For the challenge was built a Jupiter Notebook ( , considering:


  1. Subset (from raw dataset) with average age increased by 20 years old
  2. Attempted career migration via jobs site
  3. The implicit risk in the analysis of the profile, for the potential candidate to do a Career Migration through a ML model.


Attempt to verify if there could be a risk for a ‘seniors’ tech professionals to do a career migration via Job Site.


In order to helping everyone to support better decisions about their careers, we assume as a ‘case study’ that a Job Site built a ML model using the raw data from TCR 2021 considering the features of the dataset as a ideal profile for hiring candidates for a job.



For this analysis was created a Jupiter Notebook with a subset (from the raw dataset) with only the tech professional which age is 20(twenty) years above the average age of the original raw data. In this case we will find the outliers (tech professional) by the age criteria.



So, to help the tech professional, we suggest that she/he should to do for yourself this question:  what is the risk I am going to face if I try to do a career migration via a job site that use a ML model to analyse my CV?

All the information about the visualization and analysis could be found in the Jupyter Notebook at GitHub. 

I declare, on my honor, that the Jupyter Notebook and its analysis, was prepared by myself, without cheating, falsification, identity theft or any other technique that may alter the result of the contest. I further declare that I have not used any other sources, other than those indicated.