How to get a Data Science job?
More than ten years ago (when many “big data” methods were just beginning to emerge), data scientists were seen as a high risk, high reward, highly prestigious profession. It was rare that anyone would be promoted by publishing a single paper, but when they did, it could be used to enhance reputation and later on, tenure.
And the beginning of “big data” was hardly mind blowing. It was kind of like putting a pile of rocks in a stream. If you have the rock, you have a data science process to replicate, process, and analyse the original, reanalyze the data, and voila, you have a new large data set. If not, well, perhaps you could convince yourself that it was still worth doing.
This didn’t change until 1990s
During the 1990s, this became the dominant paradigm for data scientists: Powerful machine learning platforms gave rise to more general applications, such as predictive analytics, cluster computing, storage management, research-based communication, and decision support software.
This led to the publication of the seminal book Data Science and the creation of the term “data scientist”, as a specific subfield.
For good reasons, developers don’t like to update their operating system. So once you have a map, it’s straightforward to stream these updates to clients.
So why don’t more companies hire data scientists?
There are a lot of reasons, and that’s for a blog post that will be written soon. But from my perspective as an analyst, I’ve been monitoring this space very closely, and there are three things we can call out.
Software infrastructure just made it cheaper and easier for any data scientist in the world to start developing data tools. Anyone anywhere can start.
To grow a project to a level where it’s worth paying someone else to help you build it, you need to do it fast. You need someone to absorb the code you write, produce stable, reproducible and usable code, and ship it to a commercial environment.
To make that happen, your web developers need reliable, agile frameworks, good tooling and continuous integration. If you can pay a developer to do all that work, when is the last time you’ve been able to hire someone new in your development team to help work on a project? To the extent that you have a developer, they become the repository of bug fixes and feature requests. And this slows down software development.
For business to work, you need a culture of communication and collaboration
If you are aiming to be a successful business today, you need to have a culture in place that encourages project collaboration and growth. Not everyone will appreciate this, but hiring a data scientist is a way to build this culture for yourself.
Data Scientists excel at brainstorming, problem solving and data cleaning.They understand the value of data and how data can be converted into actionable insights. They manage data in a digital domain where they have a clear and coherent set of rules to follow. Data Scientists must also be ambitious, creative, data driven, and want to work on projects that are relevant to their career. This position can be career-enhancing if you choose to work on a project with a better solution to a social problem. This can give you a reason to say thank you to everyone who has worked on the project.
Data Science is one of the most sought-after positions in the world.
Jobs such as these are not easy to get.
Thus, this is a great opportunity to expand your career in the most prestigious domain!
Finally, if you want to have an overview of what it means to be a Data Scientist, then have a look at my book Data Science Job: How to become a Data Scientist which will guide you through the process.
And if you want to know more, read my other articles about becoming a Data Scientist: