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Analytical Modeling Staff Scientist-11001127

          

analyze customer data and build high-end analytical models for solving high-value business problems, such as credit card fraud, credit risk, network security, tax fraud detection, and revenue and collections optimization.

SAS Company: SAS
Location: San Diego, CA
Web: www.sas.com

_Contact_:
Apply at www.sas.com/jobs/USjobs/

search for job number 11001127

Description
You will work from the SAS office in San Diego, California. As a member of the Financial Services Modeling team, you will analyze customer data and build high-end analytical models for solving high-value business problems, such as credit card fraud, credit risk, network security, tax fraud detection, and revenue and collections optimization.

Your duties will include analyzing large volumes of customer data for accuracy and consistency, as well as novel and useful information; building high-end analytic models for problems, such as credit card fraud detection; interacting with customers from the data analysis stage to the final report presentation and assisting in technical sales support as needed; building new variables by interpreting underlying data in novel ways; improving modeling techniques to improve model performance; maintaining and improving 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; and performing other duties as assigned.

Qualifications

Essential

  • Master's degree in statistics, mathematics, computer science, engineering, or the physical sciences.
  • Experience analyzing data and building analytical models.
  • Ability to work from the SAS office located in San Diego, California.
  • Excellent programming skills with knowledge of multiple operating systems, scripting languages, and the ability to deal with very large volumes of data.
Additional
  • Excellent written and verbal communication skills.
  • 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.
  • Ability to think analytically, write and edit technical material, and relate statistical concepts and applications to statistical and nonstatistical users.
  • Ability to work independently and with a team environment.
  • Ability to travel as business requirements dictate.
Preferences
  • Ph.D. in applied statistics, mathematics, computer science, engineering, or the physical sciences.
  • Experience with credit risk modeling, financial services/credit card research, and/or dealing with large-scale transaction data.
  • Experience using the SAS System or a comparable analytical software.
  • Industry or academic experience in mathematical/statistical modeling, pattern recognition, or data mining/data analysis.
  • Extensive experience specifying and building advanced statistical solutions for the financial services and related industries.
  • Excellent knowledge of financial services fraud and credit risk issues.
  • Thorough knowledge of supervised and unsupervised modeling techniques.
  • Good programming skills and knowledge of SAS, Java, or other analytical programming languages.
  • Ability to translate model performance to financial benefit for the business by incorporating knowledge of customer business practices.
  • Ability to deal with large volumes of transaction data in UNIX-based environments.
Additional Information
The levels of this position will be determined based upon the applicant's education, skills and experience.

Resumes may be considered in the order in which 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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