Career Opportunities in Statistical Computing

1. Career Opportunities in Statistical Computing Robert N. Rodriguez Director, Statistical Research & Development Bob.Rodriguez@sas.com Workshop for Chairs of Programs in Statistics and Biostatistics August 2, 2008

2. Two Perspectives on Careers in Statistical Computing • Software development opportunities at SAS • Emerging opportunities in business SAS Cary Campus Advanced Analytics R&D ~100 Ph.D. developers in statistics, forecasting, data mining, operations research, and numerical analysis

3. SAS Global Reach & Local PresenceConnecting with Customers • More than 400 offices globally in 51 countries • 10,110 employees • 4.5 million users worldwide • Approx. 40,000 sites • 109 countries • Hundreds of local user groups globally

4. 2007 Worldwide ResultsBy Industry Services11% Retail4% Other2% Financial Services42% Manufacturing6% Healthcare & Life Sciences8% Communications 8% Government14% Education3% Energy & Utilities2%

5. What’s Involved in Producing Statistical Software? • Listening to customers • Keeping up with advances in statistical methodology • Designing, writing, testing code • Writing user documentation • Providing technical support • Consulting with customers • Presenting to customers Statistical software testers Cheryl LeSaint and Yu Liang

6. Where Do Statisticians Contribute at SAS? • Software development • 30 developers (Ph.D.) • Software testing • 20+ testers (M.S. and Ph.D.) • Documentation • Technical support • 15+ statisticians (M.S. and Ph.D.) • Education • 12 statisticians (M.S. and Ph.D.) • Marketing and consulting Statistical software developers Randy Tobias and Pushpal Mukhopadhyay

7. What Drives Statistical Software Development?

8. Where Are We Growing? ~20 new specialist positions in development and testing

9. What We Look for in Statistical Software Developers • Ph.D. in statistics, biostatistics, applied math, … • Specialization in one of the target areas • In-depth knowledge of computational techniques • Professional programming skills (hard to find!) • Ability to write large, complex programs in C (not the same as writing programs in SAS, Matlab, S-PLUS, or R) • Developed through on-the-job mentoring • Motivation • Challenged by creating software that moves new methods into practice and helps customers solve problems

10. What We Look for in Statistical Software Testers • M.S. or Ph.D. in statistics, biostatistics, … • Graduate coursework in several target areas • Knowledge of applications and computational methods • Skills • Ability to verify computations through validation programs written in SAS, SAS/IML, SAS macro • Ability to communicate effectively with other testers and developers • Motivation • Challenged by setting and meeting high standards of accuracy and performance that exceed customer expectations

11. Opportunities for Graduate Students • SAS Summer Fellowship in Statistical Computing • opportunity to experience a professional software development environment • competitive; covers stipend, living expenses • announced in December Amstat News • Permanent Positions • JSM Placement Service • Job listings at www.sas.com/jobs Guixian Lin (2008 Fellow, U of Illinois) and Robert Cohen (SAS)

12. The Business Landscape Data Flood Data-Based Decisions

13. Statistical Computing in Customer Environments • Data planning • Design of surveys, experiments, clinical trials, … • Data access and management • Disparate data sources and poor data quality undermine analysis • Databases, data warehouses are controlled by IT (not analysts) • Statisticians need better skills to participate • Data preparation • Getting the data into analysis-ready form (“70% of the effort”) • Analysis • Reporting • Graphics, web pages, FDA submissions, …

14. Emerging Opportunities in Statistical Computing:Analytical Software Solutions • Integrated solutions for key business problems • Developed by interdisciplinary teams • industry experience • software skills (SAS, Java, database, user interface) • statistical computing skills • expertise in formulating and building statistical models • Examples • monitoring for credit card fraud • credit scoring • customer retention and marketing automation • risk analysis (credit, market, and operational) • web analytics • warranty analysis

15. Emerging Opportunities in Statistical Computing:Modeling in Financial and Retail Industries • Expertise in formulating business problems statistically • Computational skills in predictive model-building, forecasting, and optimization • Examples • survival models for customer lifetime value • predictive models for repayment behavior • forecasting demand for store items • experimental design for optimizing response in direct marketing • Helpful articles for students • Kahn, “The Practice and Culture of Statistics in Financial Services”, Sept 2006 Amstat News • DeVeaux and Ungar, “Careers in Data Mining”, Sept 2007 Amstat News

16. Emerging Opportunities in Statistical Computing:Business Analytics • Enterprise-wide decision-making based on corporate data using statistical modeling, forecasting, data mining, and optimization • Davenport and Harris (2007) describe companies that use analytics to drive performance and value • New professional master’s program in advanced analytics at North Carolina State University http://analytics.ncsu.edu Davenport and Harris (2007), Competing on Analytics,Harvard Business School Press