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Strong Tier-1 brand, remote/hybrid role, and mid-level Data Scientist title increase candidate competition.
Preferred fraud, financial crime, and banking experience makes background fit highly domain-specific.
Explicit 4–8 years plus required ML, Python, SQL and deployment experience increases filter strictness.
Analyze large, complex datasets to identify fraud inconsistencies and patterns.
Build, validate, optimize, and improve machine learning models for fraud detection and prevention at enterprise scale.
Collaborate with cross-functional teams to deliver scalable analytical solutions and communicate findings to technical and non-technical stakeholders.
4 to 8 years of relevant Data Science experience.
Advanced degree in Statistics, Mathematics, Computer Science, Engineering, or related fields.
Proficiency in Python (3.7+), SQL, and Excel; hands-on experience with ML techniques such as clustering, decision trees, boosting.
Experience developing and deploying classification and regression models at enterprise scale; familiarity with ML-Ops frameworks or containerized environments (Kubernetes is a plus).
Experienced in building and troubleshooting production ML models in fraud analytics or financial crime domains.
Skilled at working in agile, multi-disciplinary teams and communicating complex analytical concepts clearly to non-technical stakeholders.
Operationally focused on continuous model improvement, data pattern research, and delivering measurable business impact through advanced analytics.