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• [toc headings="h2" title="Table of Contents"] The need for data scientists has never been greater. The high demand for professionals in this varied and fast-growing field makes it an appealing career option for tech professionals who love mathematics and have an analytical mind. The data science career track also offers strong future opportunities, projected by the Bureau of Labor Statistics to grow by 35% through 2032. One appealing thing about working in data science is that there's not just one way to make your living in this sector. There are a few different paths that a data science career can take, whether you hope to reach the executive level or are content as an individual contributor. This article will explore a few career paths in this sector to help aspiring professionals plot out their career. 

• Preparing for a career in data science

• Data scientists typically have a strong background in statistics, math, computer science, or a similar technical field. You'll need some proof of your skills in these areas on your resume in order to land a position in this industry. This doesn't always need to mean a four-year college program, however. While most data scientists do have at least a Bachelor's degree, anyone who understands how to gain insights from data can thrive in this sector, and there are other ways to show this to employers than a college degree. To that point, let's take a closer look at the key data science skills and the typical process to acquire them. 

• Required skills for data scientists

• Like for many roles, excelling in a data science job requires a mix of technical and soft skills. On the technical skills side of things, these professionals need expertise in areas like:

  • Programming languages - To organize, manage, and analyze big data sets often means using code, particularly in roles on the technical side of the industry. Python and R are the most-used languages, though familiarity with SQL, SAS, and Java can also be helpful. 
  • Statistics and probability - Particularly for writing machine learning algorithms and models, understanding of statistical analysis concepts and techniques is crucial. This includes Bayesian statistics, linear regression, probability distributions, and over- and under-sampling, just to name a few key concepts.
  • Data wrangling/Data cleaning - To make large datasets easier to work with and analyze, they first need to be manipulated. This often involves sorting it into categories, correcting erroneous data points, or transforming data from various sources into a format appropriate for query and analysis. 
  • Database management - Something else data scientists are often responsible for is gathering, organizing, and maintaining the information stored in databases. Familiarity with tools like MySQL, Oracle, and MongoDB is helpful for getting started in this field. 
  • Machine learning and deep learning - These techniques allow professionals to not just gather and analyze data more efficiently, but also to make better predictions based on future data. Some algorithms to know include logistic regression, linear regression, decision trees, random forests, or Naive Bayes, just to name some common options. 
  • Data visualization - Charts, graphs, and other visual models are helpful for conveying insights from data to non-technical stakeholders, and are crucial for using data to tell a story. Aspiring data scientists should gain familiarity with tools like Excel, Power BI, and Tableau, that are often used to visualize data.
  Along with these technical areas of expertise, data scientists need to possess certain soft skills, such as:
  • Problem solving - Data scientists are often called upon to find solutions to challenging, complex issues. The ability to work through a problem and break it down step-by-step to find the solution is very helpful in this field. 
  • Business acumen - The goal of data analysis is often to inform future business decisions. Providing those kinds of insights is easier when you understand the goals, processes, challenges, and potential opportunities, both of the specific industry and for businesses in general.
  • Teamwork - A lot of steps and effort are involved in collecting and analyzing data. Because of this, data scientists often work as part of a team, and should be able to collaborate effectively in that environment. 
  • Interpersonal skills - Even data scientists who work independently need to convey the results of their analysis to other people in the business, which means having well-developed presentation and public speaking skills. They also need strong listening skills to understand what the company wants to learn from a data set, so their communication skills need to be strong.

• Typical education and training for data scientists

• The first step toward a career in data science for many is earning a college degree. Often, students interested in data science will take courses related to those technical skills mentioned above. The most popular degrees for aspiring data scientists include computer science, data science, mathematics, statistics, engineering, and information technology. Other students focus more on the business side of the equation. If you know you're interested in the business intelligence side of the field, a degree in business administration, finance, or economics could be a smart move. Some senior professionals also go on to earn an MBA or similar Masters degree. That said, a college program isn't the only way to gain this knowledge. Bootcamps are a faster and more affordable way to acquire these skills, and can often lead directly to employment in the field. Popular data science bootcamps include the Flatiron School Online Data Science Bootcamp and the Springboard Data Science Bootcamp, though there are several more out there to explore if you're considering this option. Whether you start with a degree program or a bootcamp, certifications can also be an effective way to add skills to your toolbox and demonstrate them to employers. These certificates can be obtained through schools and professional organizations, often by simply taking an online course and passing a written exam covering the key concepts.  Some of the most sought-after certification programs for data science include: 

