To maximize its value, companies need data scientists to help them make sense of the data they collect. As a result, the demand for data scientists is booming — this means interest in pursuing a career as a data scientist is booming, too.

Data science careers are attracting all sorts of individuals, from college students interested in technology to IT professionals seeking a career change to nontechnical individuals looking for professional growth in a technical field.

How do you know if you’ll be happy in a data science career? These 10 clues may help you determine whether a data scientist role is right for you. If you decide yes, be sure to check out the career resources at the end of this article.

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Who you will work with in the data scientist job market

Due to the increasing importance of data-driven decision-making across industries, the U.S. Bureau of Labor and Statistics projected data scientist employment to grow 36% from 2021 to 2031, with about 13,500 openings per year. These openings will be driven by increased data production and replacing workers who transfer to different jobs or retire.

While the United States is one of the major employers of data scientists, other countries like Switzerland, United Kingdom, Australia, Israel, Canada, China, India, Netherlands, Germany, France and South Africa are also top data scientist employers.

In terms of demographics, the data science field has been male-dominated for decades, with men accounting for 79.6%, according to Zippia. Similarly, racial and ethnic diversity in the data science field is low, with white individuals making up 64.2%, followed by Asian (18.8%), Hispanic and Latino (6.9%), Black or African American (4.2%) and American Indian and Alaska Native (0.5%) individuals.

Further research also reveals that most data scientists are middle-aged — about 49% of professionals in the field are above 40 years old. The age group of 30 to 40 comprises about 30%, while those aged 20–30 comprise around 22%.

Note: At the time of this article, limited research has been conducted on the global outlook of the data science job market. Therefore, U.S. statistics have been used to highlight current data science job market and demographic trends.

1. You don’t like statistics, mathematics and coding

Data science professionals heavily rely on their knowledge of statistics, mathematics and coding to analyze and manipulate data. As a data scientist, you must be able to use languages like Python and R to create machine-learning models and work with large datasets.

A great understanding of statistical concepts and key statistical formulas, plus the ability to interpret and communicate statistical results, is also a prerequisite. If these subjects do not resonate with you or if you struggle to fully grasp their concepts, it may be an indication that pursuing a role as a data scientist is not the right fit.

2. You don’t like to work for long periods of time alone

When learning a new concept, it’s usually a best practice to occasionally isolate yourself to focus and fully immerse yourself in the subject matter. This is especially true for data scientists who often need to spend long hours analyzing data and developing complex models.

Data science can require extended periods of concentration. This work is best done alone and with minimal interruption. If you are a highly social person and desire constant interaction, a data science career might be too isolating.

3. You’re not detail-oriented

Attention to detail is important in data science because you’re constantly working with statistics, data, and the mathematical and logical algorithms you develop. Data science work may require you to dig into complex datasets, which require a high level of attention.

When you don’t pay attention to even the littlest detail, it can lead to inaccurate results or flawed conclusions, hence why you must be continuously analyzing outcomes of your work. An individual with a detail-oriented mind is the best fit for this activity.

SEE: Download our guide for building a successful data scientist career.

4. You don’t like tedious tasks

A great deal of time is consumed in data preparation, a tedious activity that involves standardizing data terms and cleaning up data so it can be used. There is nothing glamorous about this work, but if you don’t do it, you won’t have a good set of data to run your algorithms against. You should ask yourself if a job with lots of data preparation tedium will make you happy.

5. You don’t have a strong technical or engineering background

A strong background in engineering, IT, statistics and data modeling is important for data scientists. Data science involves working with complex algorithms, statistical models, programming languages and large datasets.

While not impossible, if you come from a background other than this, you may struggle to comprehend and apply the concepts and methodologies required in this field without additional training.

6. You can’t accept failure

Data science is experimental work. You’re trying different algorithms against different combinations of data. Like most researchers, data scientists encounter many failures because not every algorithm or theory works.

Great data scientists know when to quit and when to try another approach. When they fail, they try again. If you feel you might not have the resilience to accept repetitive failures, a data science job might not be for you.

7. You like predictable work

Data science is anything but predictable. Data scientists often work on varied projects, analyzing different datasets and applying various techniques. Sometimes, unique insights can turn up from nowhere. In other cases, what you think was a good formula or algorithm to apply to the data yields nothing.

A data scientist job is not for someone who likes coming to work each morning already understanding how their day is going to go. If you prefer strict routines or find repetitive tasks more appealing, the dynamic nature of data science may not align with your preferences.

8. You don’t like desk work

If you like IT but also like to move around physically and take a break from your desk, a job in networks, telephony or operations could be a better fit. Because of the detailed nature of the work, data scientists spend long hours at their desks analyzing and manipulating data, coding and implementing algorithms. If you dislike desk work and prefer to be more physically active or engaged in hands-on tasks, a data scientist job may not be the best fit for you.

9. Data bores you

Being a data scientist is all about data. You have to clean data, put it together, process it, analyze it and then do more of the same.

It helps if you enjoy the challenges that come with working with data. If handling and exploring data doesn’t excite you or if you find it tedious and uninteresting, it indicates that you may not have the innate curiosity and passion for data-driven problem-solving required in this role.

10. You don’t understand how to translate business problems into data algorithms

You’ll need more than a degree in data science for a data scientist position. Your internal customers will expect you to understand the business, as well as the sticky business problems they want you to help them solve, with data science.

A skilled data scientist knows how to get to the core of a business problem and find the right data. If you feel you lack this business savvy or instinct, a data science career might not be a good fit.

Does being a data scientist seem right for you? These resources can help

If you find yourself aligning with the qualities and skills required for a data scientist, you may be on the right track toward pursuing a career in this field. Explore our data scientist cheat sheet as well as our guide on what questions to ask during a data science job interview.

These guides will allow you to further explore and improve your understanding of data science and determine what kind of company you would best fit in as a data scientist.

SEE: Does data science seem like an ideal career for you? Get a head start with these top data science certification courses.

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Learn the latest news and best practices about data science, big data analytics, artificial intelligence, data security, and more. Delivered Mondays and Thursdays