Curious about following a data science career path? Just about every company relies on data to drive their decision-making and data scientists or analysts are the ones who interpret it.
While a simplified explanation, the reality is that a data scientist or analyst is on the front lines of a business’s relationship with the data they collect. And as companies collect even more information, working with and understanding data is becoming a bigger priority.
Companies need data science professionals who can mine data, understand what the data means, know how it should be interpreted, and understand what implications the data has for the company.
Careers in data science or analytics are some of the most in-demand jobs.
Let’s look at what is behind this growing job field and how to know if it is the right career for you.
Three huge economic sectors employ the majority of data scientists or analysts: finance, insurance and professional services.
The rising need for data analysis in just these fields alone makes it clear that this job will be in demand for the foreseeable future.
The need for data scientists or analysts is outpacing people entering the career field.
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But as a data scientist or analyst whose position is in demand, those with formal training and little to no experience may still be able to find an excellent position.
Data science straddles the IT and business world. Those who enjoy analyzing complex business datasets will always be in demand by the growing field of companies that utilize data.
Data science will only continue to expand as well as more teams begin to use data to make decisions, not just engineering. There will likely be data science professionals working on most marketing and cross-functional teams in the very near future.
When choosing a career path within the data science field, candidates should think about both the hard and soft skills that will be required for the job.
Data science is more than just programming Python. In most cases, it will require interpersonal skills that help you complete complex projects.
Data science professionals are able to work cross functionally and deliver results as a team.
Although entry-level data scientists may spend their time data mining for a bigger project, over time that role will change. Leaders in data science also focus on helping to distill complex concepts to teammates or developing new processes to streamline the collection of customer information.
Hard skills are specific abilities that relate to a career and might be learned or earned, like a certification. Some typical hard skills for those in the data science field are:
Soft Skills are abilities that allow you to do your job. Soft skills may not be as easily defined on a resume since these are more abilities that are shown through work and interactions with others. Some of the commonly needed soft skills in this field are:
A person who is successful in a data science career is a mix of a mathematician, computer scientist and a trend-spotter. Data scientists are curious and analytical.
This job works with both IT and the business world to link together mining and interpreting data and then evaluating that data for the business itself.
A data scientist will use a creative, problem-solving approach to look at the trends the data exhibits and then communicate with decision-makers. Data science is more than just looking at sales data.
Through storytelling, a data scientist will be able to explain trends in the data and what they mean to a business’s potential future actions. This type of job may work on a team or lead a team of others.
Data science is as much about understanding what the data says as it is helping your team get on the same page.
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This job involves working with raw data.
You will first collect the data needed by a company and then work on interpreting it.
Working on a team or leading another group of analysts, a you will use the data to help solve problems and answer questions.
Using the data, analysts help to uncover trends and meaningful patterns to assist businesses in creating future actions. Data analytics professionals are typically comfortable with a wide range of technical programs and tech tools and are also skilled at coding.
The soft skills a data analyst utilizes in this job reflect the responsibilities of communicating the data findings and interpretations of trends or problems to others within the organization.
Just like other science roles, becoming an analyst will require a specific set of hard and soft skills. In addition to typical data scientist skills like database languages and Python, you'll also need to be passionate about helping non-technical teams understand your results.
A career as a data engineer will focus on creating and maintaining databases rather than interpreting data like an analyst or data scientist does.
This field is highly collaborative as they work in teams with not only other data engineers, data analysts, and machine learning engineers.
They also work with other developers, product managers, and other cross-functional teams.
A data engineer should be comfortable not only creating the databases but discussing them with technical and non-technical audiences. They may be called on to generate presentations to describe and explain their findings to stakeholders or other business teams.
Data architects are responsible for designing data systems and architecture, determining how data is stored and consumed by different data entities.
Data architects are data scientists whose skills are in demand at organizations of all sizes.
Data architects define how applications use and process data.
Data architects have to conceptualize the data systems that a business uses so that the company can make the most of that data. This includes building data warehouses or central repositories for KPIs and other key business metrics.
