IN THIS ARTICLE
- What Does a Data Scientist Do?
- 12 Data Science Roles & What They Mean
- Data Science Roles & Responsibilities FAQ
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Data science is a field that encompasses several different disciplines and if you’re interested in pursuing a career in data science, you should consider which roles best match your interests and strengths. For example, someone with a strong mathematical base might be best suited to work as a statistician, while a business-focused data science professional would be more fit for a business analyst role.
We’re going to take a look at the various roles within the larger data science industry, and give you the information you’ll need to help make your own path.
What Does a Data Scientist Do?
Before we learn about the particulars of different roles within the data science industry, let’s examine their commonalities. Broadly speaking, data science is the process of solving problems or answering questions by gathering and processing datasets. The goal is to take unstructured data and extract meaning from it.
Businesses hire data scientists to identify the problems and solve them by sourcing and analyzing large volumes of data. Professionals in this space have strong analytical skills in the technical knowledge required to mine, clean, process, and present data.
12 Data Science Roles & What They Mean
1. Data Engineer
Data engineers format raw data so that it can be analyzed. They collect data that will be used downstream, manage it, and convert the data so that it can be used by business analysts and others on the team. Data engineers build systems that make huge volumes of data more available to an organization.
- Source data and create datasets based on organization goals
- Design algorithms to convert raw data into usable information
- Create the architecture for data pipelines to data warehouses and databases
- Ensure adherence to data governance policies
The average salary of a data engineer is $112,202 per year.
Data engineers usually have at least an undergraduate degree in a math or computing field. They need to be familiar with programming languages like Python and Scala as well as database technologies like SQL. Apache Spark and Hadoop are commonly used tools in this role too.
Related Read: How Do You Become a Data Engineer?
2. Data Scientist
Data scientists employ statistical and analytical skills to process and derive insight from large datasets. They usually use various programming languages to achieve that goal. These insights unearthed by data scientists help solve key business challenges.
- Frame questions based on the goals of the business
- Conduct data investigations and exploratory analyses to answer those questions
- Integrate and process data from various sources
- Choose models and algorithms to guide the data analysis process
The average salary of a data scientist is $74,700 per year.
Most data scientists have at least a bachelor’s degree, usually in computer sciences, engineering, or a mathematical field like statistics. Languages like Python and R are commonly used in the field. Data scientists are sometimes required to present data, for which a data visualization tool like Tableau is used.
3. Data Analyst
A data analyst examines the available data and uses statistical methods to solve specific business problems. Professionals in this field usually work in an interdisciplinary environment and collaborate with both business and data teams. Data analysts are different from data scientists, who focus on creating tools and frameworks to gather data, while data analysts unearth data-based insights.
- Analyze data to unearth patterns and derive meaning
- Develop and maintain databases and data warehouses
- Prepare reports presenting insights obtained from the data analysis process
- Work with management, engineers, and other team members to identify opportunities to improve data processes
The average salary of a data analyst is $62,610.
Data analysts need to assess which insights can be obtained from a given dataset. They use programming languages like Python and R to design data analysis algorithms. Data analysts also need to present the results of their work to various stakeholders in the company.
4. Data Administrator
Data administrators build processes to store, retrieve, and maintain the available data. They ensure that the data coming from a given source is current and stored in a secure manner. They also define policies concerning database environments.
- Monitor and maintain an organization’s data pipeline
- Filter out data that is corrupted or irrelevant
- Write and update data governance policies
- Collaborate with various stakeholders to improve data storage and retrieval efficiency
The average salary for data administrator roles is $50,634.
Data administrators need to be familiar with an organization’s data lifecycle. They use database tools like SQL and Oracle. Hadoop is a commonly used tool for data management among administrators.
Get To Know Other Data Science Students
5. Data Architect
Data architects build and maintain an organizations’ databases. They conceptualize database architectures based on a company’s requirements and build it end to end. Data architects monitor their databases and execute system migrations whenever needed.
- Ideate and build database solutions for an organization
- Study database implementation procedures to meet internal and external regulations
- Prepare database architecture reports for executive team members
- Oversee data migration from legacy systems to new database technologies
The average salary of a data architect is $123,000 annually.
Data architects need to have a strong understanding of database systems and data mining procedures. Companies often require data architects to have at least a bachelor’s degree in computer science or engineering. Good communication skills are also essential to update executive teams on an organization’s evolving approach to data storage.
