Data guy in a small company: The Opportunity
Data Scientist, Data Analyst, BI Developer, Machine Learning Specialist…
There are so many data roles out there that it can be pretty confusing. Many businesses look for Data people. However, there are a lot of differences, not just among positions, but also — and arguably even more so — among the potential employers.
Hence, when you are looking or interviewing for a job — check first how many data people are already employed. If there are fewer than two, forget about the job title and description. They will not be relevant. You are interviewing to be a jack-of-all-trades with a “Data” prefix.
In a small company you are interviewing to be a data jack-of-all-trades
And that is not necessarily bad. Especially for young, less experienced applicants, a small company might be the very right thing to look for. Here are some important things you may gain from working in a small company.
One small caveat: “Small company” in this context means a company with a very small data department. The size of the data team is loosely correlated with the company size, so I use “small company”. It explicitly excludes companies that are primarily focused on data but happen to be very small, e.g., data startups.
You’ll get a lot of it. You probably will mess up a couple things and hopefully learn your lessons. Other people in the company will give you a lot of feedback, especially if you suggest things they did not consider so far. Either they will give it a chance or tell you why the idea was not good, or you will learn to deal with mediocre management.
In a small data team you will be deployed to do not just what was written in the job description. You will inevitably
- analyze data
- write/review specs for programmers
- create visualizations
- set up some basic data architecture
- work with QA before and after deploy
In any event you’ll gain experience.
Without (m)any other employees with your skill set on the roster, the company will rely on you to answer a lot of questions, especially as the company becomes more data driven. So even if they might not pay you very much at the beginning, you will be instrumental to making huge decisions for the company. You probably will end up advising directly a VP, CFO or even CEO.
Any profitable business that does not use data analysis can improve significantly just by doing the basics. So even a junior data analyst can have a strong impact on the company’s development. Once you see your ideas having impact, you will suggest more and more. Eventually, it will give you a lot of confidence in your skill.
With not many people knowing much about data, you will often be on your own with your challenges. The Internet will help you. There are plenty of data communities out there and people are really happy to help.
I highly recommend getting accustomed to using Stack Overflow. You will learn two crucial things: finding answers as well as. arguably more important, asking new questions. Being able to ask a questions in a way that people can help you is a great skill on every level.
Overall, the ability to solve problems without any help except for the web browser will provide you with a new superpower: Independence.
You will learn to talk and explain data-related issues to people who do not live in the data world. This is a tremendously important skill for the real world.
Talking data to people without data background is a tremendously important skill for the real world
Communicating is not limited to talking. It includes creating charts, presentations or even things as basic as showing only relevant data in a neat table. You maybe will not do it 100% right from the start, but you will learn.
Last on this list, but not least. As a data guy who finds a lot of bugs, patterns and opportunities you may think very highly of yourself. The reality often will bang on your door. The people who set up the company, and made it profitable before you came, know a lot. And many times they will recognize something that you did not catch in the data analysis. And they will be right. When I was a newbie data guy, I often thought the patterns in the data were merely noise. But there was an employee at my company who had been there for ten years. This guy’s technique and communication skills were maybe not the best, but after ten years he could smell trouble from a mile away, while my inexperienced eyes would have missed it until much later.