  • Certified Analytics Professional (CAP)
  • Google Professional Machine Learning Engineer
  • Microsoft Certified Azure Data Scientist Associate
  • Open Certified Data Scientist (Open CDS)
  • SAS Certified Advanced Analytics Professional
  • Senior Data Scientist (SDS) from the Data Science Council of America (DASCA)

• Data science career paths

• There are two primary tracks that a data science career can take. Some professionals focus on the technical side of the field, leveraging their skill with data manipulation and analysis, as well as skills like programming or algorithm development, to advance their career. Other people focus more on the business insight area, deriving value from an organization's data and using their ability to interpret data to solve a problem. Here is an introduction to some of the most common job titles in each area.

• Entry-level roles in data science

• Whether you plan to focus on the technical or the business side of data science as you grow your career, you'll likely look for the same types of roles when you're first starting out. Here are some common entry-level roles for data science professionals and what each one entails. 

• Data science intern

• Average salary: $53,000 per year Typical education: Bachelor's degree or current student in a degree program An intern's role is usually to assist other data scientists in collecting and analyzing data. Often, the bulk of the day-to-day work will be cleaning data and preparing it for statistical analysis. It can also include organizing datasets, preparing reports, and sharing your findings with other members of the team. In some cases, you may even help develop new machine learning models or algorithms, or model and visualize data. The opportunity to get exposure to a wide range of data science tools and processes is the main advantage of taking an internship. 

• Junior data scientist

• Average salary: $77,000 per year Typical education: Bachelor's degree in computer science, engineering, or a related field The tasks given to a junior data scientist are similar to those assigned to more senior members of the team, and are primarily focused on data analysis, and communication of your findings to other team members or stakeholders. The primary difference is that a junior data scientist normally works under the direction of a more senior team member, rather than steering projects themselves. They're also less likely to work with machine learning models or large, complex datasets than more experienced data science roles. 

• Junior data analyst

• Average salary: $73,000 per year Typical education: Bachelor's degree in computer science, statistics, or a related field The primary responsibility of a junior data scientist is data wrangling. These data professionals usually manage and analyze big data, using techniques like regression analysis, data visualization, and similar data science skills, often under the supervision of more experienced analytics managers. They may also assist other members of the data analytics team with things like cleaning data and the organization of data sets, supporting their timely completion of projects. 

• Junior business analyst

• Average salary: $78,000 per year Typical education: Associate's or Bachelor's degree in business administration, data analytics, or a related field Like the job title suggests, business analysts are primarily responsible for analyzing data on the past performance of a company in order to improve their future decision making. The ultimate goal is to use the knowledge and insights gained from analyzing data to solve business problems. The main difference between business analysts and other data science occupations is that these professionals focus on the practical applications of data insights and analysis concepts. 

• Junior data modeler

• Average salary: $98,000 per year Typical education: Bachelor's degree in computer science, mathematics, or a related field There are two primary aspects to a data modeler's role. First, they're responsible for building and maintaining databases that companies use to organize their data. Second, they perform data modeling, creating visual representations like graphs, tables, and charts that show the relationship between data points and make it easier to identify trends and patterns. Basic knowledge of tools like Microsoft Excel and SQL or NoSQL databases is often required for individuals pursuing this data science job, though junior modelers may not have as much expertise in these areas as more senior counterparts.

• Technical career path: Mid- to senior-level roles

• After gaining some experience in an entry-level role, there are a variety of roles that open up for professionals with a specialization in the more technical side of the industry. Here are some of the common jobs in organizations that hire data scientists with a technical background. 

• Data scientist

• Average salary: $124,000 per year Typical education: Bachelor's degree in computer science, statistics, or a related field Typical years of experience: 1-3 The position of data scientist is a bit more specific than the discipline as a whole, but is still the most general mid- to senior-level position in the field. These employees oversee entire data projects, from determining the business' needs through collection, data cleaning, analysis, visualization, and presentation. Roles like Lead Data Scientist and Principal Data Scientist are often leaders within the data team since they're experts in everything data, and have a big-picture view ideal for the development of new data projects or algorithms.

• Data engineer

• Average salary: $127,000 per year Typical education: Bachelor's degree in computer science, software engineering, or a related field Typical years of experience: 3-5 Data engineers take charge of the design, creation, and maintenance of data pipelines. They start by testing businesses ecosystems and preparing the environment and information other data scientists will use to conduct data manipulation and analysis. This is one of the data disciplines that requires professionals with programming skills and knowledge of programming languages such as Python and R, as well as a deep understanding of databases, data cleaning, and other data fundamentals. 

