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Radial is a global leader in ecommerce, employing dynamic individuals to bring renowned brands to consumers via technology solutions, fulfillment, and customer care. As a Radial employee, you'll be contributing to the future of the retail industry and are encouraged to forge your own career path, in a critical and enjoyable work environment.
Radial has an open position for a Data Scientist. This role entails the identification, development, and optimization of Radial's fraud risk technology for better fraud risk decisioning. Data scientist will work with various partners to implement key fraud risk mechanisms and cooperate with the fraud department to oversee the error-proofing tools for all incoming orders.
The data scientist will be responsible for managing machine learning model life cycle and conducting exploratory data analysis. They will contribute in reducing the need for manual, repetitive processes and collaborate with technology teams for model deployment and monitoring. The data scientist will also work closely with other data scientists and team executives to develop a client protection data strategy, ensuring real-time data availability and client-level view generation for intelligent fraud decisions.
Applicants need strong programming skills, specifically in SQL, Python, Tableau and Microsoft 365 Tools. They should have knowledge of Machine learning, feature engineering and model deployment processes. They should have sound analytical, organizational and communication skills and have a master’s degree in a quantitative discipline with 6+ years of experience in data science. Fraud experience is a plus.
This is a remote job and does not require travel. Radial is an equal opportunity employer providing a reasonable accommodation for job seekers, including individuals with disabilities who may not be able to apply online.
This job offer was originally published on Jobicy
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This job offer summary has been generated using automated technology. While we strive for accuracy, it may not always fully capture the nuances and details of the original job posting. We recommend reviewing the complete job listing before making any decisions or applications.
