We need to pay homage to the open source web framework that we used to build the front end to jobtitled.com.  If you haven’t decided what Java framework to use for your web app, at least give Wicket a chance.  Version 1.4 has just been released:

The Apache Wicket project is proud to announce the release of Apache Wicket 1.4. Apache Wicket is an open source, component oriented Java web application framework. With overwhelming support from the user community, this release marks a departure from the past where we leave Java 1.4 behind and we require Java 5 as the minimum JDK version. By moving to Java 5 as the required minimum platform, we were able to utilize Java 5 idioms and increase the type safety of our APIs. Using Java generics you can now write typesafe web applications and create typesafe, self documenting, reusable custom components.

Find out more about Wicket here.

Okay, something isn’t right here.  Either bit.ly is lying or Google Analytics is lying.  Or I’m missing something.  If you aren’t familiar with bit.ly it’s one of the many URL shorteners out there.  It’s very commonly used on Twitter where there is a 140 character posting limit.  It also provides stats for who clicked on the link - which is pretty cool.  Only problem is just I noticed today that bit.ly says almost 10X as much traffic as Google Analytics reports.  What?  Anybody have any ideas?  My only guess at this point is a lot of people have Javascript disabled in which case Google Analytics would do nothing.  I’d love to know if others see the same thing.

In addition to correlating transitions between jobs we have also mapped how people have used education to further their careers.  You can start by click on the Degree tab, select your education level and type in your degree.  For example, a search for Bachelor’s in Computer Science returns the following results:

Jobs from Bachelor's in Computer Science
1 - 20 of 1353

Software Engineer  	        8.37%  	7 years
Senior Software Engineer 	4.4% 	10 years 2 months
Consultant 	                2.94% 	10 years 4 months
Software Developer 	        1.99% 	6 years 9 months
Programmer 	                1.89% 	5 years 9 months
Project Manager  	        1.52%  	11 years 10 months
Senior Consultant 	        1.38% 	10 years 9 months
...

Let me explain the two columns of numbers:

1. Percent Likelihood - This is the percentage of people with that degree who have worked as that job title.  So in the example, it means 1.52% of Comp Sci grads are currently or have been a Project Manager.
2. Time - This is the average time from when a student started their degree to when they started in that position.  Thus if you see 4 years, and it’s a 4-year degree then that would likely be their first job out of college.  If you see 0-1 year then this is typically a job they had while still in school

The Compare Jobs feature is a hidden gem of a feature that you might find useful.  Simply put in two job titles you wish to compare.  JobTitled will then search the statistics database and produce a comparison of traits.  Comparison results will provide:

1. Usage - which job title is more commonly used and by how much
2. Next Jobs - what is more likely to be the next job*
3. Previous Jobs - what was more likely to have been the previous  job*
4. Degrees - what degrees are the more likely to be had*

*= of shared jobs.

Keep in mind we can only compare jobs/degrees that both job titles have in common.  So if one job title has a transitions to Architect, while the other does not it would not appear on the list.  This is because ∞ times more likely just doesn’t make any sense now does it?  Thus, if you compare two very different job titles it’s quite possible there is nothing in common and you will get few results.  Play around with it and let me know what you think.

If this is your first time using JobTitled you might be confused about what all the numbers mean.  That’s understandable.  Let me try to explain some of them.

Next Jobs

• Percent Likelihood - Let’s take Product Manager for example.  When you see Marketing Manager 2.8%, this means that 2.8% of all the resumes we analyzed who held a Product Manager (and moved on) transitioned directly to a Marketing Manager position.   The higher the number, the more resume samples and thus the more likely we consider that transition to happen.
• Time - You will see something like “Average time in this position is 3 years 10 months” which is, well, the average time in that particular position before transitioning to the next one.  Under the bar graph if you click View All you will get in list form all the next job transitions.  In this listing you will see average times for that exact transition to happen.  So the average time for transitioning from Product Manager to Marketing Manager is 3 years 7 month

Previous Jobs

• Same concept, only this time in reverse.  Back to Product Manager example: when you see Associate Product Manager 1.4%, that means that 1.4% of Product Manager resumes we analyzed transitioned directly from an Associate Product Manager position.  Time works the same way.

These numbers are the building blocks for everything else in JobTitled.  Understanding these are the first step in your quantitive career research.  Don’t hesitate to ask if you have any questions.

Courtesy of Flickr user bootload (cc: by-nc-sa)

JobTitled is finally live.  :)  We are in public beta.  From conception to today it’s been an insane amount of hard work and long nights getting it to where it is today (tonight being one of those nights.)  I’d like to thank my trusty sidekick Joe for helping me in this development journey.  I’d like to thank all of the private beta users for providing bug reports and great ideas.  Last but not least I’d like to thank my wife for being understanding and supportive.  I look forward to an expanded group of users and welcome new feedback and fresh ideas.  I hope you enjoy looking at your career in a whole new way as much as I do.

JobTitled has moved!  We upgraded our server and connection in preparation for going into public beta.  Please let us know if you experience any new issues.  Keep the feedback coming!

JobTitled is focused on helping everyone make better career decisions through the use of analytics.  It’s a place to learn more about your career path and what else is out there.  It’s a place to explore and share with others.  We believe the career trend statistics that we calculate are a powerful new way to think about your career and the working world in general.

Why are you a private beta user?  Because we respect your opinion and want your feedback!  Please contact us if you have any questions, comments, suggestions or bug reports.  Thank you for your support.

The New Features category of this blog is for the purpose of explaining features of JobTitled and how you use them. Keep an eye on this category to see what’s new.

I’m going to start with the basics. The most basic element of JobTitled is (surprise!) the job title. There’s a lot of baggage around job titles themselves but they serve a purpose - they give a short name to what amounts to a collection of duties, responsibilities, tasks, and experience level that goes along with a position.

If you are wondering where to begin it’s quite simple: Search for your current job title. Search for the job title you’ve always wanted. Search for your frenemy’s job title. Search for any job title that you are just curious about. Start there and explore the possibilities.

I had an idea in mind, but in order to prove it would work I needed some sample data.  After much digging I found various places on the internet where I could get resumes.  Lots of them.  After some hard work and trial and error the first prototype was born.  By prototype, I mean a database that I could run queries on.  Even still, I knew exactly what it represented and what potential it had.  With data that represents individual transitions from one job to another, I had built an interconnected web of jobs — more technically speaking, a weighted graph:

Jobs = Nodes
Transitions = Edges

This graph could be searched for optimal paths between jobs.  It could be used to predict what was the likely job following any given job and what was the likely path prior to that job.  Then I created a second weighted graph to map the relationship between college degrees and jobs.  This powerful job graph is the core of JobTitled is today.  It allows us to programmatically perform career analytics on a large scale.