Not a biologist, but I remember that one mechanism to "encode", or better "express" spatial information are gradients of morphogenes, that control tissue development / gene expression.

Start here: https://www.nature.com/scitable/topicpage/gradient-based-dna-transcription-control-in-animals-1062/

Probably depends on the role. When we are looking for data scientists we usually interview for excellence in the "scientific" part of it. What do I care how good a applicant memorized the awful pandas API? At least for us that doesn't make the difference. Scientific creativity and persistance as well as understanding of model requirements and good judgement is what we are looking for.

If you are creating ML models you should now the science - low code will not save you from biases and other pitfalls.

Imagin letting a database guy doing a ML model, then use the probability for business decisions - just to find out, that your model is crap and highly biased.

And if you know the science you usually should be able to code with Python / Pandas / Sklearn ...

I am not against good and mighty tools - but typing the code is the simplest part in ML...