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Early Career Success - Part III - PowerPoint Presentation


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Early Career Success - Part III - PowerPoint Presentation

  1. 1. Early Career Success Factors for Statisticians in Business and Industry Robert N. Rodriguez Senior Director, Statistical R & D SAS Institute Pre-JSM Diversity Workshop Joint Statistical Meetings August 1, 2009
  2. 2. Outline  Personal perspective at SAS  Opportunities for statisticians in business and industry  Factors for early success
  3. 3. About SAS  Leader in statistical software used by universities, business, and government  Founded in 1976  Continuous reinvestment in research and development, including 22% of revenue in 2008  11,000 employees, 400 offices globally  Over 45,000 customer sites in 110 countries
  4. 4. SAS Research & Development  1000+ software developers in Cary, Beijing, Pune, ...  Integrated development environment  Millions of lines of C and Java code  Systems for building, documenting, and delivering software SAS Campus Cary, North Carolina
  5. 5. Advanced Analytics Division  Over 100 Ph.D. specialists in statistics, operations research, numerical analysis, …  Software products  SAS/STAT, SAS/ETS, SAS/QC, SAS/OR, SAS/IML, Enterprise Miner, Forecast Server, …  Used by statisticians, researchers, data miners, …  Analytical components for software solutions
  6. 6. Data Flood Data-Based Decisions Customer Perspective
  7. 7. Learning About Customer Problems and Data
  8. 8. Statistical Needs in Corporate Environments: The Five D’s 1. Data planning  Design of surveys, experiments, clinical trials, … 2. Data access and management  Disparate data sources and poor data quality undermine analysis  Databases, data warehouses (controlled by IT, not analysts) 3. Data preparation  Getting the data into analysis-ready form (“80% of the effort”) 4. Data analysis and modeling 5. Delivery of analytical results  User interfaces, graphics, web reports, FDA submissions, …
  9. 9. 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 and training 6. Consulting with customers 7. Presenting to customers Statistical software testers Cheryl LeSaint and Yu Liang
  10. 10. Growth Opportunities for Statisticians 1. Development of analytical solutions  Integrated solutions for business problems  Developed by interdisciplinary teams  industry experience  software development skills  expertise in statistics, data mining, operations research  Examples  fraud detection for banks  credit scoring  customer retention and marketing automation  credit, market, and operational risk analysis  web analytics  warranty analysis
  11. 11. Growth Opportunities for Statisticians (cont’d) 2. Consulting in financial and retail industries  Ability to formulate a business problem with a statistical model  Examples  survival models for customer lifetime value  predictive model for repayment behavior  forecasting demand for store items  experimental design for direct marketing
  12. 12. Early Success Factors: Undergraduate Preparation  How much math do I need for graduate work in statistics?  calculus, linear algebra  statistics is not a branch of mathematics, but you need to be mathematically prepared  What should I major in?  math, statistics, biology, computer science, physics, engineering, economics, psychology, …  What else should I take?  probability and statistics--enough to understand how modern statistics is used to analyze data and solve problems  computer programming  technical writing
  13. 13. Early Success Factors: Undergraduate Preparation Explore statistical careers through internships, StatFests, and summer opportunities North Carolina State Summer Institute for Biostatistics Training Field trip to SAS
  14. 14. Early Career Success Factors: Graduate Training  Apply to the right program in statistics or biostatistics  Where would you like to work when you finish?  Are you interested in academic research and teaching?  Are you interested in business or government?  Talk to faculty and alumni—they’ll be glad to advise you!  Consider an in-demand area of statistics  Survey design and analysis  Econometric modeling  Statistical computing  Become a student membership of ASA!  Internship opportunities in December Amstat News
  15. 15. Early Career Success Factors Interviewing  Research the organization in advance  Ask perceptive questions  Are the staff absorbed in their work?  What brings them back to work year after year?  What is the least satisfying aspect of their work?  What is the most rewarding aspect of their work?  If you are excited about what they are doing, they will be excited about you!
  16. 16. Early Career Success Factors: Where to Start Your Career  Excellence attracts excellence, so look for  group of flourishing statisticians  statisticians valued as problem formulators/solvers  statisticians collaborating with others  senior statisticians serving as mentors and leaders  statisticians active professionally (members of ASA)  Traps to avoid  isolation from other statisticians  limited understanding of what statisticians do (“just run the reports”)  lack of support for professional activity
  17. 17. Early Career Success Factors: Becoming a Prized Professional  Work on your writing skills  Careful motivation  Clear conclusions  Learn to give presentations that anticipate and meet audience needs  Develop special computing skills  Management of large data sets  Advanced statistical programming  Become active in the ASA  Keep learning  Give back to the profession! Effective Writing by H. J. Tichy A best buy at $2.57
  18. 18. Early Career Success Factors for Statisticians in Business and Industry Robert N. Rodriguez Senior Director, Statistical R & D SAS Institute Pre-JSM Diversity Workshop Joint Statistical Meetings August 1, 2009
  19. 19. What Drives Statistical Software Development? Customer Problems Recent Development Directions Complex data Highly flexible models, Bayesian models, methods for model selection and validation Missing data Multiple imputation Messy data Outlier detection, robust methods Planned data Survey methods, sample size computation, design of experiments Unexplored data Graphical methods Massive data Scalable algorithms, parallel processing, distributed computing
  20. 20. What We Look for in Statistical Software Developers  Ph.D. in statistics, biostatistics, applied math, …  Specialization in an area of modern statistics  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
  21. 21. 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
  22. 22. Where Do Statisticians Contribute at SAS?  Software development  50+ developers for statistics and operations research  Software testing  20+ testers  Documentation  Technical support  15+ statisticians  Education  12 statisticians  Marketing and consulting