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Tier-1 brand, hybrid remote, popular mid-level ML role with broad skill requirements drives high competition.
Core ML skills are transferable, but fraud and financial domain experience increases domain specificity.
Explicit 2–4 years requirement plus mandatory Python, SQL and production ML experience tightens shortlisting.
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Analyze large and complex datasets to detect and predict fraud using machine learning models.
Build, validate, optimize, and deploy enterprise-scale fraud detection models, improving performance and scalability.
Collaborate with cross-functional teams to deliver 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.
Strong programming skills in Python (3.7+) and proficiency in SQL and Excel.
Experience with machine learning techniques including clustering, decision trees, boosting, regression models, and knowledge of ML-Ops frameworks or containerized environments; exposure to cloud platforms (AWS or Azure) preferred.
Experienced in fraud analytics, financial crime, or risk management model development, preferably within banking or financial services domain.
Able to work effectively in agile, multi-disciplinary teams with strong communication skills to explain complex concepts to non-technical stakeholders.
Demonstrated expertise in deploying and troubleshooting machine learning models in production environments, including use of containerization technologies such as Kubernetes.