Fraud Strategy Decision Scientist

Navan is expanding its Fraud Risk Management organization to build world-class fraud prevention and detection capabilities across our travel and expense platforms. We are seeking a Fraud Strategy Decision Scientist to lead data-driven fraud strategy initiatives focused on machine learning features, rules development, and scalable fraud controls.

This role is ideal for a Fraud Strategy Decision Scientist who blends strong data science intuition with practical fraud rule design, understands how models and rules work together in production, and can partner deeply with Product, Engineering, and Fraud Operations to reduce fraud losses while enabling business growth.

You will play a critical role in shaping Navan’s end-to-end fraud strategy, translating advanced analytics and ML outputs into actionable rules, thresholds, workflows, and policy decisions across expense card issuing, payments, onboarding, and travel fraud.

What You’ll Do

Own and drive fraud strategy for key risk areas across travel and expense, balancing fraud loss reduction with customer experience.

Design and evolve fraud rules, thresholds, and decision workflows, informed by data science models, ML features, and investigative insights.

Partner closely with Data Science teams to translate machine learning model outputs and features into effective, explainable fraud strategies.

Lead strategy development across onboarding, payments, expense submissions, and transaction monitoring.

Apply advanced analytics techniques (trend analysis, segmentation, clustering, network analysis) to identify emerging fraud patterns and control gaps.

Perform root-cause analysis and loss attribution, quantifying financial impact and prioritizing strategy improvements.

Own strategy performance metrics, including fraud loss, approval rates, false positives, and customer friction.

Collaborate cross-functionally with Fraud Operations to ensure strategies are operationally executable and continuously optimized.

Partner with Engineering and Product to implement fraud strategies into real-time and batch decisioning systems.

Drive experimentation and A/B testing of rules, thresholds, and model-driven strategies.

Contribute to the long-term fraud strategy roadmap, including tooling, rule engines, model integration, and automation.

Support vendor evaluations and third-party data integrations to enhance detection signals.

Mentor junior fraud strategists and analysts, setting best practices for strategy design, documentation, and governance.

What We’re Looking For

7–10+ years of experience in fraud strategy, fraud analytics, or financial crime risk management.

Strong experience designing and managing fraud rules, policies, and decision strategies in production environments.

Deep understanding of how machine learning models and features are used to inform fraud decisions.

Proficiency in SQL and strong working knowledge of Python for analysis and strategy validation.

Experience working with large-scale data platforms such as Snowflake, Databricks, Spark, or similar.

Solid understanding of card payments, transaction flows, identity verification, and fraud typologies (ATO, synthetic identity, first-party fraud, third-party fraud, scams).

Demonstrated ability to partner effectively with Data Science, Engineering, Product, and Fraud Operations teams.

Strong analytical mindset with the ability to translate complex data into clear, actionable strategy decisions.

Excellent communication skills, including presenting strategy recommendations to senior leadership.

Experience in fintech, payments, travel, or e-commerce environments preferred.

Bachelor’s degree in a quantitative or analytical field; Master’s degree preferred.

Navan uses AI-assisted Automated Employment Decision Tool (Metaview) to assist with evaluating resumes against job qualifications for this role. All final decisions are made by human recruiters and hiring managers.

Human oversight: Metaview does not automatically reject candidates or make final hiring decisions. Our recruiters and hiring managers review all outputs and make the final hiring decision regarding every application.

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