Sharing my Thoughts on (Data Science) Consulting
I have been a (Data Science) consultant for the past 3 years, and I decided to transition out of consulting several months ago to take on a new challenge as a Data Scientist in the gaming industry; I can not possibly be happier with the change. Retrospectively I have met many amazing people over the past few years and they have greatly contributed to my personal development. I would like to take this opportunity to share some of my thoughts in terms of the pros and cons of consulting for any aspiring Data Scientist.
Overall, I would highly recommend anyone to work as a consultant as part of their career journey.
PRO - Exposure
I have grown tremendously in the past few years in terms of both my communication and technical skills. The opportunity to develop one’s communication skills is clear. As a consultant, we interact with client contacts regularly for progress updates, issue updates, and presenting our deliverables. The opportunity to develop one’s technical skills is also enormous. When I started as a data scientist who could only perform elementary data exploration and machine learning modeling, I had the opportunity to lead a data engineering/software development project (which I did not have any exposure of) as well as other kinds of projects. This eventually helped me grow to become a much more mature data analytics professional with the ability to develop end-to-end systems. The opportunity usually comes from consulting firms being short-staffed, and consultants need to wear multiple hats to deliver projects outside of their comfort zone.
I have also had the opportunity to work on interesting projects. For example, in 2019 I worked on a reinforcement learning project for banking applications. It is often the case that clients require help in implementing the coolest state-of-the-art technology, but the opportunity depends on the direction of the consulting practice. If practice focuses on RPA, they may not get as much machine learning related projects. One should be careful before making their career choices.
Furthermore, as we work with new clients every couple of months, we can gain a holistic view of the industry and able to pick up quickly best practices on many of the data science processes.
PRO - Hours
There seems to be a lot of misconception that consultants work long hours. After being in two firms and being in different departments while assuming the roles of different levels, I highly disagree. While it is true that deliverables need to be presented to clients promptly, and most of the time we do not want to postpone deadlines, there would be some overtime at critical times and boss would call you at an ungodly hour to fix things, but it is not any busier than a normal non-consulting job. The reason why we hear consultants working long hours is due to their personality and that they are extremely vocal. It seems to be human nature they like to brag about long hours.
The bottom line is, working on a deliverable is pretty much like a sprint (not in the agile sense). You go full speed on a deliverable for a few days and then you can relax for the rest of the week. There is also a lot of flexibility in how you want to manage your time as long as work is finished before the deadline.
CON - People and Culture
I have met numerous extremely unpleasant people during my 3 years as a consultant. Due to the extreme hierarchy of the firms I have had to opportunity to work in, the “pls fix” culture is very real. Many of the middle management simply cascade work even when they have nothing to work on, this is simply due to their pride in having the power to order people around. They do not provide any value and contribute deeply to the toxic culture and high turnover rates.
As a conclusion, the pro for exposure can easily outweigh the con of people and culture in the short-run, and I would recommend jump-starting a data science career in consulting.
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