A hybrid Data Analytics role at PayJoy.
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Original listing text, shown exactly as published by the company.
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Analyze large datasets to identify trends, patterns, and indicators of fraud, focusing on both broad fraud trends and specific areas such as credit card and transactional fraud.
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Develop and refine fraud detection models and algorithms to improve detection rates and false positives.
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Drive research projects through all its phases, from inception to deployment and impact assessment.
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Prepare and present analytical reports on fraud trends, findings, and the effectiveness of fraud prevention strategies.
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Write queries, design, build and maintain dashboards and visualizations to monitor randomized experiments and key metrics.
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Continuously quantify the impact in gross margin of fraud initiatives, size the economic opportunity of product vulnerabilities and process inefficiencies.
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Work cross functionally with stakeholders in Data Science, Fraud Operations, Product, and Go-to-market.
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Bachelor’s degree in applied quantitative field, such as statistics, economics, mathematics, etc.
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1-2 years of work experience with transactional fraud in credit lending and credit cards, working in the telecom industry and a fast-paced and rapidly changing environments.
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Strong SQL competencies, and experience with statistical programming languages such as R or Python.
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Excellent written and oral communication skills, summarize and communicate technical analysis to broad audiences.
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Strong analytical skills and who are curious by nature.
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Results-oriented and feels comfortable working both autonomously and in heavily cross-functional environments, taking full ownership of his/her own projects.
PayJoy
Data Analytics
91 open roles on Sydicom
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Source: company website
