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Job Title:	Fraud Detection Analyst
Job Number:	JO-2101-6706
Location:	Pennsylvania - King of Prussia
Employee Group:	Regular
Shift:	Day
Travel:	0%
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Fraud Detection Analyst We are Radial, the leader in Omnichannel commerce technologies and operations. The Radial Fraud Team using cutting-edge technology and advanced machine learning models to help eCommerce companies detect and prevent fraud. We are looking for a Fraud Detection Analyst to join a team of best-in class analysts. Radial enables our clients to profitably exceed customer expectations by taking on the complexity of their Omnichannel retail business and transforming it into a seamlessly orchestrated customer experience. To bring order to ordering. To make fulfillment more fulfilling. To keep commerce clicking. When we partner with our clients to execute their orders, payments, fulfillment, or customer care, our clients’ promises become ours. Learn more: http://www.radial.com/about/company#sthash.e8Z9tltu.dpuf As a Fraud Detection Analyst, you will be responsible for identifying new and unique ways of finding and capturing ecommerce fraud trends. This role is a terrific opportunity to work in the dynamic space of e-commerce fraud to solve complex problems using big data technologies. Reporting to the Fraud Advanced Analytics Manager, this role will work alongside a team of analysts and data scientists. The department uses a wide variety of systems, tools and algorithms to evaluate data, execute their investigation and develop an efficient analytical workflow including machine learning models, velocity detection and behavior analytics. Fraud Detection Analysts work cross-functionally with other teams to identify and communicate fraud trends and/or anomalies they uncover. He or she will be expected to effectively succeed at the day-to-day workflow of an analyst. This role requires the candidate to be self-motivated with exceptional attention to detail. The Fraud Review analyst will be held to meeting department goals. We’re seeking technology minded individuals to be able to come in with a strong skill set and a readiness to learn and grow. Consider joining this competitive, innovating team with a prestigious east-coast e-commerce company. Consider learning about fraud in the e-commerce world and working to protect our clients and consumers. Key Areas of Responsibility: Research, design and develop new business rules, data models and processes, working closely with machine learning experts and technology teams to test and implement. Conduct robust data analysis to identify fraud trends Design rules and data models to proactively mitigate fraud and reduce customer impact Actively participate in identification and investigation of relational data and its financial impact Partner with the Fraud operations team to educate and effectively communicate tending issue and discoveries Work with 3rd party vendors to enhance analytics and improve data capabilities for analysis Evaluate, resolve, and respond to fraud impacts for clients via analysis Support the Fraud Analytics, Order Review, and Chargeback teams, and work closely with other internal and external technology groups. Qualifications: Strong data and analytical skills Self-starter with exceptional attention to detail Knowledge and/or experience with Oracle and/or SQL Must be organized and be able to prioritize work Proven ability to meet or exceed goals in a metrics driven organization Effectively manage cross-functional relationships Good communication skills Proficient with Microsoft Office Suite Willingness and flexibility to work all shifts including weekends and overtime Bachelors’ Degree or Equivalent Desired Qualifications: Direct experience with e-commerce or bank fraud Experience in R and Python Bachelor’s degree in Criminal Justice, Economics, Statistics, or Business-related field or equivalent experience Location: Option to work remote or based out of Radial’s headquarters in King of Prussia

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Radial is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.

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