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Tier-1 brand, hybrid/remote working, and mid-level Data Scientist demand creates high competition.
Strong ML skill transferability tempered by fraud and financial-services domain specificity.
Explicit 2–4 year requirement plus mandatory ML, Python, deployment and cloud skills increase shortlisting strictness.
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Analyze large, complex datasets to detect and predict fraud cases using machine learning models.
Build, validate, optimize, and enhance fraud detection models utilizing advanced statistical and ML techniques.
Collaborate with cross-functional teams to deliver scalable analytical solutions and communicate insights to technical and non-technical stakeholders.
2 to 4 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, with hands-on ML experience (clustering, decision trees, boosting, etc.).
Knowledge of statistical techniques like regression, feature selection, time series; experience with ML-ops/containerization (Kubernetes a plus) and cloud platforms (AWS or Azure).
Experienced in developing and deploying enterprise-scale classification and regression models, preferably within fraud analytics or financial services.
Comfortable working in agile, multidisciplinary teams and engaging with diverse business and technical stakeholders.
Capable of troubleshooting production data and models, with familiarity of containerized environments and cloud platforms enhancing fit.