Data Scientist was declared as "the sexiest job of 21st century"; it sure makes it the jobs that attracts people and if you have the right skill set you might be the next great Data Scientist. The demand for data scientists is plenty; it is still a job that hasn't been around for long. Data Scientists are being paid anywhere from around $30K and also paying up to $150k depending on the skills one has and skills he can provide to the job. Recruiters and companies include an assortment of skills in the job description.
This is a list of 7 things created by Ajit Jaokar that can help you get you a 150k dollar pay scale as a data scientist.
1) Understanding of research and algorithms: The ability to create new Intellectual Property and understand Research papers. In many cases, for more complex parts of AI, you are dealing with some significant unknowns. It then becomes a case of searching Google Scholar or arxiv, trying to find research papers for similar problems and then attempting to duplicate that solution. This is not easy, and it does not neatly fit in the agile / sprint approach also. Hence, it is not a common skill set in industry but is more common in academia. Related to this, you need to understand the maths behind algorithms because you may need to explore deeper options based on the workings of the algorithm itself if you want to enhance it
2) Working with large and high volume and often real time datasets: If you see the uber tech stackand the problems it is designed to handle, the volume and velocity of data implies that you need to rethink many aspects of the stack. The experience of working with large data volumes and real time datasets will be valuable
3) Full stack experience: In larger implementations, the Data Science role comprises of three roles: The Data Engineer, The Data Scientist and Devops Engineer. Experience of working in this environment is valuable (even if you are personally involved with only one of the components)
4) Large scale deployments: Related to the previous point, as I outlined in the four quadrants of enterprise AI business case, large deployments are likely to involve business considerations like explain ability and deployment considerations like CICD (Continuous Improvement Continuous Delivery) etc
5) Selling: This may come as a surprise but at the higher end of the scale, you would also be expected to sell (probably within a consulting role)
6) Domain knowledge: AI deployments in various domains are likely to be increasingly domain specific. For example, in areas I work with (bioinformatics) you often have sequence-based data. So, time series algorithms are likely to be used. The actual knowledge of the business problems and the algorithms applied to them will also be increasingly crucial
7) Working with people: You would often need to work with people in context of AI including customers, vendors, management, data engineers, devops, external stakeholders etc.
a) These jobs would need Engineering or Maths based background i.e. not only an accounting or a management degree with no technical qualifications.