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Academic Job Search: Materials & Methods

September 13, 2016

A series of posts by Prof. Booty provided some data on successful academic job candidate profiles from the perspective of a single search committee.  I was on the academic job market recently and as a mechanism for managing the anxiety associated with the process, I kept track of the information accessible to me as a candidate.  Over the next couple posts, I’ll share these data and profile the mechanics of many search committees normalized for a single candidate.

Materials & Methods

Sample preparation:

My (long) post-doc was in a glam lab and prior training all done at top tier R1 universities.  Along with a collection of solid publications, I have a CNS pub out of both my post-doc and my grad work, though the post-doc CNS paper was not a sure thing during the application season.  I had a fancy post-doc fellowship but no K99 or other mobile awards.   In short, I fit Prof. Booty’s “successful candidate” profile.

I applied to a little over a hundred positions.  Once my application materials were set, I spent an average of an hour and a half on each application to research the department and university, customize the materials, fill out web forms, and upload documents.

While I’ve mentored a fair number of students, my teaching experience amounted to a handful of guest lectures.  I’ll add that outside of their letters of recommendation, my references did not contact any of the departments I applied to.

Data collection:

I recorded application due dates, application submission dates, requests for letters of recommendation, requests for phone/skype interviews, invitations for on-campus interviews, interview dates, and rejections.

In addition to formal communication, I maintained a simple professional website with an IP tracker.  The site came up on the first page of results for a search on my name and I included the web address in my application materials.  I checked the hits at least a few times a week and in the case where the IP address mapped to a university, I recorded the first and second visit dates.  If I applied to multiple positions at a university, I assigned the hit to the earliest application date unless there was a good indication of department in the IP (e.g. genetics.yale.edu).  For large geographical areas with only one university, I counted hits from the city where the university was located even if it wasn’t through a university assigned IP address (e.g. for University of Utah, also hits from Salt Lake City).  I did not do this for cities with multiple universities.  Thus, IP tracking data is much more robust for large state universities than it is for universities in areas like Boston, New York and San Francisco.  It is likewise more accurate for universities with one open position than with multiple open positions.  Finally, it is much more accurate for applications with earlier due dates (say August through November) because later on there started to be too many hits to parse with as much granularity.   The data set suffered a bit because the task grew in complexity as other demands on my time increased and my anxiety over finding a position diminished.

In the next post: a little more about all those applications.

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