| You mean leving programming? Can't help you there. But if you want to change subfields... I only know data scientists who come into my workplaces from other fields, and rarely know folks who move out into other fields. So these anecdata are heavily biased based on where I've worked. 1. Robotics has a huge gob of data every test, and parsing it is basically Sisyphean. Someone who can learn about, and educate others about, building proper observers and reporters into C++ codebases, and building proper dashboards with data coming out is always really valuable. From there it's a short hop into roboticsy systemsy things itself. But beware, large shops will have these silo'd. Think smallish labs for large companies. You do not want to get stuck building reports for product teams - stick to engineering teams. 2. Manufacturing, at the highest levels, is metrics driven, so again, getting in and helping to establish data-driven process refinements, then moving "down" the stack into the software is a good way to make your pivot into embedded systems or industrial IoT. But beware, large shops will have these very much silo'd. 3. Science / academia. A good analyst for a research lab is impossible to find, because of pay differentials. But if you can take the hit, and are willing to grovel a little, you can easily become the most valuable person in a large enough academic lab. The ones I've been adjacent to are Geophysics, Planetary Sciences, and Astrophysics. All really tough data problems. |
But it's pretty difficult to find those type of jobs willing to pay through Deel or Glob.Partners.