How Laurel & Yanny have been looking for a machine learning engineer: search queries
Meet Laurel & Yanny, professional IT recruiters! Today we’ll tell you a story about their machine learning engineer search.
Laurel and Yanny have just received a new vacancy to work on. Here it is:
Wow! Doesn’t look like an easy task.
Laurel & Yanny use AmazingHiring to find a machine learning engineer for CHOAM. Their initial task is to create a search query. Let’s see how they’ve managed.
Oops, Yanny has obviously made some mistakes. Let’s take a closer look:
- Firstly, boolean search modifiers. Deep learning lacks quotation marks to be searched as a phrase, not two words separately. The same is applied to C++.
- Languages listed using boolean operator OR should be placed within parentheses so that the search output includes specialists whose skills contain both deep learning and at least one of the languages listed (Python, Java, C++, etc).
- Secondly, the search query lacks important skills listed in the job requirements. Including machine learning APIs and computational packages (TensorFlow, Theano, PyTorch, Keras, etc) and big-data technologies (Hadoop, Spark, etc.) can narrow down the search output and help find the most relevant candidates.
- Then, the current title. “Machine learning engineer” (using quotation marks) narrows down the search output significantly. Dozens of potentially relevant candidates whose current title is named broadly, like developer or engineer, will not be found.
- As the required experience for the position is 2+ years, it’s right to specify the level of seniority as middle. But it’s a mistake to include middle in the current title field. For this purpose, the seniority level filter should be used.
Laurel has not made these mistakes in her search query and, what should be noticed, she included all the important job requirements in terms of skills and excluded SQL from the query as it significantly extends the search output.
She also excluded the current title analyst as there are many analysts among data specialists but they are not relevant for the CHOAM role.
Worthy of applause, high chances to find an ideal machine learning engineer, don’t you think?
In the next episode, we will show how Laurel & Yanny work with the search output 🙂