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I would suggest you to start working on real world data science projects. The best possible match for a real world data science problems can be found on kaggle.com or UCI. Try to pick different types of project in order to cover the broader dimensions of the data science. For example, start with a predictive analysis and then move on to a classification problem once you are done with the first one. After completing both types of supervised learning, you should work on an unsupervised learning based project. Once you are done with at least these 3 projects, you are prepared enough to appear for the Data Science interview. The more you work on the projects, the more you will be prepared for a job. Good luck with your job hunt :)
Well said by @Shivam Solanki Given my experience on how I became a Data Scientist, he's got some point you should consider following. In addition to his suggestions, I recommend you take part in Kaggle competitions and showcase them on your git repository so employers can see them.
Additionally, I will like to point out that most companies nowadays from their models on the cloud. So a bit of cloud computing is highly recommended. What do i mean, i meant being able to deploy your models to productions. Few examples are Wastson Machine learning, WS SageMaker, Google ML. As you keep building your portfolio do not forget to keep yourself updated with research papers, blog posts...I hope this helps. Feel fee to write me if something is not clear. I do not believe in luck so I will say go out there and make it happen. You have got this. Cheers :) Damilola Omifare