Monday, May 25, 2009

Building a Career in Statistics

by David L. Banks, Department of Statistical Science, Duke University

This article is based on a talk I gave at the JSM 2007 meeting for the ASA Committee and Career Development. But, I should confess at the outset that I have no particular qualifications or any expertise on this topic, aside from having a lot of jobs (which ought to raise questions about my suitability on first place).

Years ago, I was involved in drafting the New Researchers Survival Guide (available at Reading over it again, we were awfully earnest and a bit naïve, but I think it has a lot of value for people who are beginning in academic career. So, I refer new faculty members so that, and, in this article, shall focus on topics that apply to everyone, not just recent PhDs and not just academics.

Although statisticians are relatively homogenous in our training, we have the usual range of talents, personalities, and utility functions. This creates many career paths and many ways to be successful. It also means you can be miserable if you get caught on a path that doesn’t fit your personal strengths and values.

All careers have a stochastic component, so we should look to dynamic programming as a model for continual reappraisal of our situations and ways that may better them. This implies a portfolio analysis perspective: We each have different mix of strengths and weakness, and we should try to adaptively invest our energy in combinations that seem most likely to pay off. Some skills that apply to employment in all sectors are the following

1. Technical Strength.
This is the foundation when you are starting out, but it often becomes less important as you advance. Especially in business and government, one needs breadth more than depth at higher levels.

2. Computational Ability.
Anyone who can do solid statistical programming will never miss a meal. It is a blue chip skill and a way of thinking that has a unique value. But, it is hard to become rich or famous on this alone.

3. Public Speaking.
Every member of the ASA has survived at least 1,5 decades of dull lecturer in school, which is why it amazes me that so many of us have not learned enough from that experience to avoid giving bad talks. Good presentation are key of component of almost any success story, and whatever you can do to build strength in this area will repay of your efforts.

4. Writing.
It is crucial to be able to write clearly, correctly, and briefly. This is a lifelong learning process – anyone who writes well is constantly studying how to write and attending to their process.

5. Social Networking.
This is crucially important, and it sometimes statisticians study it more, while learning less, than those in other field. You need diverse networking; having a lot of friends who work on local asymptotic minimaxity is not as helpful as having friends with complementary strengths.

6. Organization.
This sounds mundane, but it is very hard for a manager to promote you if you are sloppy or slow about paperwork. And the discipline of quick turnaround on such items (phone cells, email, appointments, referee report) helps in other aspects one’s career.

7. Time Management.
Don’t waste time feeling guilty about wasting time, just be efficient when you actually get down to work.

Someone else would probably generate a slightly different list, but these are all key areas to cultivate.

For those who need to stick in their job, there are still ways to advance. Personality counts for a lot. Try to pretend to be happy and productive. Read the newspaper so you have a wealth of conversation topics and aren’t stereotypically dull or narrow. You should avoid doomed projects, those that do not build new professional assets and those for which you are not central. I’d recommend looking for projects that cross division boundaries – it helps to have a broad base of good opinion, and you can build unique collaborations the organization needs. Try to differentiate yourself. Think of at least one of idea a week, but be properly skeptical of its value.

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