For the past two years, I’ve been using Linkedin to broaden my connections for my Youtube Channel Destino Profesional and stay up to date with the latest trends in tech and entrepreneurship. By doing so, I managed to build a network of around 550 people. One year before finishing my degree and close to starting my job hunt, I would like to understand which companies and positions would be valuable options to consider applying to, based on my Linkedin connections.
Before continuing, I have to warn you that most companies in my network are either Argentinian or Argentinian-based since that’s where I live and I’m doing my full Data Science degree (I’m at UTSC for the Fall Period of 2022 as part of an exchange program)
Networks creation Process
To create my two networks of companies and positions, I followed the steps proposed by Benedict Neo in his medium post of October, 29 2021 using Python and the libraries pandas for data cleansing, pyvis for network creation and plotly.express to plot my network.
First, I downloaded my connections.csv file from Linkedin. Then, I cleaned my table by erasing my contacts’ names, surnames and emails, leaving only the company and position of each contact to work with. After a bit of iteration, I discovered I should erase all companies that are universities and all positions that are founder or C-Level since I’m not planning to apply to work in a university and, in most cases, I wouldn’t be able to get a founder’s or C-Level executive’s attention.
Afterward, in order to create my networks I created two tables, one for companies and one for positions, with each company/position within my contacts and the count of contacts working in that company/position. After doing so, I decided to keep only the companies with more than 3 repetitions within my contacts, to maximize the probability of strong ties existing. Applying the Strong Triadic Property, if I have two strong links with employees of a certain company, I should be able to create at least a weak link to a senior manager or recruiter from that company, increasing my chances of being hired. I also kept the positions with more than 3 repetitions to guarantee that I have more mentors or people I could contact if I decided to apply for a certain position. Finally, after making several changes to my original dataset, it was time to create my two networks. Here are my results:
My conclusions
Looking at my company’s network, two main companies set apart from the others: Mercado Libre and Globant. These are two Argentinian Unicorns* that have operations in several countries all around the world. I guess I should consider these companies. Other companies that could be interesting are Accenture and Google in the tech sector and JPMorgan and NaranjaX in the finance sector.
Regarding the positions I should apply to, it seems that “Pasante”, the Spanish word for intern, is the first position I should consider. The prominence of the intern position has a simple explanation: Most of my university’s contacts, which are students undertaking their degrees, had to start with something. So I guess, it would be a good idea to apply for an intern position.
Now, what position should I intern for? That is when it gets interesting as four positions caught my attention: Data Scientist, Data Analyst, Product Manager and Software Engineer. I believe these are all solid options that I should consider.
To finish with this brief blog post, I have a short question:
Dear reader, What position would you choose if this was your decision?
I’ll check out your answers in the comments.
*Unicorn: According to Wikipedia, a unicorn company is “a privately held startup company valued at over US$1 billion”.