How to Build a Career in Data Science

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career in data science

Considering a career in Data Science? Good news: The U.S. Bureau of Labor Statistics estimates that the employment rate for data scientists will grow by 36% between 2021 and 2031, with 40,500 more jobs expected to be created in that time. As the amount of data generated globally increases at a rapid pace, so too does the demand for Data Science professionals who can help organizations analyze all this data, improve internal operations, and increase revenue.

To build a successful career as a data scientist, there are a few things to consider. From understanding the different roles available within the industry and realizing what technical skills you’ll need, there’s a lot to unpack as an early-stage data practitioner.

What Are the Roles and Responsibilities of Data Scientists?

Data scientists are critical in helping organizations turn data into insights that drive business decisions. In 2021, we collectively created 2.5 quintillion bytes of data daily. Business leaders are turning to Data Science experts to help them make sense of the massive datasets they are generating. Data scientists’ responsibilities include the following:

  • Identifying data sources
  • Cleaning and preparing data for analysis
  • Building data models
  • Communicating insights to stakeholders 

core skill required for data scientists is the ability to analyze and interpret large datasets. They must identify data trends, patterns, and anomalies and turn those insights into actionable recommendations. 

In addition, data scientists must be knowledgeable about statistical methods and have experience building predictive models. The goal is to be adept at spotting trends, creating a model based on that, and translating that information into a digestible format for decision-makers.

However, this is not easy. Data Science professionals face many challenges, and one such challenge is dealing with messy or incomplete data. It is essential to learn how to identify or leverage analytics tools to find missing values, outliers, and other issues and develop effective strategies to address these problems. 

Another challenge is communicating complex data and analysis to stakeholders. Most decision-makers are not from a technical background, which is why it’s often difficult for them to understand what the data is saying. The technical team’s responsibility must be to communicate the business impact of data. Ultimately, these aspects improve with more experience with data-related projects, so Data Science professionals should consider applying for additional certification programs to strengthen skillsets.

What Are the Different Career Paths in Data Science?

There are several different career paths in the Data Science field. Here are five of the most common roles that aspiring Data Science professionals can explore:

Data Scientist

Data Scientists possess the ability to combine technical skills such as coding and problem-solving with a more creative side, which includes data visualization and storytelling. They build models using this data, and in turn, they provide strategy recommendations or projects by converting that model.

Education: Bachelor’s degree in a Data Science-related field (computer science, Data Science, engineering)

Skills required: Machine and deep learning, programming, mathematics, data analysis, and tools like SQL and Hadoop

Average salary in the U.S. (2023): $126,350 per year (Glassdoor)

Business Intelligence Analyst

Business intelligence (BI) analysts are data professionals charged with the task of helping organizations make sense of their large data sets. This is done by designing and creating dashboards, reports, and analytics that help identify business performance trends. 

To become an effective BI analyst, you’ll need a strong foundation in mathematics and statistics and the ability to communicate complex insights through sophisticated visualizations.

Education: Bachelor’s degree in computer science, statistics, mathematics, or finance

Skills required: Data warehousing, ETL (Extract, Transform, Load), SQL, NoSQL, programming (Python, R), statistics, and data visualization

Average salary in the U.S. (2023): $87,386 per year (Glassdoor)

Machine Learning Engineer

Machine learning engineers are responsible for building algorithms and systems that allow computers to discover patterns in data sets. Their skillset must be well-rounded and include math and computer science fundamentals, expertise in coding languages like Python or R, library frameworks such as Pandas and NumPy, and an understanding of the business problem or product being addressed. 

By deeply engaging with the intricate details that come with this profession, those looking for a challenge may find themselves suited for the role of a machine learning engineer.

Education: Bachelor’s degree in computer science or engineering

Skills required: Data modeling, programming, statistics, probability, software design, machine learning algorithms, natural language processing

Average salary in the U.S. (2023): $132,450 per year (Glassdoor)

Data Architect

Data architects are responsible for designing and overseeing the development of databases and database management systems, which involves analyzing user requirements and developing a logical design structure. 

This role also consists of implementing and managing security controls and troubleshooting any technical issues. All the systems they create should consolidate available data sources and allow stakeholders to access information when needed.

Education: Bachelor’s degree in computer science or software engineering (database design)

Skills required: Programming (SQL, NoSQL, Python, Java), ETL, data mining and management, machine learning, and data modeling

Average salary in the U.S. (2023): $164,663 per year (Glassdoor)

Data Mining Engineer

Data mining engineers use their advanced programming skills to create algorithms or automated processes that can sift through large data sets and uncover trends and correlations. They set up and operationalize the infrastructure for storing, analyzing, and reporting the data. 

This can include aspects like creating data warehouses and a system to organize data in these warehouses, enabling easy access and flow of data.

Education: Bachelor’s degree in computer science or data analytics

Skills required: Data software systems, programming (Python, Java, R, MapReduce), experience with cloud computing platforms (Google Cloud, Azure), and analytics tools (Pandas, PySpark)

Average salary in the U.S. (2023): $116,821 per year (Glassdoor)

How to Get Started as a Data Scientist

Before jumping into the technical side of things, focus on understanding the field and identifying what skills you need to succeed. Here’s a five-step process you can use to build a career in Data Science:

  • Graduate with an advanced degree: A degree in areas such as computer science, mathematics, statistics, or Data Science can give you a solid foundation in the field. As it’s an advanced field, it’s best to get a master’s degree too, as an undergraduate degree would be the bare minimum in this technical area.
  • Focus on developing your programming skills: You must also have fluency in programming languages such as R, Python, or Java. You can enroll in online courses, watch video tutorials, or join coding communities to improve your programming skills. Additionally, you can sign up for certification programs.
  • Gain practical exposure in the field: Practical experience is crucial to your career as a data scientist. Find data-oriented internships or contribute to open-source projects that use Data Science to solve real-world problems. You can add these projects to your portfolio, which can also help you get an entry-level job.
  • Build a Data Science portfolio: A strong portfolio of Data Science projects can help you stand out to potential employers. Work on personal or professional projects and showcase your skills through presentations or upload them to websites like GitHub or Kaggle.
  • Stay current with industry trends: The Data Science industry is constantly evolving, and staying current is essential to remain competitive. Attend conferences, read industry publications, and participate in online communities to stay informed about the latest developments. 


Data Science is a growing field with a range of roles and responsibilities. There are several career paths available within the field, making it an attractive option for individuals who prefer working with large data sets but have specific preferences for what they want to do with them. 

Additionally, there are plenty of resources available to get started. From online courses and certification programs to university programs, aspiring data scientists have tools within their reach to become successful practitioners in this vital field.

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