Find Your Degree
This is an advertising-supported site. Featured or trusted partner programs and all school search, finder, or match results are for schools that compensate us. This compensation does not influence our school rankings, resource guides, or other editorially-independent information published on this site.

Five High-Paying Data Science Jobs

High-Paying Data Science Jobs

Five Most Lucrative Data Science Jobs

  • Chief Data Officer
  • Data Scientist
  • Data Engineer
  • Data Architect
  • Data Analyst

Data science is a wide-ranging field that focuses on capturing, processing, maintaining, analyzing, and communicating details about data. The concept of the modern data scientist is relatively new and has come to the forefront of business analytics with the emergence of computers capable of handling massive amounts of data. Anyone who wants to work in a cutting-edge field of science may want to consider training as a data scientist. One of the benefits of working in data science is the high salaries available in most jobs. Here are five of the most rewarding and high-paying data science jobs available today.

 

1. Chief Data Officer

Data Science Jobs

This is among the most lucrative and rewarding job in data science career field, if not the most. Chief data officers (CDOs) are senior executives responsible for managing and implementing an organization’s data. They also govern the collected information and assist with data-driven business decisions and strategies, in an advisory capacity. Their job description revolves around ensuring that their organization benefits from the available data.

It is a high-profile, C-suite position with a median annual salary of about $192,812. There are not so many vacancies out there because the role emerged recently although market indications show that it is on a rise. Prerequisites include knowledge of various Big Data tools, an extensive managerial experience, a strong understanding of all the relevant concepts, and good communication skills.

There isn’t a single route to becoming a chief data officer, and those interested in attaining this high-level position may want to focus on degree programs in management information systems (MIS), data science, statistics, or business analytics, or any other related field, for their education. In many cases, earning a Ph.D. will offer the best opportunity to become a chief data officer; however, earning a graduate degree and spending some time working at the management level in business may earn one the status needed to rise to the executive branch at a company.

Some of the courses commonly offered in Ph.D. in Data Science programs include Integrative Data Science, Data Analytics and Mining, and Mathematical Analytics. Most prospective Ph.D. students will need a Master of Science in Data Science or a related graduate degree to qualify for enrollment in a doctoral program. While enrolled in the program, a student will focus on a topic for his or her dissertation, and that topic should align with a facet of the business world in which the student would like to eventually study.

Some of the essential skills required for a data scientist include excellent communication skills and analytical skills, as well as leadership skills that are essential in the role of an executive. The chief data officer will need to coordinate and lead a team that includes senior data engineers, senior data scientists, heads of data science, heads of analytics, and a handful of other data science experts. The chief data officer must be aware of trends in technology, engage in regular team-building efforts, and have superior verbal communication skills when interacting with the team. Possessing these skills will allow a chief data officer to thrive in his or her position and earn an industry-leading salary.

2. Data Scientist

Data scientists employ their knowledge and skills in programming and statistics to provide actionable insights from raw data. They identify the right dataset, clean out the analysis, and extract insights from the said analysis. Then they communicate a simplified version of their results to the decision makers. They also advise the executives by explaining the effects of the findings (data) on a particular process or product and the expected consequences of certain actions.

Most employers require a bachelor’s or master’s degree in statistics or mathematics. Five or more years experience is an added advantage. On top of a host of perks and benefits, Data Scientists earn between $106,529 to $137,037. That means that the average data scientist earns about $122,258 per year.

In business, a data scientist advises their business through the use of data mining and statistical analysis, and these techniques are meant to improve business performance. Data science jobs are available as soon as the scientist earns his or her bachelor’s degree, but there are opportunities for advancement when the data scientist chooses to earn a graduate degree. Some of the topics discussed in the average data science program include statistical learning, probability & statistics, and machine learning. Most data science programs also include a significant number of classes in traditional computer science and computer programming, where topics discussed will include algorithms, programming, and data structures.

The traditional degree required for work as a data scientist is the Bachelor of Science in Data Science, and the advanced degree is generally a continuation of that degree and styled as s Master of Science in Data Science. After taking some time to work in the industry, a data scientist with a bachelor’s degree may want to continue his or her education in a graduate degree program that may allow the scientist to advance his or her career and earn a higher salary. It may be worthwhile to compare the cost of a graduate degree with the expected increase in income to determine whether the degree is worth the time and money.

More than a quarter of all data scientists work for the federal government and another large percentage work for businesses involved in computer systems design. Others work in research & development, as well as for software publishers, and in the educational sector for colleges and universities. Data scientists should be comfortable sitting in front of computer screens for long hours, as well as comfortable working with mathematical calculations, algorithms, and computer programming.

3. Data Engineer

Good Paying Data Science Jobs

Data engineers are an essential part of any organization. They see to it that the Data scientists and Data architects employ the appropriate management systems and manipulate the right kind of data. Their work involves enabling their colleagues by sorting out messy, unstructured Big Data and providing a usable end-product for analysis.

They work in close tandem with Data architects but tend to have a more in-depth understanding of the various data languages and tools. An entry-level data engineer makes an average of $90,083 per year while a senior data engineer earns up to a whopping $123,749 annually.

