Senior Data Scientist, Fraud Operations

Bangalore, INOnsiteFull TimeSeniorPosted 22 days agoVisa sponsorship available

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Required skills

fraud detection
risk modeling
financial crime analytics
machine learning
predictive modeling
anomaly detection
network analysis
Python
SQL
ML libraries

Nice to have

Applied Mathematics
rule engines
streaming data systems

Role summary

Navan is seeking a Senior Data Scientist for its Fraud Operations team in Bangalore. This role focuses on designing and scaling advanced machine learning solutions for fraud detection and prevention in travel and expense businesses. Responsibilities include leading ML feature design, building rule workflows, developing predictive models, conducting applied research in areas like anomaly detection and graph modeling, and managing the model lifecycle. The ideal candidate has 5+ years of experience in fraud data science, expertise in Python, SQL, ML libraries, and big data platforms, and a strong understanding of payment and transactional fraud. This is a strategic, hands-on role involving cross-functional collaboration and mentoring junior team members.

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

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 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.

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

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.

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