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 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. Location: Bangalore, India <h4>What You’ll Do</h4> <ul> <li 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.</li> <li 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.</li> <li 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.</li> <li 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.</li> <li style="font-family: helvetica, arial, sans-serif;">Own model lifecycle management from feature engineering and experimentation to monitoring, model retraining, and post-deployment optimisation.</li> <li 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.</li> <li 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.</li> <li 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.</li> <li 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.</li> <li 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.</li> <li 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.</li> </ul> <h4>What We’re Looking For</h4> <ul> <li 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.</li> <li>Master’s (must-have) or PhD (preferred) in Computer Science, Statistics, Applied Mathematics, or related quantitative field.</li> <li style="font-family: helvetica, arial, sans-serif;">Deep technical expertise in machine learning, predictive modelling, anomaly detection, and network analysis applied to fraud problems.</li> <li style="font-family: helvetica, arial, sans-serif;">Proficiency in Python, SQL, and modern ML libraries.</li> <li style="font-family: helvetica, arial, sans-serif;">Experience with large-scale data environments such as Snowflake, Databricks, Spark, or equivalent big data platforms.</li> <li style="font-family: helvetica, arial, sans-serif;">Strong understanding of payment processing, card networks, identity verification, and behavioural 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.</li> <li style="font-family: helvetica, arial, sans-serif;">Familiarity with fraud detection tools, rule engines, and streaming data systems is a plus.</li> <li style="font-family: helvetica, arial, sans-serif;">Proven ability to communicate complex data science findings to technical and non-technical audiences, including executives.</li> <li style="font-family: helvetica, arial, sans-serif;">Experience collaborating in cross-functional teams that include Product, Engineering, and Fraud Operations.</li> </ul><div class="content-conclusion">&nbsp; 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.&nbsp; Human oversight:&nbsp;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.&nbsp; <ul> <li> Your rights: If you prefer to have your application reviewed without AI assistance, you may request a human evaluation by entering your email <a href="https://my.metaview.app/applications/opt-out?organization=15dd7e883f">here</a>. Your decision to do so will not affect how your candidacy is evaluated.&nbsp; </li> </ul> Please refer to our<a href="https://navan.com/candidate-privacy-notice"> Candidate Privacy Notice</a> for more information about our processing of personal data, and your rights.</div>