Moving from a mid-career IT services role to machine learning is possible — Part 1
Here is how.
I took actions to move from a legacy IT role into data/analytics. If I can most of you can as I had nothing fancy with me.
Coming back to the main point — I had No fancy degrees, no great product brands, no $250K package, no internal posting and no ‘aha’ moment — nothing.
These are the steps I took.
Accept: I accepted that my role was stagnant. I could see no hikes happening, I felt under skilled Infront of young crowds, I heard of stories of folks drawing 3x my salary and I was offered the work which was repetitive, and I was already an expert at it. No one offered me more than 10-20% hike in the job market then.
“I understood that knowing things at “box” level will no more work in digital era. In long term I was reading basic paper summary for the technology I was skilling for like the paper on Resilient distributed dataset.”
Explore: Back in 2012–13, you typically had a stack called as SMAC — Social, Mobile, Analytics and Cloud. I explored all of them at 101,102 levels to get skilled “independently” through online mediums and invested in a hi-spec laptop. I stopped exploring legacy skills and certifications.
Dig deep: I was a Senior Manager then and was off coding for nearly 10 years but Python made so much sense even starting new and I coded all the areas in python following codes of others in blogs, courses etc. Some codes made sense some did not but I did not stop, even made small hobby projects. I understood that knowing things at “box” level will no more work in digital era. In long term I was reading basic paper summary for the technology I was skilling for like the paper on Resilient distributed dataset.
The advice I have is accept, explore, dig deep, specialize and map to execute. This model may work in other areas as well not necessary machine learning.I have seen some mid career managers become excellent big data consultant for example.
Specialise: I kept analytics as my primary skill area and cloud (Azure) as secondary, I left actively pursuing other fields. I started creating my own blogs and articles as well. My blogs never became viral but it did serve as a basic proof of work and my own log of learning. I kept doing these without sight of immediate success or transition. I cleared Azure Data Scientist track as well.
Map to execute: I mapped my personal goals with professional ones like where I wanted to live, what is the use of my life, how much money I wish to make and with what sacrifices etc. This mapping and the continuous iteration of learning/skilling made me confident of taking up assignments in Universities initially and later in corporates mostly in training. From training, I moved into small time consulting stuff like — “can you solve this real quick and in real less budget sort of stuff”. Iterated here again and moved up the value stream. From there went to an agency business model.
For me i wanted more control over my time and work so i went the entrepreneur way but this phase of “Map to execute” will also work if someone wants to change job or apply for internal jobs involving machine learning.
The advice I have is accept, explore, dig deep, specialize and map to execute. This model may work in other areas as well not necessary machine learning.I have seen some mid career managers become excellent big data consultant for example.
In summary this is the distance i covered, maybe it is not the best outcome but definately these steps have provided me a recharge in my career and also in income and respect that earn in my daily work now -
Before moving in machine learning (Role/Work)-> Senior project manager managing legacy ERP but also managed a small consulting assisnment that was doing options analysis for Data lake. This was some where in 2015 in the UK.
Today my role and work is -> Run my own boutique data agency, most of my work involves machine learning consulting and training. (There were other factors that made me a “data entrepreneur”. But that’s for another blog)
Please share if you think this post can help others. You can connect with me over Linkedin here.
Part 2 of this blog is at this link.
Here is me in 2023–8 years since i started skilling holding two great books that helped me in machine learning skills and in the next blog series i will talk which learning medium and mode helped me most.
Comment what else you may need to know? Follow me for more posts on data, machine learning.
