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Radial offers a unique opportunity for a skilled individual to develop, maintain, evaluate, and enhance both real-time and offline Machine Learning Models. This role involves a proactive approach to identifying new methods and processes that can increase revenue while reducing costs and operational friction. The successful candidate will be closely working with the Technology team to build and improve Radial's Data Science framework.
The role entails supporting the company's transformation into a cloud-based infrastructure, thereby enhancing the capabilities of Radial's Data Science and Machine Learning spectrum. Carrying out ROI analysis, executing business case justifications, and reviewing new vendors or partners are integral to the position. The appointee will also be expected to regularly report on the status and planned next steps for all Fraud Risk Technology Partners.
Acting as the primary point of contact for both off-shore (IDC) and in-house data scientist resources, the role requires flexible availability during both on and off hours to ensure smooth communication and operation. An option for telecommuting is provided, allowing for a more flexible work environment.
An ideal candidate must hold a Master's degree, or foreign equivalent, in Data Science, Data Analytics, or a closely related quantitative field, coupled with at least two years of professional experience in the job offered or two years in a related occupation within Business/Data Analytics, Statistics, or similar fields.
Key skills required for the position include proficiency in:
The company acknowledges that any suitable combination of education, training, and experience may be acceptable for a candidate showing versatility and capability in related fields.
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.