Fraud Analytics Professional - BFS

Profile:Fraud and Risk Analytics

Must-have skills : SAS,R, SQL, Machine Learning, Predictive Modelling

Essential Job Responsibilities :

- Build analytic models using a variety of machine learning and advance analytics techniques such as logistic regression, decision tree, Neural Networksand pattern recognition

- Assist in the creation of Fiserv Fraud Consortium models by working with customers to identify data sourcesand collecting the data in a useable format for modeling

- Analyze and understand large amounts of historical fraud and risk data to determine suitability for use in models and then build and test those models

- Work with technical and development teams to deploy models

- Build Model documentation and Performance Reports for all models

- Sales support for Fraud products by providing analytic expertise in presentations to clients as needed

- Carry out end to end delivery including requirements, solution design, analysis, modeling and business recommendation

- Must have a solid understanding of business drivers and how data is used to inform and drive decisions and behaviors

- Responsible for conducting complex analysis using statistical and other quantitative techniques

- Ensure delivery of the project as per timelines

- Associate would work closely with stakeholder to develop models and analytics solutions as applicable

- Manage communication and stakeholder expectations

- Hands on experience on statistical tools like SAS & R

- Results oriented with excellent communication and interpersonal skills

The above statements are intended only to describe the general nature of the job, and should not be construed as an all-inclusive list of position responsibilities.

Education:

- Bachelor's degree in Computer Science, Machine Learning, Statistics, Engineering, Mathematics or Information Technology

- M.Sc. Statistics/ B.Sc Statistics

Job Related Experience:

- Minimum 3 - 6 years of experience working in Fraud analytics

- Minimum 3 years of experience in building predictive models

- Proven experience in building and deploying models

- Background and experience in machine learning, statistics, mathematics or engineering

- Experience with SAS, JAVA, R, or Python (experience with more than one is preferred)

- Proven experience with large sets of data

- Experience working with banking, credit card, debit card, payment fraud, bust-out, first party fraud or AML risk modeling

Preferred Qualifications:

- Master's or PhD degree

Additional Skills/Knowledge:

- Excellent communication skills, both verbal and written

- Strong analytical and problem solving skills

- Expertise in Microsoft Word, Microsoft PowerPoint, Microsoft Excel