A data engineer is an essential part of the overall data science industry because the information that data scientists use isn’t available without the data pipelines and infrastructure built by the company’s data engineers. One of the most difficult facets of the data engineer’s job is developing and deploying new systems for data scientists to use. The data engineer’s role is heavy in computer programming, frameworks, and databases. In many cases, the starting salary for a data engineer exceeds the starting salary of a data scientist, but experience in either area can eventually lead to the highest-paying jobs in computer science.

The traditional data engineering curriculum begins with the fundamentals of data science, machine learning, and predictive analytics. Classes will delve into programming languages like python, as well as topics like data management, data ethics, and big data. Classes may include Introduction to Data Visualization, Language Analytics, Skills for Data Professionals, and Apache Spark. Electives that may be available include those on topics like quantum computing, advanced machine learning, forecasting, and artificial intelligence.

One of the reasons the salaries are high for data engineers is because they must act in the capacity of a manager where they direct the tasks of a team of j7nior data engineers and other support personnel. Not only must the data engineer optimize the business’s current databases, but he or she must also explore new technologies and engage in regular benchmarking of new platforms. The data engineer may receive guidance within the business from the head of data science or the chief data officer.

4. Data Architect

In most companies’ structures, Data architects slot slightly below data and earn a tad less. Data architects are responsible for designing a workable data management blueprint. They design the stages of a data infrastructure project by defining the storage, consumption, and management of the said data.

One needs a bachelor’s degree in mathematics, statistics, computer science, or a related field, and tons of technical skills to qualify. The average salary for an entry-level data architect is $74,809 per year, while a senior-level data architect earns a median annual salary of $136,856.

The role of the data architect is related to that of the data engineer because the two data science professionals must work together in the building of enterprise data management frameworks. At one time, data engineering was the sole role within this area of data management, but the data architect has emerged as a separate and wholly necessary part of the equation. According to the Bureau of Labor Statistics (BLS) and their occupational outlook for computer network architects, the growth rate for these professionals will be faster than average, with about 8,000 new data architects needed in the next decade.

Some of the courses a future data architect might take while enrolled in a bachelor’s degree program include those on data warehouses, Apache Spark, systems thinking, and data architecture. One of the options available to data architects is to become certified through a company like IBM, which offers an IBM Certified Data Architect exam. Certification may lead to higher salary opportunities. Some of the prerequisite skills that are necessary to pass the exam include understanding data replication and synchronization, cluster management, network requirements, data modeling, latency, and disaster recovery. Software types the data architect must know include Hadoop, BigInsights, Cloudant (NoSQL), and BigSQL.

In addition to earning status as a Certified Data Architect, it may also benefit a data scientist to enroll in a graduate program. For example, a student who earns a bachelor’s degree in an area like computer science, computer programming, or data analysis may want to consider a Master of Science in Data Architecture to facilitate a move into the role of data architect at a business. Senior positions are often more readily available to applicants who possess a graduate degree, even though entry-level positions usually only require a bachelor’s degree.

5. Data Analyst

5 High-Paying Data Science Jobs

Data analysts crunch and analyze large amounts of data to reach appropriate logical conclusions. They simplify large amounts of data, whether research-sourced or domain-specific, to provide useful insights that ease the decision makers’ jobs. Their role differs from that of data scientists because while data scientists provide predictive mathematics-based analytics, they (data architects) provide analysis based on the components of data architecture.

The profile requires a low starting point compared to the rest. It is a good place to start if for those who want to advance to careers in more lucrative fields such as data science and data engineering. The mean annual salary is $55,804 for startups and rises up to a mean of $88,532 with more experience.

To advance one’s salary as a data analyst, the best options are earning an advanced degree, earning certification, and spending some time in the industry before choosing a specialized path. Some of the areas available for specialization for the working data analyst include data visualization, data warehousing, data mining, and statistical analysis. Other areas of focus include machine learning, operations-related data analytics, and data engineering.

While still in high school, a future data analyst may want to focus on science, computers, and mathematics for electives with at least four years taken in mathematics and four years taken in science. When enrolling in a bachelor’s degree program, some of the topics discussed may include predictive statistics, algorithms, data visualization, machine learning, and statistical analysis. Anyone unsure as to whether they want to enroll in a complete bachelor’s degree program may want to begin with some basic online classes that will allow the student to become familiar with the knowledge of a data analyst and help him or her decide whether the job is something he or she wants to pursue.

Some of the qualities that are essential for a data analyst and which may help the entry-level analyst attain a higher salary include having great attention to detail, possessing a creative mind for problem-solving, and having excellent communication skills. Although a data analyst may stare at a computer for many hours each day, he or she must also know how to effectively communicate with coworkers and managers about current projects.

Conclusion

One of the benefits of working in data science is the superior growth of the profession as a whole. An article from Forbes Magazine cites data from IBM that suggests the demand for data scientists will increase by 28 percent by 2020. Not only can a degree in or knowledge of data science help an individual find quick work, but it can also result in an impressive salary that is far above the nation’s average. Becoming a data scientist is an excellent path to an interesting and well-paid career.

Related Resources: