2. You do not want to follow the scientific method

I have been a data scientist in tech for a little over five years now, and during that time I have had the opportunity to work with very skilled data scientists and to follow, to learn, and be mentored by them. I also currently mentor junior data scientists myself. Recently, this has really got me thinking about the role of a data scientist. Thinking about what the role of a data scientist is and thinking about the questions people should ask themselves to decide if becoming a data scientist is right for them. Do you want to become a data scientist because you really want to become a data scientist? Do you want to become a data scientist because the pay is good? In this article I wanted to talk about some of the reasons why you should not become a data scientist.

Data science is a very interdisciplinary field, at the nexus of computer science, statistics, and business. I see a lot of people who start off in computer science or business or statistics or have a combination of two of those, but it’s very rare for someone to come out of college and have all three. This means that you are going to have to take what you learn at school and expand into the other two. My traditional training come from a science background, so I had the statistics knowledge, but only a little computer science and almost no knowledge on the business side.

Many people have said to me:

The answer is always: YES! You can become a data scientist. However, I have noticed that if you come from a more technical background (e.g., computer science or statistics/math), it is generally easier for you to pick up the business part of it. Of course, I have also seen people who come from a business background that are amazing data scientists. This is true of many of my co-workers, but it did usually take longer for them, and it was more effort for them to go and then learn the computer science and math components.

If you are someone who is very interested in one of these fields and just not really interested in picking up the other two, then data science may not be the best career for you. Truth be told learning all of these other interdisciplinary fields is not easy, and it is not something that you just spend a few days on. It takes grit and is a very gradual continuous progress that requires you to be highly motivated. You must be disciplined and constantly filling in the gaps and it often entails asking dumb questions. Going into meetings that were business related and asking dumb questions like what does this acronym mean?

It also means admitting your mistakes. When you write code that produces a result that is incorrect, it is important to then own up to that mistake and then fixing it. Then next time making sure that you do not do it again. Finally, just picking up these skill sets constantly like your mindset isn’t like:

It is a gradual process of picking up more and more bits from here and there, and learning from other people. If any of these things do not sound like you, then becoming a data scientist may not be what you want to do.

Data scientist. It is in the title. Data scientists are first and foremost, scientists. A scientist is someone that follows the scientific method. This entails is first doing some research to investigate your problem and your question, then forming a hypothesis, and then conducting some experiments to confirm or deny that hypothesis. When we create models, we are trying to answer a hypothesis to see if it is correct or not. Then from the results, we would then draw some conclusions and then ultimately present those findings. Hopefully, we will be able to influence strategy or improve the company in some fashion from this. You are the person that kind of has to hold people accountable to the truth and when you make recommendations it has to be grounded in the truth and evidence. You use the tools of computer science, business, and statistics to inform that decision or recommendation.

That is not to say that data science is not a creative process, because it is. But you do need to be someone that really respects the truth, you are building upon the truth, and you embrace that. If you don’t keep everyone honest nobody will.

So, you think you want to become a data scientist? Maybe you think:

WRONG! WRONG! WRONG! Data science is constantly progressing and morphing in front of our eyes. Data science has changed immensely from when I first got into the field. What we do now is different from what data scientists are doing today. Data scientists of tomorrow will be doing things that are different from what we’re doing today. Almost every week there is a new tool or a new way of doing things that’s coming out. As a data scientist it is your job to solve problems efficiently and effectively and that requires that you take the initiative to keep up-skilling yourself. You need to be learning these new tools that are coming out and first deciding if these tools are worth learning about (there are way too many things to learn about). So deciding what is actually important and then going and actually up-skilling yourself to become better.

From my own observations of more senior data scientists, I have noticed that they really do two things:

So you will be constantly learning throughout your career. If this is not something that you are interested in doing, then you maybe you should not become a data scientist.

If you don’t feel comfortable doing whatever it takes using whatever tools that you need to accomplish the goal. A scenario that has come up many times, my boss comes to me and says:

Often, I then think to myself that it will be impossible if I get stuck in the weeds and trying to write the perfect code.

Instead, I must prioritize what is important. So, I can do this project in a couple of days, but I need to think about the best way for me to approach the problem so that I can achieve the project requirements. I ask myself:

That usually means putting together a combination of tools. Basically, just scraping together all these things and producing a minimal viable product. It is imprecise. It is not perfect. The code can be painful to look at, but it gets the job done. As a data scientist that is what is required, because data scientists have such a diverse skill set in business, statistics, and computer science. For example, if there’s some data that is not clean, and we need to write pipelines about it…I then become a data engineer. Sometimes you need a user interface component, then you kind of become a mini software engineer. Sometimes we need to convince leadership by presenting a strategy that this is the right approach…so you become a hybrid businessperson or product manager. I have had to personally do all these things in addition to what my core role is as a data scientist.

So, if you’re someone that prefers to focus on a single project and doing things in a very specific way, then data science may not be the best job for you.

You should not become a data scientist if you do not like marketing your own work. Sadly, even if you have an amazing analysis, and you have an amazing product that you develop, you have to self-advocate to get people to use these insights. So, you also must work on presentation, you must work on understanding what the business needs are. You need to know how to approach someone and tell them why this product you made is going to help them achieve their goals. If they don’t understand why what you have produced is valuable, they are not going to use it. The strong data scientists are not only amazing at data science but are also excellent communicators. They use a lot of different forms of communication. They can network with people understand what their issues are and just having a deep understanding their needs and the space that they’re working in to make sure that the work that they’re doing is extremely valuable to that space.

If you’re someone that prefers to just really focus on the technical components and just let the work speak for itself, data science may not be the best field for you.

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