About the role
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
Originally posted on Himalayas