Data Scientist Job Titles

On the surface, many job titles appear to refer to the same position. While some organizations will use different titles from others to refer to similar positions, most job titles help to define the areas of expertise or specialization. Many job listings for Data Scientists will include “Junior” or “Senior” in the job title to indicate the level of experience required for the position. A Machine Learning Engineer is a type of Data Scientist, but this job title makes clear that the main duties of the position revolve around machine learning. A Data Engineer designs and builds systems that collect, store, and analyze data at scale. A Senior Data Modeler manages how information flows between departments by using relational databases. A Data Science Instructor is an experienced Data Scientist that works in an academic or educational setting preparing students for careers in data science. The following sections offer additional information regarding how to become a Data Scientist and the types of job titles that Data Scientists hold.

What is a Data Scientist?

Data Scientists extract meaning from raw data to detect patterns and propose solutions that meet an organization’s needs, especially the needs to compete and grow. A Data Scientist’s responsibilities include finding valuable data from data sources, developing machine learning algorithms, improving data collection procedures, cleansing and validating data integrity to ensure accuracy, and detecting patterns and solutions based on data. Data Scientists build models based on data, create data visualizations that communicate patterns and findings to stakeholders, and automate collection processes. Because data plays a critical role in the success of any organization, Data Scientists can build careers in business, technology, finance, nonprofits, and many other industries. 

Those who wish to become a Data Scientist should develop the analytical, statistical, and programming skills needed to manage and interpret raw data. These skills include understanding statistics, machine learning, and reporting tools. Aspiring Data Scientists also benefit from understanding the programming languages R, SQL, Python, Java, and C++. 

Read more about what a Data Scientist does.

Data Scientist Skills

The first step to becoming a Data Scientist is to learn the necessary skills for gathering, managing, and interpreting raw data used by organizations to achieve their goals. Data Scientists must develop several mathematical and scientific skills, including a thorough understanding of machine learning and programming languages used in data collection, management, and analysis. Such skills include understanding: 

• SQL
• R programming
• Python
• File management
• Machine learning and algorithms
• Natural language processing
• Linear algebra
• Calculus
• Statistics
• Probability

Data Scientists often create data visualizations to communicate findings to stakeholders, so they must also understand how to use data visualization tools such as Tableau. A successful Data Scientist knows how to work closely with others, focus on details, recognize patterns, and develop solutions to problems. A Data Scientist must also develop the communications skills necessary to relay recommendations and findings to stakeholders.

Read more about what skills you need to become a Data Scientist.

Data Scientist Career Path

Data Scientists begin their careers with entry-level positions. You can find an entry-level Data Scientist position at many different types of organizations including social media companies, multinational corporations, technology companies, government agencies, and nonprofits. Many entry-level positions require no previous experience, however, if you are targeting positions that seek previous experience, or wish to make your resume more competitive, you can explore Data Scientist internships. Internships provide hands-on experience, allow you to build your professional portfolio, add experience to your resume, give you industry connections, and provide you with references. 

As Data Scientists gain more experience, typically over several years, and build their skills, they can advance to mid-level and senior positions. Such positions may include management responsibilities and overseeing more complex tasks. Promotions to mid and senior-level positions also come with a salary increase.

Some Data Scientists take an alternative career path by working as a freelancer. You can learn more about freelancing as a Data Scientist and how to become a Data Scientist without a degree. 

Read more about the typical Data Scientist career path.

Junior Data Scientist

The job title of Junior Data Scientist applies to entry-level Data Scientists, typically with zero to two years of professional experience. Junior Data Scientists typically carry out routine tasks such as cleaning and preparing data. They may also build models or prototypes. Data Scientists at this level learn new skills and gain experience as they go, especially if working with a data science team or alongside more experienced Data Scientists. In large organizations, Junior Data Scientists focus on learning and performing routine tasks. Because they are entry-level, they are unlikely to take on a mentorship role in larger organizations at this stage. However, smaller organizations may have a Junior Data Scientists instruct and manage interns. As with many jobs in small organizations, Junior Data Scientists may have more responsibilities and tasks to juggle compared to someone in the same position at a larger organization. As with any Data Scientist role, a Junior Data Scientist depends on mathematical and scientific skills such as understanding statistics, identifying trends and patterns, using Python programming, thinking critically, and problem-solving.

Senior Data Scientist

Senior Data Scientists are those who have advanced beyond the entry-level title. They have more experience than Junior Data Scientists and generally make a higher salary. Because they typically have more than two years of professional experience with data science, a Senior Data Scientist may be trusted with more complex tasks than a Junior Data Scientist. They may also act as a mentor or manager to interns and entry-level Data Scientists. Senior Data Scientists present findings and suggestions to internal stakeholders to help the organization plan for the future and meet its goals. The presentations and reports a Senior Data Scientist compiles tend to include data visualizations such as charts, graphs, or interactive dashboards. 

