Data science is one of the fastest-growing career fields because companies continue to collect more information about consumers, making job opportunities in the field increase. If this sounds interesting to you, consider earning your degree in data science and help sort vast amounts of data into useful information for various industries. Understanding the various available career paths in this field may help you decide the right job for you.
In this article, we discuss what a data scientist is and list nine of the highest-paying data scientist jobs, including sharing the salaries and job duties.
Who is a Data Scientist?
Data scientists collect, analyze, and interpret complex and large data sets to extract insights to make data-driven decisions. They use statistical and computational techniques to clean, process, and transform raw data into meaningful information. These experts can work with a wide variety of data, including structured data, like numbers and dates, and unstructured data, like text and images. They collaborate with various stakeholders, including business managers, engineers, and other data specialists, to understand the business needs and provide insights to help organizations achieve their goals. Data scientists work in various industries, including health care, finance, e-commerce, and social media.
Data scientists can come from various academic backgrounds and career experiences, which include:
Mathematics and statistics: Data science requires a strong foundation in mathematical and statistical concepts, so many data scientists have degrees in mathematics, statistics, or related fields.
Computer science and programming: Data scientists also are proficient in programming languages such as Python, R, and SQL, so specialists have backgrounds in computer science or other related disciplines.
Engineering: Some data scientists have backgrounds in engineering, particularly those who work with data in fields like manufacturing, energy, and transportation.
Natural sciences: Data science is also essential in fields such as biology, chemistry, and physics, where massive amounts of data get generated and analyzed.
Business and economics: Many data scientists work in industries like finance, marketing, and e-commerce, so backgrounds in business, economics, and related fields are helpful.
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9 highest paying data scientist jobs
1. Data analyst
National average salary: $70,676 per year
Primary duties: A data analyst collects, cleans, analyzes, and visualizes data to derive insights. They use tools such as spreadsheets, SQL, and Tableau to identify trends and patterns in data and use the results to help organizations make data-driven business decisions. They also create reports and visualizations to communicate their findings to stakeholders. Data analysts have strong analytical and problem-solving skills and communicate complex information to non-technical audiences. These specialists can work across various industries, such as health care, finance, marketing, and retail.
2. Business intelligence analyst
National average salary: $82,559 per year
Primary duties: A business intelligence (BI) analyst analyzes data to provide insights and recommendations to business leaders. They work with stakeholders to identify BI needs and develop reports and visualizations that help the organization make informed decisions. They have strong analytical skills, a deep understanding of data analysis tools and techniques, and the ability to communicate complex data insights clearly and concisely to non-technical stakeholders.
National average salary: $86,960 per year
Primary duties: A statistician collects, analyzes, and interprets numerical data, applying statistical principles to solving challenges. They design experiments or surveys, collect and analyze data, and present their findings to stakeholders. In addition, statisticians might be involved in developing new statistical methods, software, or tools to improve the efficiency and accuracy of data analysis. Statisticians help organizations make informed business decisions based on empirical evidence and statistical analysis.
4. Business intelligence developer
National average salary: $94,253 per year
Primary duties: A BI developer designs, develops, and maintains the data infrastructure that supports an organization’s BI needs. This includes developing data models, building data warehouses, and designing and implementing extract, transform, and load (ETL) processes to ensure data is accurate, accessible, and organized to support analysis. They also work closely with the BI analyst to ensure the data infrastructure supports the organization’s reporting and analysis needs.
5. Data modeler
National average salary: $102,376 per year
Primary duties: A data modeler designs and maintains the data architecture of an organization, which involves creating and managing complex data models that facilitate the storage, manipulation, and retrieval of data. The duties of a data modeler can include analyzing business requirements, defining data architecture standards, creating data models, developing data dictionaries, and ensuring data integrity and security.
6. Data scientist
National average salary: $124,693 per year
Primary duties: A data scientist analyzes, interprets, and organizes large and complex data sets. This involves gathering, cleaning, and transforming data into a form others can analyze using various statistical techniques and machine learning algorithms. Data scientists have excellent programming skills, proficiency in statistical modeling and data analysis, and a solid understanding of database technologies. They also work with stakeholders to understand business needs and provide insights that help inform business decisions.
7. Data Architect
National average salary: $125,977 per year
Primary duties: A data architect designs and implements data solutions for organizations. They analyze business requirements, design and implement data models, define data governance policies, and ensure data security and privacy. Data architects also oversee the implementation of data solutions, monitor performance, and ensure the data architecture remains up-to-date and relevant. Additionally, they often collaborate with cross-functional teams and manage data-related projects from conception to completion. They also work closely with stakeholders to understand their data needs and create solutions that meet those needs while aligning with the organization’s overall business strategy.
8. Big data engineer
National average salary: $128,631 per year
Primary duties: Big data engineers manage and analyze large and intricate data sets and design and maintain data pipelines to transfer data from diverse sources into the company’s data storage systems. Additionally, big data engineers have expertise in various data management technologies, such as Hadoop, Spark, and NoSQL databases.
9. Machine learning engineer
National average salary: $150,186 per year
Primary duties: A machine learning (ML) engineer develops, designs, and implements machine learning algorithms. They also produce new algorithms or enhance existing ones to improve model accuracy, train models on large datasets, and evaluate model performance. Additionally, machine learning engineers design and implement scalable ML systems that can handle large amounts of data and deploy in production environments. Machine learning engineers work closely with data scientists, software engineers, and product managers to develop and deploy machine learning solutions.
Factors affecting salary
Data scientists are highly skilled, educated employees and may earn a good salary. Factors that may influence their salaries can include:
Location: Some geographic areas provide higher incomes than others. Usually, data scientists may find higher-paying jobs in large metropolitan cities, like San Francisco, versus smaller cities or rural towns.
Experience level: Data scientists may earn more money the longer they work in the field.
Seniority: Staying with the same company for several years can allow you to make more money than moving around to different companies.
Skills: You may earn more in a data science job if you continue to develop your skill set. For example, you can market yourself as a valuable asset to employers by showcasing your skills in artificial intelligence (AI), data mining, machine learning, data analytics, and information technology (IT).
Education: To earn higher salaries, you may consider pursuing advanced education like a master’s degree or data science certifications.
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