Senior Data Scientist, Fraud Operations

What You’ll Do

What We’re Looking For

Qualifications

• 5+ 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 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.
• 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.
• 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.
• 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.
• Your rights: If you prefer to have your application reviewed without AI assistance, you may request a human evaluation by entering your email here. Your decision to do so will not affect how your candidacy is evaluated.
• Please refer to our Candidate Privacy Notice for more information about our processing of personal data, and your rights.

Preferred Qualifications

• Master’s (must-have) or PhD (preferred) in Computer Science, Statistics, Applied Mathematics, or related quantitative field.
• Familiarity with fraud detection tools, rule engines, and streaming data systems is a plus.

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 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 optimisation.
• 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.
• 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.
• 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.
• 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.
• 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.
• Your rights: If you prefer to have your application reviewed without AI assistance, you may request a human evaluation by entering your email here. Your decision to do so will not affect how your candidacy is evaluated.
• Please refer to our Candidate Privacy Notice for more information about our processing of personal data, and your rights.