Description:

Required Skills and Experience:

- 2.6 to 4 years of analytics experience as a statistical modeler or data scientist in the identity and fraud, credit risk, telecommunications, financial services, payment, ecommerce, B2B or B2C, marketing, insurance, or security analytics arena

- Strong quantitative analytical experience, including hands-on and background in using statistics, regression modeling, neural networks, decision trees, random forest, support vector machine, kernel based methods, clustering and similar methods to detect and prevent fraud, risk and other behaviors

- Master's degree or higher in Mathematics, Computer Science, Engineering, Operations Research, Statistics or other related discipline

- Strong consultative acumen and ability to understand complex analytical solutions

- Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner

- Ability to create new ideas for analytical solutions to address customer's business issues

Preferred Skills and Experience:

- Proficient in SAS programming including SAS Macro

- SAS Certification preferred

- INFORMS CAPS certification a plus

- Experience with R, SPSS, MATLAB and other packages preferred

- Familiarity with programming languages such as Perl, Python (or similar scripting language), Java, C/C++ a plus

- Experience with state of the art machine learning algorithms such as neural networks, support vector machines, ensemble models, kernel-based methods genetic algorithms etc. preferred

- Experience working with very large datasets, knowledge of distributed computing tools (MapReduce, Hive, PIG, FLUME, SQL, Apache Mahout etc.) a plus

- Extensive knowledge in fraud prevention methods and detection tools a plus

- Strong knowledge of credit bureau data and business problems in financial services and/or telecommunications a plus

- Knowledge of optimization and design of experiments techniques a plus

- Experience working in UNIX environment preferred

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