Like any good data science pro, they will seek out weaknesses or problem areas within the data system and work to improve or upgrade it. Data architects help companies move from outdated, siloed systems to more integrated, modern data architecture.
One of your key responsibilities as a data architect will be to develop data management systems. What data gets prioritized? How is it organized? How will it be displayed to the rest of the team?
A business intelligence (BI) position is a specialization within the field of business analyst.
A BI professional reviews and interprets data to help inform decision-making within a company.
This job will require working with internal data from the sales or marketing departments as well as external data that could come from surveys or market research.
A BI analyst helps business leaders make decisions within the company through thorough interpretation and reviewing of data for market trends, internal improvements and increasing efficiency.
A BI professional will spend a lot of time translating their analysis into non-technical language so excellent communication skills are required in this job.
People in this career are excellent at asking questions to better understand trends and patterns.
While still in the realm of data analysis, a statistician will use mathematical models to identify statistical trends in data. Statistics and data science often work hand in hand.
A statistician may create new data sets by administering polls or tests for a business based on analysis needs.
Someone in this job must also be an excellent verbal and written communicator because a statistician will need to be able to explain highly technical math concepts without using technical language. Data science isn't an innate skill most of us!
Statisticians must be able to easily determine the validity and relevance of data to protect the accuracy of data evaluation and analysis.
Leadership ability within the statistician career is helpful since this job requires critical thinking about data in the same way a leader does and then using that lens to interpret and analyze the data as well.
The core of entry-level positions as a data scientist and an analyst are much the same.
Both an entry-level data scientist and an entry-level data analyst will answer questions about data.
But a data scientist will use the context of data to answer questions about a business while a data analyst will answer questions about historical data and even run A/B testing on products to create new data, both to guide a business decision.
While both are analyzing data, the focus of a data scientist and a data analyst is very different.
Being interested in working in data science presents you with lots of options for your career. That's especially true for junior data scientists who may not yet know the direction they'd like to go!
But choosing the right path for your data science passions and abilities means looking carefully at what you want out of your professional life in the long run.
What do you want your day to look like and what kinds of tasks fit best with your skills and abilities?
Even a senior data scientist may struggle to answer these questions! Luckily, this career path allow for flexibility and growth long-term.
Jobs that work with teams: Data Science, Data Analyst, Data Engineer, Statistician
Careers that work independently: Data Architect, Business Intelligence
Jobs that require excellent communication/presentation skills: Data Science, Data Analyst, Data Engineer, Business Intelligence, Statistician
Jobs that require high technical expertise: Data Science, Data Analyst, Data Engineer, Data Architect, Business Intelligence, Statistician
Careers that create data sets: Data Science, Statistician
Jobs that manage and create data storage: Data Science, Data Engineer, Data Architect
Unfortunately, as recently as 2020 fewer than 15% of the data science field was comprised of females.
And as a statistician can tell you, a lack of diversity within any given data set weakens it!
According to a 2020 BCG Women In Data survey, between 40 and 63% of women worldwide see data science in a negative light and this disproportionally affects women’s entrance into the field.
But schools and companies are working to dispel some of the incorrect perceptions about data science and the STEM field. They're creating programs to introduce young women and girls to the world of data even earlier. Ultimately, these programs encourage women to pursue computer science degrees and careers in tech.
Negative views of the data science career path brightens when the job’s realities are more transparent. Schools and businesses hope to reverse this trend to encourage women to join this career in greater numbers in the future.
We hope this article has helped you learn more about the data science career. If you're an aspiring data scientist, you'll likely have a series of interviews in your future!
Luckily, Exponent helps folks just like you ace their upcoming data science or machine learning interviews.
Check out our Data Scientist Interview resources to help you do just that:
💬 Review more commonly asked data science interview questions.
📖 Read through our company-specific Data Scientist interview guides
👯♂️ Practice your behavioral and leadership skills with our mock interview practice tool.
👨🎓 Take our complete Data Scientist interview course.
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