Data Scientist – Credit Risk and Fraud

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