Fortunately, the democratization of education has made it fairly easy to build a roadmap to learn essential technical skills.

Typically, the learning path includes building foundations comprising linear algebra, mathematics, probability, statistics, etc. along with a good grasp of at least one programming language like Python.

This list by no means is complete (or, will ever be), as the data science field involves continuous learning –

Given that the industry is witnessing a change in skill expectations, here is what you should focus on to build a stellar data science career:

Hone the often-overlooked skill of decision-making, essential for making the trade-offs in building scalable machine learning systems.
Build the ability to make informed decisions even in the absence of complete information, demonstrating quick thinking and adaptability.
Building ML models requires extensive stakeholder management, implying potential friction. Master the art of stakeholder management to navigate potential conflicts and drive decisions with a compelling rationale.

Working with cross-functional teams also means that your audience might come from varied backgrounds, so building tailored communication is a big bonus.
Most AI projects fail at the proof of concept (PoC) stage and do not even make it to production, while the ones in production struggle to show results. In short, organizations are waiting to see the returns on their AI investments. So, become that go-to person for getting things done and demonstrating the results while making progress.
Ensure the alignment of business problems with statistical ML solutions to lead the given AI project to success. If this step goes wrong, anything downstream will not be useful.
Innovation is a must – not just for enterprises but for all of us. Think outside the box and design innovative solutions. It is a sure-shot way to build your reputation as an expert data scientist.

Figuring out things on the fly is an art, seldom taught in classrooms

Overcoming fear also means asking questions. For example, always “Start with Why?” Why are we building this? Why would our customers/stakeholders care? Why now?
Once the "Why" behind the problem statement is clear, the "what" and "how" will follow naturally, simplifying the process of creating exceptional AI products.