It was an afternoon, somewhere in the start of February 2020 and I was taking a break from academic coursework to check my mail. On a normal day, I am not a regular recipient to interesting mails. Most of them are just enticing swiggy alerts with cringey slogans or a tremendous amount of newsletters that I forget about as soon as I sign up for them. But this afternoon, things were different. I had a mail from the organization that I had recently applied to for a data scientist position. I opened it and the contents of the mail looked rather promising! After a couple of mail exchanges from either side, I had finally been officially appointed as a “data scientist” at the organization. (Why the bold and quotes for data scientist? I will let you know that in a while)
How did it all begin?
I first came across People For Animals, Bangalore(PFA) in the summer of 2019. I had recently read about conservation data science (the use of data science in aiding ecological conservation) in this wonderful article and was fired up to find organizations near me that would have such a job for me. That is when I came across PFA, and I was immediately in awe of the work they did. They were a small team making a large impact in helping Bangalore’s urban widlife. The real idea that struck me was that PFA did exactly what I had always wanted to do since being a child, work with and help out animals. And having read about conservation data science, I now had a whole new door open in front of me - I could do a bit of what I had always wanted to do and do it with the skills I had been trained for. This was very good indeed! But, I was in the 3rd year of my undergraduate and was amidst an extremely tight schedule of academic and extra-curricular work. So, I decided to wait a year to apply.
A year passed and now it was time to identify if they had an open position for a data scientist. I was searching for a data scientist job opening in an animal shelter. You are right, that’s as crazy as it sounds. And guess what? I didn’t find such an opening. They had a general internship, veterinary internships and volunteer jobs open. None talked about a data-related opening. I was dissappointed and it felt like I would have nothing to do here after all.
And then I saw a visualization! A single visualization that quantified the number of species rescued by the shelter. Then it struck me, “If there is a viz, there has to be some data. And if there is data, maybe they need somebody to pick up insights”. It was a good thing I found PFA’s official mail id on their website, and I immediately sent over a long mail about why I should be given a chance to intern as a data scientist and how having me could help the shelter. And this mail was what made all the difference.
Thoughts after being offered the role
I had bagged the role I wanted at the animal shelter and was pretty excited when I told my family, friends and teachers about it. I had agreed to join at the organization after a couple of weeks. However, I must admit that the next few days were highly turbulent for my thought process. I began vacillating on whether I should accept the offer or not. The reasons for this sudden sense of doubt had nothing to do with the shelter or the work they did. They were 2 simple questions that I had to ask myself before taking the role up:
Should I go for it? Would this opportunity be useful to my career?
Would this be even considered a “real” job on my resume?
I don’t know the complete answer to the first question. And I never might until a few years from now. But, what I did know then was that, “Trying to bring data-driven culture into a place that had never seen it before was not an easy task. And challenges are what helps us grow”. Therefore, it made sense taking up the role.
The second question was probably my hidden ego speaking of an unreasonable pedestal it thought it had because I was an engineer. However, thinking that way was outright wrong on my part and I am glad I didn’t let my blatant ignorance cause me to lose such a tremendous opportunity to work on an organization that did what I had always wanted to help with.
I finally accepted the role and I must say that I was extremely fortunate to have great support from my family in letting me pick up this role and start my data career with a pro-bono assignment at a local animal shelter.
Had I finally become a “Data Scientist”?
On my first physical meeting with the team at PFA, I realised that they had some really good records of data about the shelter’s activities. However, they had never tried to tap into the data they had before and so, data science was an alien term to the team. What this meant was that I didn’t have a superior from my field who could help me out with data-related work. I had to do what I knew from scratch and constantly discuss with my domain experts(in this case, the PFA team) about how I could serve them better with my abilities.
The data was not in the cleanest format and required me to spend a lot of time cleaning it up. I had to write a lot of code to do this and also visualize it to help the team understand what I was providing them with. Communication was an important part of my job too. However, I decided to update my job role as “Data Analyst” at the shelter because I honestly am not ready yet to be a Data Scientist and I probably am not ready yet to be an analyst too. An honest title would be, “Data-loving Kid” but, I don’t want to be frowned upon by the professional world!
Jokes apart, I love doing work for the shelter. The data is raw, engaging, unruly and extremely fruitful if I am patient enough to understand it. I am still working here and so would not be able to release anything more about my responsibilities yet. Probably, I could do so after my internship terminates and with the permission of PFA.
Current Challenges at Work
There are a few obvious challenges about the role I picked up and I feel its important to share these here as the challenges we face are usually more important to others than the comforts we get.
Challenge 1: Lack of Technical Supervision
The lack of a senior data scientist or analyst at the shelter is a challenge for me, especially because I do not have a central source of authority to approach when I hit a data inconsistency issue. I am the first to have collected the data at the shelter and while it has its perks, it also has severe challenges.
Challenge 2: Having to Simplify Everything
Brevity is a skill I naturally lack. And simplification is a virtue that has not yet been completely bestowed upon me by life. Therefore, having to simplify the data terms I worked with and explaining my insights in fashion that would interest the PFA team continues to be a great challenge for me. Ater all, it’s not about what I know or what I can do. It’s always going to be about what value I can generate for them.
And that’s a wrap for this post!