Fraud Analytics Consultant - Fair Isaac (San Diego, CA)

Main responsibilities include: analyzing and understanding large amounts of historical data to determine suitability for modeling, data clean-up and filtering, pattern identification and feature extraction.

Job overview/responsibilities:
The chosen candidate will be part of a talented team designing, developing, and deploying state-of-the-art, data-driven predictive models to solve business problems using the latest technologies in neural networks, machine learning, statistical modeling, pattern recognition, and artificial intelligence.

Main responsibilities include: analyzing and understanding large amounts of historical data to determine suitability for modeling, data clean-up and filtering, pattern identification and feature extraction, feature (variable) selection, experimenting with different types of algorithms and modeling techniques, analyzing performance, preparing model reports for communication with internal and external clients, participating in pre-sales, and providing post implementation support.

The main functions of this job lie in the areas of data analysis, algorithm and modeling technique development, and client interaction.

• Data analysis: Perform data clean-up and basic statistical analyses and develop an in-depth understanding of large datasets; modify and use various techniques for filtering, pattern identification, feature extraction, and variable selection.
• Algorithms and modeling techniques: Experiment with different types of algorithms and modeling techniques; develop prototypes of promising methods and conduct performance studies; create commercial-quality implementations of algorithms and techniques for use in analysis and production.
• Client interactions: Prepare model reports for communication with internal and external clients; participate in pre-sales meetings and calls; and provide post-implementation product support.

Requires a Bachelor's Degree in a science or engineering field with 3 or more years of relevant industry experience or Master's of Science in a science or engineering field. Industry and academic experience will include employing neural networks, machine learning, mathematical/statistical modeling, pattern recognition, or data mining/data analysis on real world problems. Extensive experience with Unix and at least one major programming language such as C or Java.

Prefer PhD in a science or engineering field. Prefer domain knowledge in the telecommunications industry.

The successful candidate will have a strong background in at least two of the following areas:

• Software skills: Strong UNIX background and experience in Perl, C, C++, or Java. Experience with team-based development environment. Familiarity with basic software design principles and coding standards and best practices; ability and interest to acquire further expertise in this area and lead efforts to develop this practice.
• Large dataset analysis: Experience with analysis of large datasets. Strong skills in scripting languages such as Perl; knowledge of data-cleaning techniques and their uses on large datasets. Experience in performing basic statistical analyses leading to the understanding of the structure of the data.
• Probability/statistics/machine learning: Good working knowledge of several of the following: Bayesian networks, PCA, independent component analysis, linear and logistic regressions, inference, estimation, experimental design, neural networks, SVM.
• Financial services industry: Work/research experience in credit/fraud risk management as related to, e.g., mortgage fraud, credit/debit transaction fraud, identity theft, application fraud. Understanding of financial principles and analysis, familiarity with types of risk management issues in the financial services industry.

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