Data Scientist – Credit Risk and Fraud
这是一个远程职位。我们正在寻找一个数据科学家，加入全球银行环境中的企业决策智能平台。该角色重点关注跨越多个国际市场的信用风险和欺诈防范，支持生产银行系统中实时和批量决策。该平台结合了大规模结构化数据处理，机器学习模型和 GenAI 编排层次。它在严格的延迟，可用性和监管要求下运行在显著范围内，并不断扩展新的模式，数据来源和推理组件。责任 ·设计和维护信贷风险和骗局检测模型 · 执行大型结构化金融数据集的功能工程 · 培训，验证和优化机器学习模型用于生产使用 ·监测模型性能并实施持续改进 · 与 ML 工程师合作部署，跟踪和生命周期管理 · 将模型输出整合到 LangChain 和 LangGraph 主管管道 ·确保模型可解释性，强度和法规遵守 · 在受管制环境中支持文档和治理要求 · 数据科学和应用机器学习方面的实践经验 · Python 和普通数据科学图书馆 (Pandas, NumPy, scikit-learn) 的熟练知识 · 经验与 XGBoost 或 LightGBM 等推进梯度框架的经验 · 强大的 SQL 技能和与大型数据集工作经验 · PySpark 或分布式数据处理的经验 • 对实验跟踪和模型管理的 MLflow 的经验·了解生产模式生命周期和监测实践 · 在受管制或风险敏感环境中工作的能力 · 熟练英语专业合作很好拥有 · 信用风险，欺诈检测或金融服务方面的经验 · 对分析结果编排而接触到 LangChain 和 LangGraph · 将 ML 模型整合到实时决策系统中的经验 · 了解模型解释性和可解释性的框架 • 优势 · 坚实的，竞争力的工资 · 在跨国项目中工作 · 全面的医疗保健 · 与稳定的项目管道进行长期 B2B 合同 · 从远程工作模式 原始地在喜马拉雅山上发布
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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