Introducing the Tech Job Navigator for Tech Recruiters
Know how a title breaks down into skills and how it differs from another
When you recruit across many different roles, it's hard to keep up with the latest trends and technologies. The Tech Job Navigator helps you speak with confidence about the skills and experiences that are in demand for each role, whether it's a Data Scientist, Data Engineer, Big Data Engineer, the Machine Learning Engineer, AI Engineer, MLOps, etc.
Which skills are in high demand and and which are less so? Which skills are most specific to a role and which are more general?
When you are building a job profile, you want to be sure to check off the usual asks (as well and knowing their standard names). If a requirement is unusual, you can dig into that. It may be a new trend or a mistake.
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' almost always comes up with 'Data Scientist'
That's why we also calculate how specific the phrase is to the title. This association measure is shown by the brightness of the bar.
Ranking by association 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 rankings to get a sense of the most common skills and experiences and the most specific ones using the simple ordering control.
Let's say your client was windering whether they needed a Data Scientist or a Data Engineer. You can compare the two roles side-by-side to see which skills and experiences are most prominent in each role.
Connecting links make it easy to compare the ranking of the same phrase across the two roles. No link is shown if a phrase is missing from either list, or is too remote.
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, while keeping in view the popularity of the phrase. The combination of popularity and specificity is a powerful way to understand the requirements of a role.
For example, pandas is both relatively popular and specific to the Data Scientist role. Snowflake is likewise both relatively popular and specific to the Data Engineer role.
It's early days for the Tech Job Navigator with a lot of exciting developments in the pipeline. This is an open alpha version. Have a play with it and let me know what you think.
Try the Tech Job Navigator