Description

You will lead the end-to-end development and deployment of advanced fraud detection systems using machine learning and graph analytics.

This role is on-site.

Responsibilities

• Lead fraud use case discovery, scenario design, and strategy definition.
• Build and validate pilot fraud detection models, optimizing precision, recall, and catch rates.
• Identify predictive variables and construct comprehensive fraud feature catalogues.
• Collaborate with engineering teams to implement low-latency scoring architectures.
• Drive model explainability, governance, and compliance while supporting challenger model validation.

Required Skills

• 10+ years of experience in data science, with a focus on fraud analytics.
• Strong expertise in Machine Learning, Graph Analytics, and Agentic AI Architecture.
• Proficiency in Python and Neo4j for data manipulation and graph processing.
• Hands-on experience with Dataiku for model development and deployment.
• Solid understanding of feature engineering, model explainability, and statistics.
• Experience designing experimentation frameworks and managing model governance.
• Ability to lead technical initiatives and mentor stakeholders.

Key Skills

Education

Any Graduate