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About the role
Roles and Responsibilities : Develop and implement fraud detection models using machine learning algorithms to identify potential fraudulent activities in banking transactions. Collaborate with cross-functional teams to design and develop data pipelines for large-scale data analysis, visualization, and reporting. Conduct exploratory data analysis (EDA) on complex datasets to identify trends, patterns, and correlations that may indicate fraudulent activity. Provide insights and recommendations to stakeholders on how to improve fraud prevention strategies based on statistical modeling results. Job Requirements : 10-15 years of experience in data science or a related field with expertise in artificial intelligence, machine learning, natural language processing, or similar technologies. Strong background in fraud analytics or a related field such as risk management or finance. Proficiency in Python programming language with experience working with popular libraries like scikit-learn, pandas, NumPy etc. Roles and Responsibilities : Develop and implement fraud detection models using machine learning algorithms to identify potential fraudulent activities in banking transactions. Collaborate with cross-functional teams to design and develop data pipelines for large-scale data analysis, visualization, and reporting. Conduct exploratory data analysis (EDA) on complex datasets to identify trends, patterns, and correlations that may indicate fraudulent activity. Provide insights and recommendations to stakeholders on how to improve fraud prevention strategies based on statistical modeling results. Job Requirements : 10-15 years of experience in data science or a related field with expertise in artificial intelligence, machine learning, natural language processing, or similar technologies. Strong background in fraud analytics or a related field such as risk management or finance. Proficiency in Python programming language with experience working with popular libraries like scikit-learn, pandas, NumPy etc.
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