• Data architect

• Average salary: $129,000 per year Typical education: Bachelor's degree in engineering, information technology, or a related field Typical years of experience: 5-7  Similar to data engineers, data architects create new database systems then maintain them, ensuring the data is accessible and formatted correctly for data analysts to use it. This person is also often responsible for administration of the database and controlling who can access and manipulate the data. 

• Machine learning engineer

• Average salary: $162,000 per year Typical education: Bachelor's degree in computer science, engineering, or a related field Typical years of experience: 2-4 One of the most in-demand roles within data science, machine learning engineers help businesses use artificial intelligence to make sense of and gain insights from data. Their key responsibility is designing, testing, and monitoring machine learning systems. Since they are often called on to write code, proficiency in at least one coding language, as well as understanding of AI concepts, is often a prerequisite for employment.

• Director of data science

• Average salary: $163,000 per year Typical education: Master's degree in computer science, engineering, or a related field Typical years of experience: 5+ The Director of Data Science is a leadership role that oversees all of the data activities within a business. While they need to have a deep understanding of data collection, manipulation, and analysis, as well as the common technologies used for these tasks, they often spend less time working with data hands-on. Instead, they take a top-level role, delegating work to others in data teams and serving as a liaison between individual data contributors and executives.

• Chief technology officer (CTO)

• Average salary: $188,000 per year Typical education: Master's degree in computer science, information technology, or a related field Typical years of experience: 13-15 The CTO is an executive-level leader who oversees the development and use of technology across an organization. They're responsible for determining the strategies and approach for the ways technology is used in the organization, as well as setting department goals, establishing policies and best practices, and ensuring the productivity of workers within the tech teams. 

• Business career path: Mid- to senior-level roles

• In addition to the technology-focused roles described above, backgrounds in data science can also open up opportunities in business intelligence. The roles below are more concerned with results than the data itself, and how the insights derived from data can make an impact on business growth.

• Business intelligence developer

• Average salary: $100,000 per year Typical education: Bachelor's degree in business, finance, or a related field Typical years of experience: 2-4 Also called BI Developers, these professionals develop the strategies that guide the way business leaders make decisions. While they often use and design tools to gain these insights, their primary focus is on the business and operations side, and a strong grasp of business strategy is required to excel in this role. 

• Database administrator

• Average salary: $99,000 per year Typical education: Bachelor's degree in computer science, information technology, or a related field Typical years of experience: 2-4 In many industries, particularly in larger companies, the people using a database aren't the same ones who build and manage it. That work is done by a Database Administrator, who monitors the performance of databases, tracks the data flow, and ensures the security of the data by managing employee access. 

• Statistician

• Average salary: $96,000 per year Typical education: Master's degree in statistics, mathematics, or a related field Typical years of experience: 2-4 This is the ideal data science job for people who love math. Statisticians identify data sources, then analyze that collected data to make predictions and deliver insights that organizations can use to make decisions. Superior problem solving and critical thinking skills are a must-have in this role. Statisticians are often also experts in data storytelling, applying theoretical concepts like probability and linear algebra to solve real-world problems.

• Analytics Manager

• Average salary: $119,000 per year Typical education: Bachelor's degree in computer science, information technology, or a related field Typical years of experience: 5+ Like other data science professionals, an Analytics Manager collects, assesses, and reports on data. Their specific role will vary between employers. In some companies, this is a leadership position that oversees individual contributors who work hands-on with data sets. In smaller companies, the Analytics Manager works directly with data, often serving as a subject matter expert for clients and other non-technical stakeholders.

• Chief operating officer (COO)

• Average salary: $146,000 per year Typical education: Master's degree in business administration (MBA) Typical years of experience: 13-15 As part of the executive board, the COO is a high-level leadership role. They oversee day-to-day operations, including everything from human resources and production to sales, marketing, and customer service. Because the position has such a broad scope, there are a number of career paths that can prepare an individual for a COO role. However, professionals with a background in business intelligence and strategic analysis are often ideally positioned to serve as operations leaders, making this a potential career track for data professionals.

• Choosing your ideal data science career track

• The practice of data science is already thriving, and the need for it is only likely to expand further with time. Hopefully this article has given you insights into the many career options available in the realm of data analysis and which one is the best fit for your skills and aspirations.