Hello! I recently completed my PhD in biomedical science and am hoping to transition to a career as a ML Engineer. During my graduate studies, I gained solid experience in applied data science and statistics, primarily analyzing sequencing datasets using R.
I found that what I enjoyed most about being a scientist was analyzing datasets and generating insights from data. I became excited about a career in Data Science/ML and so I began with the ML Specialization, and am now pursuing the DL Specialization and am applying these techniques to some fun projects in my lab. However, I become disheartened when I look at ML Engineer job postings, where there seem to be many skills and pipelines that I still lack (AWS, Cloud services, MLOps, etc.).
I guess Iām wondering - will learning the concepts/approaches in MLS and DLS and applying them to real-world problems be sufficient to stand-out in the job market? Are there other things you all might recommend I do? Thank you very much!