Data Modeler Analyst

Job Description

Description - Experian’s Campus Recruiting team is looking for a Data Modeling Analyst to join the Fraud and Identity Solutions (FIS) Analytics team. This person would execute analytical engagements for Experian clients, primarily involving credit score analysis for account acquisition, applicant decisioning, and account management. 

Key Responsibilities:- Execute analytical projects, at times leading analysis project from solution design and data integrity evaluation through solution documentation and implementation.- Collaborate with internal and external clients to determine the appropriate analysis parameters and performance measures to be applied as well as requirements for decision tools and strategy implementation and monitoring.- Interpret results of analyses, identify trends and issues and recommend alternatives to support business objectives.- Communicate with and deliver presentations to end users on analysis results.- Produce implementation plans and participate in audits to support the implementation of statistical models and other decision tools.- Support field sales force and marketing organization by providing analytical expertise.- Support the development of new analytical and data products and services, and the enhancement of current processes and offerings. Qualifications- Must be a recent college graduate (i.e. 2016, 2017)- Minimum of 2 years of statistical modeling / data analysis experience or course work including data manipulation experience preferably using SAS- Advanced degree, preferably in Statistics or Mathematics- Expert analytical skills to evaluate understand and interpret data from both an internal and client perspective- Demonstrated ability to communicate ideas and analysis results effectively both verbally and in writing to both a technical and non-technical audience- Excellent communication, organization, interpersonal and presentation skills- Strong knowledge of Unix and Microsoft office products- Financial services industry experience preferred