Data Scientist, Fraud Modeling
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Position Location = San Diego, CA - SAS Regional Office.
You will work from the SAS office in San Diego, California. As a member of the analytics team, you will analyze customer data and build high-end analytical models for solving high-value business problems, such as credit and debit card fraud, online banking fraud, credit risk, network security, etc. Duties include:
* Processing and analyzing large volumes of (customer) data
* Building predictive models with advanced machine learning algorithms such as Neural Networks, Decision Trees, Boosting/Ensemble methods, Clustering, Online learning, etc
* Interacting with customers from the data analysis stage to the final report presentation
* Assisting in technical sales support as needed
* Constantly innovating by building new variables; improving modeling techniques to boost model performance; maintaining and refining the processes and procedures for building high-end analytic modeling solutions
* Writing coherent reports and making presentations on high-end analytical projects
* Leading a group of scientists through project completion as needed
* Master's degree in statistics, mathematics, computer science, engineering, or the physical sciences
* At least 2 years related experience such as analyzing data and/or building analytical models; in either an academic or professional setting
* Good programming skills with knowledge of multiple operating systems (e.g. Unix/Linux), scripting languages (e.g. Bash, Perl, Python) and the ability to deal with very large volumes of data
* Ability to communicate with people of various technical and business backgrounds, including the ability to explain difficult technical concepts in simple terms to business users
* Thorough knowledge of at least some supervised and unsupervised modeling techniques such as Logistic/Linear Regression, SVMs, Neural Networks / Deep Networks, Boosting/Ensemble methods, Decision Trees, Clustering, etc
* Excellent written and verbal communication skills
* Ability to think analytically, write and edit technical material, and relate statistical concepts and applications to technical and business users
* Ability to work independently and with a team environment
* Ability to travel as business requirements dictate
* Ph.D. in applied statistics, mathematics, computer science, engineering, or the physical sciences
* Industry experience in mathematical/statistical modeling, pattern recognition, or data mining/data analysis
* Extensive experience specifying and building advanced analytic solutions for the financial services and related industries with large-scale transaction data
* Extensive experience in data management, deployment and product support for advanced analytic solutions
* Excellent programming skills and knowledge of SAS and scripting languages
* Ability to translate model performance to financial benefit for the business by incorporating knowledge of customer business practices.
SAS looks not only for the right skills, but also for a cultural fit. We seek colleagues who will contribute to the unique culture that makes SAS such a great place to work. We look for the total candidate: technical skills, culture fit, relationship skills, problem solvers, good communicators and, of course, innovators. Candidates must be ready to make an impact.
SAS is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status or any other characteristic protected by law.
The level of this position will be determined based on the applicant's education, skills and experience.
Resumes may be considered in the order they are received.
SAS employees performing certain job functions may require access to technology or software subject to export or import regulations. To comply with these regulations, SAS may obtain nationality or citizenship information from applicants for employment. SAS collects this information solely for trade law compliance purposes and does not use it to discriminate unfairly in the hiring process.
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