Career Quadrant for Data Scientists and ML Engineers
Optimizing the approach and making the most out of your current situation.
I’ve been doing some study on the type of folks we have in data science and ML. My goal is to find the best possible model to provide value to these individuals.
I have been interacting with Data Scientists and ML Engineers from all sorts of backgrounds and all of them have a different story, they broke into the field through different doors, and almost all of them are looking to optimize their situation.
Some are looking for a way to get started, some are waiting for their first offer letter, some have accomplished big feats and now trying to find the next challenge in life, and some are looking to switch to a bigger and better work profile.
I have tried to document my response in this week’s newsletter for all of these folks in clear and concise steps that they can follow.
Important factors that influence career trajectory in DS/ML
After scrolling over dozens of job descriptions and Linkedin profiles, I’ve found that the most influential factors of a person’s career trajectory are their work and their educational background in a computational domain(statistics, physics, mathematics, economics, computer science, etc).
Most individuals have a Bachelor’s degree but the minimum qualification for the role of a data scientist requires a Master’s and preferably a Ph.D .
Crossing over these two factors gives you 4 different quadrants of people:
People with both an MS/Ph.D degree and work experience in the field - Experts.
People without MS/Ph.D but who have work experience in the field - Analysts and Engineers.
People with neither an MS/Ph.D nor the work experience - Absolute beginners.
People with an MS/Ph.D but no work experience in the field - Freshers.
Questions that identify each quadrant
All of them are trying to get to the next step but not many know have the clarity to reach figure out the next step. Each quadrant is identified by the type of question they are trying to answer.
These questions are defined by the desire people have at that stage of their career.
So, here is the infographic for you to dig in:
Again, this is NOT an exhaustive list of options/steps. This world is full of opportunities and outliers. This is created on the basis of my understanding of the field and job market analysis. You can download the PDF version from here.
If you have any questions related to any of the point or if you need any specific details, you know how to reach me!
Interesting read of the week
Data scientists often find themselves dumping their experiments and projects and it’s mainly because of lack of a well-defined research phase and a feedback mechanism.
This post on Peer reviewing Data Science Projects delves into both the research phase and the model development phase. With an exhaustive list of questions, frameworks and checklists, majority of your data science projects and experiments, which you think could provide you deeper insights, should be dumped which in turn will saves you time.
The article is drawn from the personal learnings of Shay Palachy and has a lot to offer and incorporate in your experiment design process.
Highlight of this week
A quick announcement. I will be doing a webinar on Introduction to tensors with TensorFlow this Sunday(May 23) at 11 AM IST in association with Commudle.
Feel free to join in by registering on this link and get to be a part of a community where we’ll be sharing a lot of content, resources, assignments and more.