Navan is expanding its Fraud Risk Management team to build world-class fraud detection, prevention, and analytics capabilities supporting rapidly growing travel and expense businesses.
The role is strategic and hands-on, partnering with fraud strategy members, product, engineering, and data teams.
Responsibilities
Lead the design and deployment of advanced ML features, build rule workflows to detect and prevent fraud across travel and expense.
Lead the design and development of advanced ML and statistical models to detect, predict, and prevent fraudulent behavior across onboarding, payments, and expense workflows.
Drive applied research in anomaly detection, network/graph modelling, real-time clustering, and link analysis to identify emerging fraud patterns and organized fraud rings.
Partner cross-functionally with Risk Strategy, Fraud operations, Engineering, and Product teams to translate insights into scalable prevention rules, thresholds, and model-driven interventions.
Own model lifecycle management from feature engineering and experimentation to monitoring, model retraining, and post-deployment optimisation.
Perform root-cause and loss attribution analyses, identifying vulnerabilities and quantifying financial impact.
Sign up for stretch fraud loss goals and drive the roadmap to meet the goal.
Collaborate with Data Engineering and Platform teams to define data schemas, pipelines, and infrastructure for real-time fraud monitoring and analytics.
Mentor junior data scientists and fraud analysts, providing technical guidance and driving excellence in experimentation, model governance, and reproducibility.
Contribute to the overall fraud strategy roadmap.
Partner with external vendors and third-party data sources to enrich detection signals and improve model precision and recall.
Requirements
5+ years of experience in data science strategy, with a strong focus on fraud detection, risk modeling, or financial crime analytics.
Master's (must-have) or PhD (preferred) in Computer Science, Statistics, Applied Mathematics, or related quantitative field.
Deep technical expertise in machine learning, predictive modelling, anomaly detection, and network analysis applied to fraud problems.
Proficiency in Python, SQL, and modern ML libraries.
Experience with large-scale data environments such as Snowflake, Databricks, Spark, or equivalent big data platforms.