Statistics

Quantitative Risk Analyst—PayPal

The Quantitative Risk Analyst will report to the Manager of Risk Detection and work with a cross functional team of quantitative and business analysts. Primary job responsibilities are to develop analytical processes (scripts and computer programs) to be used for proactively protecting PayPal and PayPal users from fraudulent activity. This role offers a unique opportunity to innovate and improve PayPal's fraud prevention and detection technologies, and have a direct impact on the PayPal community of users.

• Analyze and segment data from multiple sources to find patterns that can be used to detect fraud activity in our system.
• Applying analytical skills to predict and manage risk associated with new online payment products. Working with product to provide risk expertise to contribute in product design.
• Applying statistical skills to predict and control risk associated with new accounts, and to combat credit card fraud, bank account fraud, identity theft fraud, spoof fraud etc.
• Analyzing data covering a wide range of information from user profile to transaction history. Identifying new fraud patterns through data mining.
• Work with rules operations, and modeling teams to communicate best practices for combating nascent fraud trends.
• Analyze detection method performance and communicate findings to management.

Job Requirements

• B.S. (MS/PhD preferred) in Statistics, Mathematics, Computer Science, Operations Research or other quantitative, engineering area is required.
• At least 3 years of experience in statistical data analysis and predictive modeling using SAS or similar statistical tools
• Experience in analyzing massive and highly complex data sets, performing ad-hoc analysis and data manipulation (SQL, database programming)
• A great communicator and team player, strong project and people skills, and superb attention to details
• A can-do attitude to carry out a project from scratch to production on a timely basis
• A passion for excellence in analytics
• Familiarity with UNIX is strongly preferred
• Previous experience handling data analysis for large banks or financial services companies is preferred.

Contact Michael Murff for further information (mmurff@ebay.com).

 

Statistician/Computer Professional—University of Utah

The University of Utah, Department of Pediatrics, Division of Critical Care is looking for a full-time statistician with an MS degree or equivalent experience. The individual will provide statistical support for research projects in the areas of study design and analysis plan, calculation of sample size/power, statistical analysis programming, and interpretation of results. To apply, please visit http://www.hr.utah.edu/careers and search for job posting number #30866.

Primary duties to include:

1. Assisting in the planning of research including: study design, analysis plan, sample size/power calculations, and protocol
   development.
2. Statistical analysis programming, including completing descriptive and inferential analyses and the interpretation of results.
3. Data cleaning, interaction with data management, and creation of standard or analysis variables.
4. The presentation of interim and final results in written summary including tables and figures, as well as a written description of
   methods and statistical analyses used.
5. Help in the preparation and submission of grants, manuscripts, and presentations.
6. Direct communication with study investigators and other clinicians regarding statistical issues including interpretation of study data.
7. Conforming to and maintaining statistical standards at the Center.

This position requires a solid knowledge of standard statistical analysis procedures with a minimum of a Bachelors degree with 2 years work related experience. Masters' degree is preferred. Working knowledge of at least one statistical programming language such as SAS or R, and general database management or programming skills are required. An ability to work on several projects simultaneously, and manage deadlines is essential. Good communication skills, including the ability to clearly explain statistical techniques and present data results are required. Strong familiarity with SAS and experience in clinical trials is preferred.