copyright © 2006, sas institute inc. all rights reserved. career opportunities in statistical...

Copyright © 2006, SAS Institute Inc. All rights reserved.

Career Opportunities in Statistical ComputingRobert N. Rodriguez Director, Statistical Research & [email protected]

Workshop for Chairs of Programs in Statistics and BiostatisticsAugust 2, 2008


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


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


Services11%

Financial Services42%

Retail4%

Other2%

Manufacturing6%

Healthcare & Life Sciences

8%

Government14%

Energy & Utilities2%

Education3%

Communications 8%

2007 Worldwide ResultsBy Industry


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 customersStatistical software testers Cheryl LeSaint and Yu Liang


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


What Drives Statistical Software Development?

Customer Problems Examples of 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

Large data Scalable algorithms, parallel processing, distributed computing


Where Are We Growing?

Mixed modeling Nonparametric regression

Bayesian modeling Reliability data analysis

Bayesian econometrics Statistical graphics

Statistical process control Survey data analysis

Marketing research methods Survival data analysis

Data mining, machine learning Predictive modeling

~20 new specialist positions in development and testing


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


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


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)


Data Flood

Data-Based Decisions

The Business Landscape


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, …


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


• 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


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

copyright © 2006, sas institute inc. all rights reserved. career opportunities in statistical...

Documents

sas institute

robert cohen sas slide

data access

data warehouses

data preparation

sas macro ability

testing slide

data survey methods