The space of data science is huge and covers many different problems, professional roles, and career trajectories. As a result the space is both using the term data scientist more liberally to cover more roles that more insights driven like data analyst to more general programming roles like data engineers.
I'd suggest first to think about what types of problems are you interested in solving and what your role in solving them would be. If you are interested in analyzing business data and help companies utilize those insights to build better products, market more effectively, and reach wider audiences consider looking for business analytics, marketing analytics and business intelligence roles. Some will use the title business intelligence analyst or data science - analytics. Additionally, there is opportunities to take traditional marketing analytics and growth scaling roles and bring your data science background to generate novel value through running A/B campaigns, automating SEO optimization experiments, and using your data science skills to drive and evaluate conversion metrics.
If you're interested in working on the customer customer side, consider looking at retail companies like Wayfair, Amazon, etc, where you'll be building and tuning content recommendation models. Many traditional companies like Macy's, Nordstrom's and Walmart are building large datascience teams to uphaul their online shopping experiences and compete with the likes of Amazon. The fashion space is also a fascinating place for data science innovation with companies like Zolando and Stitchfix doing cutting edge research and building fashion recommendation services.
The list goes on. But each of these areas has a distinct set of problems, skills, and needs. By identifying a space you're interested in, you can narrow your focus to apply with greater precision for roles that interest you. You can also identify analogous data driven roles to break into the space or apply for traditional roles that you can innovate with your skills.