Fraud Risk Analytics Manager

Overview

At Early Warning, we’ve powered and protected the U.S. financial system for over thirty years with cutting-edge solutions like Zelle®, Paze℠, and so much more. As a trusted name in payments, we partner with thousands of institutions to increase access to financial services and protect transactions for hundreds of millions of consumers and small businesses.

Positions located in Scottsdale, San Francisco, Chicago, or New York follow a hybrid work model to allow for a more collaborative working environment.

Candidates responding to this posting must independently possess the eligibility to work in the United States, for any employer, at the date of hire. This position is ineligible for employment Visa sponsorship.

• Execute analysis for identifying and consistently categorizing the root cause of fraud events observed or attempted in the network.
• Develop fraud analysis to quickly source, analyze and deduce root cause consistently and accurately across the network.
• Be able to effectively articulate and influence within the organization and those at the participating financial institutions on actions recommended, based on insights derived from advanced analytics.
• Execution of a discipline to identify exposed network credentials, social tokens and payment information; categorize the risk of the same and apply appropriate risk treatments to those data elements.
• Support the company’s commitment to protect the integrity and confidentiality of systems and data.

• Education and/or experience typically obtained through completion of a Bachelor’s Degree.
• Minimum 5 or more years of progressive experience in advanced data analytical skills.
• Minimum 3 years of SAS/SQL/R/Python or comparable analytic coding experience.
• Proven track record of converting concepts to analysis to cases for change
• Exceptional written and verbal communication skills.
• Experience with digital banking, payments and/or payment networks.
• Background and drug screen.

• Post Graduate degree – Math / Statistics / Data Science / MBA.
• SAS/SQL/R/Python or comparable analytic coding certification.
• Experience with two or more financial institutions in the area of fraud risk management.
• Experience in both consumer and commercial banking applications.
• Additional related education and/or experience preferred.

The above job description is not intended to be an all-inclusive list of duties and standards of the position. Incumbents will follow instructions and perform other related duties as assigned by their supervisor.

Working conditions consist of a normal office environment. Work is primarily sedentary and requires extensive use of a computer and involves sitting for periods of approximately four hours. Work may require occasional standing, walking, kneeling, and reaching. Must be able to lift 10 pounds occasionally and/or negligible amount of force frequently. Requires visual acuity and dexterity to view, prepare, and manipulate documents and office equipment including personal computers.

Employee must be able to perform essential functions and physical requirements of position with or without reasonable accommodation.

The base pay scale for this position in:
Phoenix, AZ in USD per year is: $104,000 - $130,000.

Additionally, candidates are eligible for a discretionary incentive plan and benefits.

This pay scale is subject to change and is not necessarily reflective of actual compensation that may be earned, nor a promise of any specific pay for any specific candidate, which is always dependent on legitimate factors considered at the time of job offer. Early Warning Services takes into consideration a variety of factors when determining a competitive salary offer, including, but not limited to, the job scope, market rates and geographic location of a position, candidate’s education, experience, training, and specialized skills or certification(s) in relation to the job requirements and compared with internal equity (peers).

The business actively supports and reviews wage equity to ensure that pay decisions are not based on gender, race, national origin, or any other protected…