As a Data Scientist and 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.
- 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.
- Master's degree in statistics, mathematics, computer science, engineering, the physical sciences, or any other quantitative field.
- 2+ years related experience such as analyzing data and/or building analytical models; in either an academic or professional setting.
- Knowledge of multiple operating systems (e.g. Windows, Unix/Linux).
- Proficiency with 1 or more of the following Programming or Scripting languages: R, SAS, Bash, Perl, Python, MatLab.
- 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.
- Ability to manage very large amounts of data.
Additional Skills and Abilities:
- 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 both independently and in 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.
Location: this position will be seated out of our HQ in Cary, NC.
SAS looks not only for the right skills, but also a fit to our core values. We seek colleagues who will contribute to the unique values that makes SAS such a great place to work. We look for the total candidate: technical skills, values fit, relationship skills, problem solvers, good communicators and, of course, innovators. Candidates must be ready to make an impact.
To qualify, applicants must be legally authorized to work in the United States, and should not require, now or in the future, sponsorship for employment visa status. 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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