This is not a technical question but can have a yes/no answer, and a why statement. My understanding about digital signal processing (DSP) is that it allows to take real-world analog signals (1D, 2D, etc...), sample them to create digital representations on the computer, and either way: extract useful information from filters or modify the signal to take it back to the analog world (using a digital-to-analog converter if you like). There is an overlap with other disciplines like pattern classification or statistics as DSP implements dimensionality reduction techniques that make easier to classify samples; or with embedded systems, that have DSP ICs. I am not considering analog signal processing which is another topic for a question.
There a talk explaining that there is an "identity crisis" in DSP nowadays with all the machines learning (ML) methods because they can process raw data without having to apply the carefully crafted DSP knowledge (let's say, using a CNN instead of extracting feature vectors). Of course, sacrificing computational power, time tunning hyperparameters and or the effort to create enough training data. But people don't care as long as they get the processing they want and don't have CPU, power, or memory constraints. This also reminds me this article pointing that DSP roles are not longer the same they used to be 25 years ago.
I have observed certain similarity between computer vision (CV) and data science roles with DSP, but I am still trying to convince my self that someone can really make some DSP working in them. On a CV role you are in charge of dealing with 2D+ signals and perform tasks such as face recognition or pose estimation (which has historically been a DSP task) but the problem is that the majority of the work is to reuse components-out-of-the-shelf (COTS), like openCV, to specific areas, without having to demonstrate full understanding of the yielding techniques (of course, I am talking about someone that hasn't to work in a lab where some research work needs to be done). And about data science, I know that they use a lot of ML, data visualization, statistics, business logic, A/B testing; and have to use the data gathered by data engineers, but they do not use DSP-core techniques like sampling and interpolation or FIR/IIR/adaptive filtering. A former data scientist said that you can even not use ML at all if you want.
Can someone really do DSP with these positions?
P.S. I know there are a lot of roles that have a better overlap with DSP (e.g. EEG, acoustics, radar, music information retrieval, ASR, media codecs) but I am focusing on the ones I mentioned, mostly because they seem to be doing a big impact today. And I believe someone would answer something like "it depends on how good you want to be at your job" but I'd like to consider roles where you are naturally forced to build DSP ad-hoc solutions.