The Effective Industrial Statistician: Necessary Knowledge and Skills

1. The Effective Industrial Statistician: Necessary Knowledge and Skills William Q. Meeker Department of Statistics Center for Nondestructive Evaluation Iowa State University wqmeeker@IAstate.edu QPRC 2009 IBM, Yorktown Heights, NY 3 June 2009

2. Overview • Evolution of the Industrial Statistician • What Applications do Industrial Statisticians See? • What Tools Does an Industrial Statistician Need? • Statistics Graduate Program • Personality of a Statistician • Other Skills • Internships for Statistics Graduate Students • Concluding Remarks

3. Evolution of the Industrial Statistician • Snapshot at 1975 • Snapshot today Can we extrapolate into the future?

4. Typical Tasks for an Industrial Statisticians in 1975 • Design experiments • Modeling and analysis of data (including general number crunching) • Interpret results • Training • Conduct research for nonstandard problems Many US statisticians worked in a statistics group within the company, e.g.: Allied Chemical Amoco Bell Labs DuPont GE GM IBM Kodak Pratt and Whitney Proctor and Gamble RCA Shell How many remain?

5. The Industrial Statistician’s Environment in 2007 • Modern statistical software can do an effective job of modeling and analysis of data and designing simple experiments, and readily accessible to all • Statisticians tend to get involved in more complicated interdisciplinary problems • Training customers (perhaps increased due to six-sigma) • Customers do not want pay for research (or even technical reports) • Fewer “Statistics Groups.” Most statisticians integrated into product development or manufacturing groups. • More need to be proactive, rather than reactive

6. What Applications do Industrial Statisticians See? • Product quality and manufacturing • Product design (including reliability) • Process design (including reliability) • Process monitoring • Warranty and other reliability field data • Marketing • Financial services • Environmental issues • Many other business processes

7. Some Statistical Tools Needed by Industrial Statisticians • Bayesian Statistics • Categorical data methods • Censored data analysis • Design of experiments • Graphical methods • Image analysis • Multivariate analysis • Optimization • Regression analysis (linear and nonlinear) • Reliability theory • Response surface methods • Simulation • Spatial statistics • Statistical computing and programming • Survey sampling • Time series analysis

8. What Should Be in a Statistics Graduate Program Core? • At least two semesters of mathematical statistics (probability and statistics, perhaps stochastic processes). • At least two semesters of statistical modeling and methods with applications (linear and nonlinear regression and maximum likelihood) • SAS and R (or S-PLUS) use and programming, plus exposure to Excel, JMP or MINITAB • A creative project, thesis, and/or a course in consulting, and corresponding internship experience.

9. Which Statistical Electives? • Design of experiments • Statistical methods for reliability • Statistical methods for quality • Others according to interests • Important: While pursuing a graduate degree, you cannot learn everything that you will need. • The purpose of education is to learn how to learn. • Statisticians should be prepared to learn (and in some cases develop) new methods to meet the needs of the client (through continuing education and self-study). • In some cases statisticians may need to suggest hiring an outside consultant for special problems

10. Personality of a Statistician • The joke:A statistician is someone who loves to work with numbers but who did not have the personality to be an accountant. • The reality: • Today’s Industrial Statistician works almost exclusively in collaborations with scientists, engineers, managers, and other non-statisticians. • Interpersonal skills are extremely important Unknown

11. Other Skills of an Effective Industrial Statistician • Communications skills • Written • Listening • Presentation • Interpersonal • Leadership skills (needed to be proactive) • Knowledge of relevant subject matter areas, e.g.: • Biology • Business and Finance • Chemistry • Engineering • Genetics • Physics • Flexibility and adaptability

12. Communications with Clients • Statisticians should strive to learn some of the scientific/engineering background in the area of their client. • It is imperative that the statistician learn and use the language, notation, and traditions of the client’s area.

13. Thanks to • Mentors at GE • Mentors at ISU • Colleagues and supervisors at Bell Labs • My students • My understanding family • Interesting/Helpful clients and access to real problems

14. Internships for Statistics Graduate Students • Valuable experiences possible (not the same as working in a university consulting lab) • Projects may lead to professional society presentations or publications • Effectiveness is highly dependent on the kind of project and attention of the mentor • Exposure to the business environment will provide perspective in subsequent years of study and for the eventual job search

15. Concluding Remarks • “Industrial Statistics” is nearly as broad as the Statistics discipline itself. • In spite of the new ability for others to do their own data analysis, there will continue to be healthy demand for statisticians in industry (but in somewhat different roles). • The truly effective industrial statistician will be knowledgeable about the company’s business and the science and engineering used there, broad in perspective, and proactive in their work.