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Career Opportunities in Statistical Computing. Two Perspectives on Careers in Statistical Computing 1.Software development opportunities at SAS 2.Emerging.

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Presentation on theme: "Career Opportunities in Statistical Computing. Two Perspectives on Careers in Statistical Computing 1.Software development opportunities at SAS 2.Emerging."— Presentation transcript:

1 Career Opportunities in Statistical Computing

2 Two Perspectives on Careers in Statistical Computing 1.Software development opportunities at SAS 2.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 Presence Connecting 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 Services 11% Financial Services 42% Retail 4% Other 2% Manufacturing 6% Healthcare & Life Sciences 8% Government 14% Energy & Utilities 2% Education 3% Communications 8% 2007 Worldwide Results By Industry

5 What’s Involved in Producing Statistical Software? 1.Listening to customers 2.Keeping up with advances in statistical methodology 3.Designing, writing, testing code 4.Writing user documentation 5.Providing technical support 6.Consulting with customers 7.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? Customer ProblemsExamples of Development Directions Complex data Highly flexible models, Bayesian models, methods for model selection and validation Missing dataMultiple imputation Messy dataOutlier detection, robust methods Planned data Survey methods, sample size computation, design of experiments Unexplored dataGraphical methods Large data Scalable algorithms, parallel processing, distributed computing

8 Where Are We Growing? Mixed modelingNonparametric regression Bayesian modelingReliability data analysis Bayesian econometricsStatistical graphics Statistical process controlSurvey data analysis Marketing research methodsSurvival data analysis Data mining, machine learningPredictive modeling ~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 Guixian Lin (2008 Fellow, U of Illinois) and Robert Cohen (SAS)

12 Data Flood Data-Based Decisions The Business Landscape

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 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 Emerging Opportunities in Statistical Computing: Modeling in Financial and Retail Industries


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