LightGBM
- Strong SQL skills and experience working with large datasets
- Experience with PySpark or distributed data processing
- Experience with MLflow for experiment tracking and model management
- Understanding of production model lifecycle and monitoring practices
- Ability to work in regulated or risk-sensitive environments
- Fluent English for professional collaboration
Nice to have
- Experience in credit risk, fraud detection, or financial services
- Exposure to LangChain and LangGraph for orchestration of analytical outputs
- Experience integrating ML models into real-time decision systems
- Understanding of model interpretability and explainability frameworks
Benefits
- Solid, competitive salary
- Work in a multinational environment on international projects
- Comprehensive healthcare
- Long-term B2B contract with a stable project pipeline
- Remote work model
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Location not supplied.
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.
More source context
- Long-term B2B contract with a stable project pipeline - Remote work model
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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