What Makes A Successful Data Scientist? 5 Traits to Success
By: Sara A. Metwalli
Tags: Data Science, Careers, Women In Tech, Technology

What Makes A Successful Data Scientist? 5 Traits to Success

Photo by Fab Lentz on Unsplash

An important question that we all ask ourselves at some point through our data science learning journey is, “what do I need to do or learn to become successful in the field?”. I know I ask myself that equation in every learning journey I went through in life because when we stay on a learning journey, we often want that journey to end up with success.

But, the tricky part about that equation is, the answer varies based on the person's perspective of success. For some, success is getting a high-pay job; for others, it’s learning something new or have a job where you learn a new thing every day. For me, it is to do something I love and get paid to do it. Not necessarily a big amount of money, but as long as I enjoy what I do, I am successful.

If you ask any data scientist who considers themselves successful or who is viewed as such, they will probably tell you how did they achieve their success from the personal perspective that they gained throughout their journey. But, the interesting part is, although success is personal and subjective, if you ask 100 or even 1000 data scientists, we will end up with some common answers and some unique ones.

Although the unique ones make each learning journey special, the common ones are the ones you probably need to focus on. Because throughout your journey, you will develop your own unique traits for success. So, in this article, I will focus on the top 5 traits that all data scientists would agree you need to obtain to succeed as a data scientist.

№1: Have a curious mind hungry for knowledge

I will order the traits of a successful data scientist based on my personal experience and what I believe is important yet not always addressed. The first trait any data scientist needs to succeed is curiosity and hunger to learn. As I always say, data science is an ongoing field; a lot is happing within it all the time.

So, to succeed in this field, you need to be curious, curious about the next trend in the field, curious about the possible problems that you can solve, and curious about the different ways you can solve them. If you have a curious mind, you will always be learning and always be improving, both essential to succeed in data science.

№2: Can Easily adapt to new data and situations

Data science is an interdisciplinary field; it connects maths, statistics, business, programming, design, and science communication. Because of that, data science applications are endless; there are problems data science can solve in every field. So, adaptability is an important trait for any data scientist to obtain.

Being able to adapt to new tools, to new algorithms, new data, new problems, new teammates, and new situations. This is getting more important if you are a freelance data scientist or changing jobs. The ability to apply your knowledge to any application and data can make you either build a great project or fail to come above the crowd.

№3: Critical thinker and problem solver

One of the main tasks expected from data scientists is to find trends, patterns and obtain insights from given data. The key to finding these insights and trends and making use of them is critical thinking and problem-solving techniques. Therefore, you will need to explore data, analyze it and think critically to decide on a model to use in further steps of the project.

Problem-solving and critical thinking also help you decide on which tools to use and which decisions to make. Thus, utilizing these skills can help you save time and effort while working on your project. An important thing to mention here is, your skills to think about data critically will improve as you advance in your career.

№4: Have good communication skills

Having good communication skills is, in my opinion, one of the most essential yet overlooked soft skill every data scientist need to master. Imagine spending hours working on some data, analyzing it, cleaning it, visualizing it, yet you can’t explain your findings to others. Then, all your work would be for nothing because if they can’t understand your findings they won’t be able to act upon it.

A data scientist needs to be good in science communications, and they need to be good at simplifying concepts and only focusing on what really matters. In addition, they should be able to give actionable advice and be good at utilizing tools to create effective visualizations that deliver a clear message.

№5: Have solid technical skills

I intentionally left technical skills to be the last on my list. That’s not to say that technical skills are not important. In fact, you probably can’t become a data scientist without solid technical skills. But, if you’re trying to get into data science or already have, and as the rest of us are working on your skills daily, then you know the technical skills are the core focus of many online materials.

So, you will learn the technical skills anyway because that the skills that are often tested during the job hunting process. I put technical skills last because the other 4 traits I mentioned are often overlooked or not focused on, especially new data scientists. Yet, there are essential to building a career in data science.

Takeaways

We all want to be successful, work hard on ourselves, learn new skills, develop the ones we already have, and work on trying to reach a better version of ourselves all the time; that’s just a part of life. It is also an important part of succeeding in tech as a whole and data science in particular.

Data science is an ongoing field; new algorithms and models are designed every day to offer better efficiency and performance and lower error rate. Also, because of the interdisciplinary nature of data science, you will need to learn various topics and concepts that may not seem related at first. Still, their connections appear once you start building projects and getting involved in the field.

It’s important to remember that each has a unique learning journey, a unique experience, and a unique definition of success. Still, within this uniqueness, we all share some common traits that allow us to show our individuality, prove our skills, and become the best data scientist we could be, so, in short, these common traits allow us to succeed.