Machine Learning Engineer

Machine Learning Engineers research, build, and design machine learning systems that make predictions based on large data sets. As machine learning is a branch of artificial intelligence, Machine Learning Engineers keep up to date with best practices and new developments in AI. Other skills they use include knowledge of programming languages such as Python, C++, and C. They also study, transform, and convert data science prototypes, select appropriate data sets for data collection and modeling, use statistical analysis to improve models, train and retrain machine learning models, create data visualizations, and verify data quality.

Data Engineer

A Data Engineer collects, manages, and converts raw data into information from which Data Scientists and Business Analysts can gain valuable insights. They build systems that collect, store, and analyze data at scale. This data provides crucial insights as to how an organization is performing, where it can improve, and how it can reach its goals. A Data Engineer's daily tasks may include acquiring data sets, developing algorithms for transforming data, collaborating with management to understand organization objectives, and creating new data analysis tools and validation methods. This is often a more senior position rather than an entry-level one. 

Senior Data Modeler

A Data Modeler works alongside Database Administrators and Data Architects to build computer databases that organize data correctly and in ways that help organizations achieve their objectives. A Senior Data Modeler is one that has a few years of experience and has reason to the highest level a Data Modeler can reach within the organization. To become a Data Modeler, you will need experience with data modeling, database administration, software development, SQL, and Microsoft Office. You will also need to learn and adapt quickly, have strong analytical skills, think abstractly, and know how to work independently and as part of a team.

Data Science Instructor

A Data Science Instructor is an experienced data science professional that teaches aspiring Data Scientists the skills needed to start their careers. A Data Science Instructor may develop class curriculum, lead hands-on activities in a classroom setting, mentor students, assign projects, review professional portfolios, and teach best practices. Data Science Instructors typically work through schools and established data science training programs, such as those that offer data science classes and certificate programs. Data Science Instructors might teach in-person classes or teach remotely. To become a Data Science Instructor, you must have real-world experience in a data science career. This job works well for those who enjoy teaching and mentoring others.

Why Become a Data Scientist?

Data science can provide a challenging, in-demand, and rewarding career that is relevant now and will remain so well into the future. Data Scientists: 

• Earn high salaries
• Work with the latest technology
• Use problem-solving skills and innovate solutions
• Work independently and collaboratively
• Long-term stability

The U.S. Bureau of Labor Statistics found that the median annual salary for Data Scientists is around $100,000. Entry-level Data Scientists make between $75,000 and $95,000 on average with more experienced Data Scientists earning well over $100,000. 

Data Scientists work with some of the latest technology, including artificial intelligence and machine learning. They also use Python programming and other programming languages. Because data science tools and programming languages constantly evolve, Data Scientists are constantly learning and sharpening their skills, which keeps the profession interesting and relevant. Demand for Data Scientists is expected to increase by 36% between 2021 and 2031, showing that this career makes for a rewarding option now and in the future. 

Data Scientists work both independently and collaboratively, so the job is neither too isolating nor overly draining due to constant meetings or team activities. They solve problems, think of ways to improve processes based on patterns in data, and make recommendations to stakeholders, all of which makes this a mentally stimulating career to pursue.

Read more about whether Data Scientist is a good career.

Learn the Skills to Become a Data Scientist at Noble Desktop

If you are looking to start a new career in data science, you might think the only way for you to become a Data Scientist is by enrolling in a four-year university or pursuing other costly and lengthy educational options. However, there are many alternative methods available to help you transition into a data science career, including data science bootcamps and certificate courses designed to help working professionals gain the skills needed to obtain an entry-level job as a Data Scientist. Exploring in-person and live online data science bootcamps and certificate programs can help you find the class that meets your career goals, budget, and schedule. The first step to finding the class that fits your needs is to understand the differences between in-person classes and live online classes. 

In-person data science classes meet in a traditional classroom setting at a physical location. In-person classes have the advantage of providing all necessary equipment, such as computers and software, and allowing students to network with local professionals such as your classmates and instructor. You also have the advantage of learning from an expert instructor face-to-face. The primary drawback to in-person courses is the extra time and money required to commute to the physical learning location. Live online data science classes offer many of the same benefits as in-person classes, including the ability to learn in real-time from an expert instructor. You can also collaborate with classmates, and you have the advantage of learning remotely. 

Noble Desktop offers several different in-person and live online data science classes that can help you start a career as a Data Scientist. The Python for Data Science Bootcamp teaches students foundational programming concepts and how to handle different data types, use conditional statements to control the flow of a program, use Scikit-Learn, Matplotlib, Numpy, Pandas, and other Python libraries and tools. Noble’s Data Science Certificate program and Data Analytics Certificate program provide a deep dive into the topics and skills essential to launching a career in data science or data analytics and offer one-on-one mentorship and job search assistance. All Noble Desktop classes provide students with hands-on experience, flexible financing options, setup assistance, a free retake, small class sizes, and real-time guidance from an expert instructor.

Learn more about Noble Desktop’s in-person and live online data science classes.

You can also learn more about data science careers and data science learning options with Noble’s free Data Science Learning Hub.