Introducing the Tech Job Navigator for Tech Pros
Invest your time upskilling where it counts, not randomly clicking on job boards
Staying ahead of the curve in a rapidly evolving tech landscape is crucial to keeping your career on track.
This means taking a proactive approach to knowing well in advance what skills and experience are in demand for your ideal role before you start reviewing job posts.
Occasionally dipping into job boards to skim job posts is an inefficient use of your time (and tedious too). Far better to actually spend your time building and honing the skills that are in demand in the role you want. This means understanding the skills and experiences that are in demand for your current or target role right now.
But how do you know what to focus on next? Is there a gap emerging in your skill set? Is there a new role emerging that you could pivot to?
The Tech Job Navigator lets you take a data-driven approach. It analysed hundreds of thousands of job posts from leading tech job boards and extracts the most common skills and experiences for hundreds of job titles. All expertly curated and normalised.
The simplest thing to do is to count how many times a phrase occurs with a title, i.e. the frequency.
This shows the most common skills and experiences associated with the Data Scientist role. The width of the bar indicates how often a phrase occurs with the title.
Some of the phrases are so common that they are not very informative. For example, 'Data' comes up almost all the time with 'Data Scientist'
That's where the association metric comes in. This is a measure of how strongly a phrase is associated with a title. Shown by the brightness of the bar.
This bumps the most specific skills and experiences to the top. Out go Python and Data, in come Bayesian and Random Forests.
You can switch between the two views to get a sense of the most common skills and experiences and the most specific ones. using the simple ranking control.
Let's say you were wondering about moving from Data Scientist to Data Engineer. You can compare the two roles side-by-side to see which skills and experiences are most prominent in each role. Links make it easy to compare the ranking of the same phrase across the two roles.
Ranked by frequency, you can see that Python, SQL, and Machine Learning are common to both roles. But Data Scientist has more emphasis on Statistics, R and Analytics/Analysis, while Data Engineer has more emphasis on cloud platforms like AWS, GCP and Azure.
Flipping to association ranking, we can see starkly what is specific to each role.
It's early days for the Tech Job Navigator with lots of exciting stuff developments in the pipeline. This is an open alpha version. Have a play with it and let me know what you think.