What's in a Name?

I have some exciting news, and it now feels like the right time to share it....over the past 11 months as I've entered the professional world of data I have been focussed on the MLOps side of things. The time has come for me to pivot slightly and I have moved sideways into a more "Data Science" focused role.

Now, I put the term "Data Science" in quotation marks because, as I've covered before, it's all just a job name. But, I do feel like three is a general acceptance of what different roles will entail based on the title. Anyway, yes I will be moving into a role that is more about designed the solutions and not just deploying them and monitoring them.

No Experience is Wasted

Whilst I'm excited for this new chapter, I don't feel like I've wasted my last 11 months at all. I'm so glad that I've had the benefit of learning from some Seniors in the field and have been able to develop my Ops skills. Not least because I feel like it's important to me to have awareness and skills across the ML Lifecycle but also because I do believe that this learning and experience will help me when I am thinking about designing AI/ML solutions.

It might sound a bit clichéd, but there's that saying that your experiences, good and bad, help you get to where you are today. My experiences in MLOps weren't bad, I've had the pleasure of working in some great teams and meeting some great people, I definitely have skills that I wouldn't have without my time focused on this area. I'm by no means an expert in the area, but I feel like it's time to explore other areas of the vast field that is Machine Learning. Without these experiences and this new knowledge, I wouldn't have had the confidence or skills to be progressing in my career and show that I can apply myself to new things. I have no doubt that a combination of these things is why I was able to make this transition.

Am I still a Machine Learning Engineer?

(NB - maybe the real question is, "Does it Matter?")

Well, I guess that depends on how you want to think about it. For one reason or another, my job title is still "Machine Learning Engineer" which as I've talked about before, might mean different things to different people/in different companies. A job title, for the most part, is just a way to be categorised in a company so people know roughly what you do.

Of course, there are nuances to this...on LinkedIn as a "Machine Learning Engineer" the jobs I get contacted/advertised are quite MLOps focused, lots of deployment and cloud skills needed and, maybe obviously, I get advertised jobs that are also "Machine Learning Engineer". For these reasons, I have changed my LinkedIn to include both "Data Scientist" and "Machine Learning Engineer" - hopefully the algorithm will show me a wider range of content with this new approach.

But - really does it matter? I'm not so sure to be honest. I haven't had my LinkedIn title change for that long so can't tell if it's made a difference. My job title is staying the same, but my role is changing because of the conversations I've had, the opportunities that are there and the projects that I'm on. The job title is just admin.

Maybe, Just Let It Go.

I'll share something with you...personally, I'm pretty happy to be in title a "Machine Learning Engineer" still. Why? Well, no rational reason whatsoever. I've already hinted that it might be more beneficial to have "Data Scientist" as my job title if it shows a broader range of opportunities/posts on LinkedIn as there are some expectations that come with the different role titles. So why do I still want to be a "Machine Learning Engineer"?

Pretty much, only because I've identified as an engineer for the last 15 years and moving away from that is kind of scary. Though, over the years the type of engineer I've identified as has changed...aerospace engineering, biomedical engineer, machine learning engineer...They've all been totally different jobs/roles with different day to day, but with one thing in common....the "engineer". If that's not clear evidence that the title doesn't really make a difference, then I'm not sure what is.

What I'm focusing on are the positives and the exciting step forward in my career. I'm in a great position with some foundational MLOps knowledge and experience, starting a new chapter which is my next great opportunity to learn new skills in a different aspect of Machine Learning. With me, I take my skills and awareness of the bigger picture to my new projects where I can (hopefully) have a positive impact with the perspectives I have gained over the past 11 months.