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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
Outline
Personal perspective at SAS
Opportunities for statisticians in business and
industry
Factors for early success
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
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
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
Data Flood
Data-Based Decisions
Customer Perspective
Learning About Customer
Problems and Data
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, …
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 customersStatistical software testers
Cheryl LeSaint and Yu Liang
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
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
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
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
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
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!
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
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
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
What Drives Statistical Software
Development?
Customer Problems Recent Development Directions
Complex dataHighly flexible models, Bayesian models, methods for model selection and validation
Missing data Multiple imputation
Messy data Outlier detection, robust methods
Planned dataSurvey methods, sample size computation, design of experiments
Unexplored data Graphical methods
Massive dataScalable algorithms, parallel processing, distributed computing
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
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
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