Over the past two years, I have been working as a full-time data scientist for a government company. As the sole data science team in the organization, our job is a hybrid between data science and machine learning engineering. We need to research and develop ml solutions for the organization's business problems as well as implement them in production environments. The problem is I'm feeling stuck knowledge-wise and I don't know what can I do about that. Let me explain.
I have a major in computer science (B.Sc). Although I took some ai/ml courses during my major, I would contribute most of my data science education to the wonderful book "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow". During these past two years in the organization, I have gained a lot of experience in the field: I managed to bring some fair – but, far from perfect - ml solutions for a couple of the organization's business problems.
But alas, I'm still feeling like I'm missing a big piece of the puzzle that holds me from going forward. I'm feeling like I'm stuck somewhere between a beginner to an intermediate data scientist. I know all about the basic ml models and their basic intuitions and algorithms. I know the basics of deep learning and how to implement them in keras/tensorflow/pytorch. I know about CNNs, RNNs, and other basic deep learning architectures. I'm pretty prolific with pandas, numpy, and all other common data preprocessing\wrangling\visualizations libraries. And yet, despite all of that, I can't shake the feeling that I'm missing something important. That something that would make the difference on the previous ml problems I worked on and would differentiate a professional data scientist from me. Sometimes I feel like, for a lack of a better term, a 'stack overflow' data scientist. I mean, with every problem it’s the same – I preprocess the data a bit (nothing too fancy or advanced), I try a couple of basic ml models (usually random forests\gradient boosting works the best) and then I try to see if I can get better results with a deep learning approach. Finally, I will do some hyper-parameters optimization and will start the process of implement this model in production.
I know the primary suspect is my not-so-great math/statistics knowledge but is it really? Obviously, I know the basic math behind the models (not that I see it really critical at this point) and I know the basic concepts in statistics. Will improving either one of these areas will really improve me as a data scientist in my day-to-day work? Cause honestly, I don’t think this is the answer. I'm not looking to do a master's in computer science. I'm looking more for some useful books, online courses, or anything else that might help.
To sum it up: how can I 'escape' this beginner area and become a next-level data scientist/ml engineer? A one that can bring something unique to the table, other than doing the basic and obvious stuff for each problem.
I would really appreciate any advice on this. Thanks in advance.