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Ask HN: Advice on Transitioning from Data Scientist to ML Ops Engineer
6 points by rollo_fuaxsao 20 days ago | hide | past | web | favorite | 1 comment
A bit of background; I have no formal education past (UK) A-levels (I studied music production undergrad for a year before dropping out in 2012) since then I've worked in IT support whilst teaching myself how to code in JavaScript and Python, this lead to a job as a Full Stack web developer at a pharma company which I held for 3 years. I then jumped on the Data Science bandwagon and in 2016 moved to a Data Science team within the same organisation.

My role in this team was focused mainly on how we could productionize and scale our models due to my background in web dev and deploying web apps. So I've done a lot of model optimisation, containerization, CI/CD and architecture within all 3 of the big cloud providers over the years but still feel I lack a lot of the skills needed to be a full DevOps Engineer. I did really enjoyed this work and in retrospect believe it was more of an MLOps role but due to personal relocation needs I had to leave in late 2019 where I joined a more 'traditional' retail company where the Data Science team is essentially a renamed Data Analytics team. Most of the team currently use SQL, SAS and Excel for their work and we don't currently have any cloud compute or admin privileges on our machines to run any non white listed programs (our IT support team don't even know what docker is) which has caused me much frustration.

I would greatly appreciate and advice or recommendations on how best to pivot towards a MLOps Engineering role as my current Org (500+ employees) don't see the need for this role at this time.

*The reason I joined my current Org was during the interview process I was sold a 'Digital Transformation Project' which I would supposedly have an impact on transitioning the team to a more product delivery focused Data Science team but due to changes in upper management this has fallen through and I now mostly work on producing spreadsheets, slide decks and coding in SQL

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