Data Science Career Path (With Progression and Skills)

By Indeed Editorial Team

Published November 6, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

A career in data science usually comprises a variety of professional options. Data scientists collect and analyze data using statistical tools and procedures to get meaningful information. Understanding a data scientist's career path can help you evaluate this profession if you want to pursue a career in this field. In this article, we discuss the data science career path, show how you can become a data scientist, share their national average salary, and outline the fundamental skills for this line of work.

Related: Everything You Need to Know about Data Science as a Career

What is a data science career path?

A data science career path refers to all the job positions and education that enable you to achieve both short- and long-term career goals as a data scientist. Most of these professionals start their careers as junior data scientists and progress to senior positions. This professional path comprises mid-level data scientists, senior data scientists, and data managers. Data scientists can progress in their jobs and assume different job roles, including:

Data manager

Data scientists can become data managers or data leaders. These professionals are responsible for establishing a data-based system within an organization, convincing stakeholders to embrace data-driven methods, and overseeing the set-ups of these processes. They can also accomplish the following tasks:

  • recruiting data scientists and adopting essential resources in the company

  • establishing data projects and data management master plans

  • growing the data science team through mentorship and enhancing their skills

  • using data analytics to predict and decide what to incorporate into the production system

  • helping companies increase data quality

Data leaders' skills may differ depending on their workplace and industry. To become a data manager, it's crucial to build experience and fulfill the employers' needs.

Senior individual contributor

Senior individual contributors (senior ICs) begin their professions as data analysts. A data analyst position is an entry-level role for individuals with little or no experience. These professionals perform their duties supervised by an experienced colleague. With over one year of experience, data analysts can become data associates and can work autonomously. They cannot only complete separate tasks which are constituents of a project, but they can also be responsible for the entire project. Data associates might have the authority to plan ways of accomplishing a project or changing the scope of the project toward its goals.

With more experience and expertise, data associates can become senior individual contributors. Senior ICs don't manage other employees but handle their own projects or a specific function in the team. Because of their expertise, these professionals collaborate in various functions and influence team members with no positional authority. To become a senior IC, you require knowledge in data science, an engineering degree, and extensive data knowledge.

Product manager

A product manager is a professional who identifies client needs and aligns them with company goals. They can also perform the following duties:

  • observing market trends and developing viable analyzes

  • coordinate team members to support a project's vision

  • highlighting product features and competence

  • ensuring cooperation and shared knowledge across teams

During their career, data scientists gain and develop some product skills that qualify them as product managers. Typically, some essential traits of product managers include proficiency in product design and expertise in communication.

Data engineer

Data engineers use systems and procedures to gather, control, and convert raw data into useful information. With experience in technical work, including those professionals that perform software engineering duties, data scientists can progress to data engineers. Normally, data scientists are proficient in big data, distributed systems, extract, transform, and load (ETL) coding processes, and machine learning pipelines, which are essential attributes for data engineers. If you wish to move into engineering roles, it's advisable to gain extra skills, including writing quality codes, data architecture, and system design.

Related: What Is a Data Scientist vs. Data Engineer? (With Salaries)

Business manager

Data scientists can progress to become business managers depending on their industry and employer. For instance, those with experience in customer relationship management (CRM) or digital analytics can become marketing managers, while those with supply chain experience can progress to supply chain managers. To assume these roles, data scientists require good business knowledge, which helps them analyze and understand key metrics and problems. Project management skills are also crucial for business managers as they help them formulate business plans, coordinate amongst multiple parties, manage dependencies, and communicate with stakeholders.

Related: How to Learn Data Science (A Complete Guide)

Data scientist career requirements

Here are some requirements to progress in a data science career:


With a bachelor's degree in data science or computer science, you can qualify for an entry-level position as a data analyst. Other related fields, such as IT, mathematics, or business, are acceptable. During the undergraduate level, students gain knowledge in areas including software engineering, coding, data analysis and reporting, data structures, big data platforms, research, and data mining.

For senior positions, most employers prefer candidates with a master's or doctoral degree in data science. During postgraduate studies, data scientists learn more complex ways of examining and presenting a large quantity of data in a more advanced system.


As a data scientist, you can choose a particular field to specialize in and train before starting a full-time position. The training can help you adapt to the work environment and understand how to apply academic knowledge to complete tasks. To increase your proficiency, it's advisable to develop your knowledge through online courses in areas such as big data analytics, programming, and machine learning.


Although certifications aren't compulsory for data scientists, having one shows your proficiency and eagerness to advance your career, and makes you more competitive and beneficial to employers. Some essential certifications include:

  • Advanced Analytics Professional certification: This popular certification provided by the SAS Institute covers topics such as data analysis, data mining and reporting, statistics, SAS programming, and visual analytics. You can enroll in the program after accumulating over six months of programming practice.

  • Data Curation Professional: This training provided by the SAS Institute combines both instructor-led and online training, making it flexible for participants with diverse learning styles. Professionals with an advanced understanding of programming and data manipulation qualify to register for this certification.

  • Data Scientist Associate v2 (DCA-DS) Certification: This program is appropriate for all data scientists, including those at the associate level. It focuses on abstract and logical attributes of analytics and big data, qualifying it for data scientists working in all industries.

Related: How to Become a Data Scientist in 5 Essential Steps

Data scientist's annual average salary

The national average salary of a data scientist is $80,975 per year. The salaries may differ depending on your employer and industry. You can enhance your earning prospects by pursuing a master's or doctorate degree in data science and earning a relevant certification.

Read more: How Much Does a Data Scientist Earn?

Essential data scientist career skills

To advance in your data scientist career, you require both technical and soft skills, which include:

  • Programming: Data scientists apply coding to arrange unstructured datasets. It's vital to be proficient in the most popular programming languages.

  • Knowledge in analytical tools: Analytical tools assist data science experts to gather significant information from an arranged data set. You can get knowledge through certifications in standard analytical tools.

  • Proficient with unstructured data: Professionals in this field work with unstructured data from dissimilar sources. Having the skills to extract relevant information, which can provide useful insight when making business decisions, is paramount.

  • Machine learning and artificial intelligence: Machine learning computerizes data analysis processes and instantly makes data-informed predictions. Data scientists monitor the realization of machine learning by arranging data and using appropriate tools to get maximum information from data.

  • Probability and statistics: Data science embraces the use of algorithms and statistics systems which apply estimations and predictions when analyzing data. Data scientists use probability and statistical procedures to prepare estimates for advanced analysis.

  • Communication skills: These scientists interact with other team members and stakeholders when analyzing data. Effective communication skills help them communicate their findings simply.

  • Problem-solving skills: These scientists spot business prospects and employ data to explain potential challenges and propose solutions.

Related: Data Science Specialization: Types, Benefits, and Tips

Please note that none of the companies, institutions, or organizations mentioned in this article are affiliated with Indeed.

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