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

Comprehensive healthcare

Long-term B2B contract with a stable project pipeline

Remote work model

Originally posted on Himalayas