Navan is expanding its Fraud Risk Management organization to build world-class fraud detection, prevention, and analytics capabilities supporting our rapidly growing travel and expense businesses. We are seeking a highly skilled and visionary Staff / Senior Staff Data Scientist, Fraud, to design and scale advanced data science solutions that protect Navan’s customers, platform, and financial ecosystem.
This is a strategic and hands-on role, where you’ll partner with other fraud strategy members, product, engineering, and data teams to design the ML features, build the rule workflow, build next-generation fraud models, develop actionable insights, and lead the application of AI/ML to mitigate emerging fraud threats both in expense card issuing and travel fraud. The ideal candidate combines deep technical expertise in machine learning and data systems with a strong understanding of payment, identity, and transactional fraud patterns.
You’ll report to the Head of Fraud Risk Data Science strategy and play a critical role in shaping Navan’s end-to-end fraud detection infrastructure and analytical roadmap.
What You’ll Do:
- 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 modeling, real-time clustering, and link analysis to identify emerging fraud patterns and organized fraud rings.
- Partner cross-functionally with other Risk Strategy team members, 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 optimization.
- Perform root-cause and loss attribution analyses, identifying vulnerabilities and quantifying financial impact to inform control effectiveness and business risk appetite.
- Sign up for the stretch fraud loss goals and the ability to drive the roadmap to meet the goal.
- Collaborate with Data Engineering and Platform teams to define data schemas, pipelines, and infrastructure that enable 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, helping evolve Navan’s machine learning and analytics capabilities to stay ahead of emerging fraud trends.
- Partner with external vendors and third-party data sources to enrich detection signals and improve model precision and recall.
What We’re Looking For:
- 10+ years of experience in data science strategy, with a strong focus on fraud detection, risk modeling, or financial crime analytics.
- Deep technical expertise in machine learning, predictive modeling, 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.
- Strong understanding of payment processing, card networks, identity verification, and behavioral fraud typologies (e.g., synthetic identities, ATO, friendly fraud, first-party fraud, third-party fraud, scams).
Demonstrated success in developing and deploying ML models in production, including monitoring and score drift management.
- Familiarity with fraud detection tools, rule engines, and streaming data systems is a plus.
- Proven ability to communicate complex data science findings to technical and non-technical audiences, including executives.
- Experience collaborating in cross-functional teams that include Product, Engineering, and Fraud Operations.
- Master’s or PhD in Computer Science, Statistics, Applied Mathematics, or related quantitative field.