<p><span style="font-family: helvetica, arial, sans-serif;">Navan is expanding its Fraud Risk Management team 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 <strong>Data Scientist, Fraud,</strong> to design and scale advanced data science solutions that protect Navan’s customers, platform, and financial ecosystem.</span></p> <p><span style="font-family: helvetica, arial, sans-serif;">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.</span></p> <p><span style="font-family: helvetica, arial, sans-serif;"><strong>Location:</strong> Bangalore, India</span></p> <h4><span style="font-family: helvetica, arial, sans-serif;"><strong>What You’ll Do</strong></span></h4> <ul> <li style="font-family: helvetica, arial, sans-serif;"><span style="font-family: helvetica, arial, sans-serif;">Lead the design and deployment of advanced ML features, build rule workflows to detect and prevent fraud across travel and expense.</span></li> <li style="font-family: helvetica, arial, sans-serif;"><span style="font-family: helvetica, arial, sans-serif;">Lead the design and development of advanced ML and statistical models to detect, predict, and prevent fraudulent behavior across onboarding, payments, and expense workflows.</span></li> <li style="font-family: helvetica, arial, sans-serif;"><span style="font-family: helvetica, arial, sans-serif;">Drive applied research in anomaly detection, network/graph modelling, real-time clustering, and link analysis to identify emerging fraud patterns and organized fraud rings.</span></li> <li style="font-family: helvetica, arial, sans-serif;"><span style="font-family: helvetica, arial, sans-serif;">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.</span></li> <li style="font-family: helvetica, arial, sans-serif;"><span style="font-family: helvetica, arial, sans-serif;">Own model lifecycle management from feature engineering and experimentation to monitoring, model retraining, and post-deployment optimisation.</span></li> <li style="font-family: helvetica, arial, sans-serif;"><span style="font-family: helvetica, arial, sans-serif;">Perform root-cause and loss attribution analyses, identifying vulnerabilities and quantifying financial impact to inform control effectiveness and business risk appetite.</span></li> <li style="font-family: helvetica, arial, sans-serif;"><span style="font-family: helvetica, arial, sans-serif;">Sign up for the stretch fraud loss goals and the ability to drive the roadmap to meet the goal.</span></li> <li style="font-family: helvetica, arial, sans-serif;"><span style="font-family: helvetica, arial, sans-serif;">Collaborate with Data Engineering and Platform teams to define data schemas, pipelines, and infrastructure that enable real-time fraud monitoring and analytics.</span></li> <li style="font-family: helvetica, arial, sans-serif;"><span style="font-family: helvetica, arial, sans-serif;">Mentor junior data scientists and fraud analysts, providing technical guidance and driving excellence in experimentation, model governance, and reproducibility.</span></li> <li style="font-family: helvetica, arial, sans-serif;"><span style="font-family: helvetica, arial, sans-serif;">Contribute to the overall fraud strategy roadmap, helping evolve Navan’s machine learning and analytics capabilities to stay ahead of emerging fraud trends.</span></li> <li style="font-family: helvetica, arial, sans-serif;"><span style="font-family: helvetica, arial, sans-serif;">Partner with external vendors and third-party data sources to enrich detection signals and improve model precision and recall.</span></li> </ul> <h4><span style="font-family: helvetica, arial, sans-serif;">What We’re Looking For</span></h4> <ul> <li style="font-family: helvetica, arial, sans-serif;"><span style="font-family: helvetica, arial, sans-serif;">5+ years of experience in data science strategy, with a strong focus on fraud detection, risk modeling, or financial crime analytics.</span></li> <li><span style="font-family: helvetica, arial, sans-serif;">Master’s (must-have) or PhD (preferred) in Computer Science, Statistics, Applied Mathematics, or related quantitative field.</span></li> <li style="font-family: helvetica, arial, sans-serif;"><span style="font-family: helvetica, arial, sans-serif;">Deep technical expertise in machine learning, predictive modelling, anomaly detection, and network analysis applied to fraud problems.</span></li> <li style="font-family: helvetica, arial, sans-serif;"><span style="font-family: helvetica, arial, sans-serif;">Proficiency in Python, SQL, and modern ML libraries.</span></li> <li style="font-family: helvetica, arial, sans-serif;"><span style="font-family: helv