Related Read: 8 Best Data Architecture Courses To Boost Your Career
6. Machine Learning Engineer
Machine learning engineers use artificial intelligence to automate data analysis processes. This includes processes such as predictive modeling, data mining and pattern recognition. It is a role that combines approaches from engineering, math and artificial intelligence.
- Design machine learning systems to automate data-related processes
- Analyze statistical data and optimize machine learning algorithms
- Choose machine learning libraries and tools to simplify workflow
- Identify datasets to train new machine learning models
The average salary of a machine learning engineer is $132,900 per year.
A bachelor’s degree in computer sciences or engineering is required for machine learning engineer jobs. Professionals in this field need to be well-versed in statistics and machine learning algorithms. Machine learning engineers are also required to have an understanding of database architecture and database systems.
7. Machine Learning Scientist
Machine learning scientists have research-focused roles. They research the algorithms and models that a company plans to implement as part of its data analysis process. While machine learning engineers primarily implement algorithms, machine learning scientists gauge their efficiency, applicability and security.
- Identify candidate algorithms to solve various data-related business problems
- Study different algorithms and identify key characteristics
- Test and implement algorithms for data analysis
- Present their findings to various stakeholders
The average salary of a machine learning scientist is $137,053.
Machine learning scientists are often PhDs with a focus on artificial intelligence and neural networks. They use tools like OpenCV to model machine learning algorithms. The role requires the ability to work on distributed systems and model deployment.
8. Business Intelligence Developer
A business intelligence developer analyzes data to produce insights for their organization. Business intelligence developers also generate reports with accessible insights to help make business decisions.
- Translate organizational needs into technical specifications for data teams
- Analyze markets, products, and product-market interactions to source data points for datasets
- Build systems for business performance monitoring and generate reports for executive teams
- Perform quality assurance checks on business intelligence systems
The average salary for business intelligence developer roles is $94,800.
9. Business Analyst
Business analysts use data to interpret changing business needs, andmeasure how changing processes affect a business.They also communicate between different teams, acting as intermediaries to translate business goals into concrete objectives.
- Model business processes and measure the impact of various changes using data
- Communicate changes and translate requirements for various stakeholders
- Assess data analysis proposals and suggest modifications
The average business analyst salary is $79,000.
Business analysts need strong analytical skills. They use Python and R to perform analyses that require data wrangling and data manipulation. Tools like Power BI and Tableau are commonly used by business analysts to generate reports.
10. Database administrator
A database administrator (DBA) oversees a company’s database, which is important because a company needs to have reliable access to accurate data at all times. DBAs ensure that databases are functioning correctly and create processes for data backups.
- Monitor database systems and identify any performance or security issues
- Establish authorizations for different stakeholders and guard against unauthorized access
- Design database architecture and front-end to simplify access for other team members
- Ensure that database functionality is in agreements with company’s data governance policies
The average salary of a database administrator is $73,800.
A strong understanding of database technologies such as SQL, PostgreSQL, and Oracle is key for database administrators. Completing a certification like a Microsoft Certified Database Administrator (MCDBA) can be beneficial for a career in the field. DBAs need to stay abreast of developments in their field and recommend new tools or processes.
Statisticians use analytical techniques to interpret numerical data. They help other data professionals produce scaling models and algorithms that perform calculations and produce projections.
- Liaise with various departments to obtain numerical data
- Perform calculations and generate forecasts using statistical techniques
- Present their statistical research to management
- Assist data scientists and other professionals in creating models to derive insights from numerical data
The average salary of a statistician in the US is $92,270.
Professional statisticians working in data science usually have at least a bachelor’s degree in statistics. You need to have strong quantitative and analytical skills to work in the field. SPSS is a commonly used language for statistical analysis. Statisticians also use R and Python in their work.
12. Applications Architect
Application architects build software applications that help interpret data. Application architects focus on the applications themselves, rather than data. Professionals in this field assess business needs and receive input from various stakeholders during the development process.
- Work with managers to determine business needs and identify problems that can be solved using data
- Develop prototypes using feedback obtained from various stakeholders
- Build applications and run diagnostics
- Integrate the application into existing systems and perform migrations, updates, and maintenance
The average salary of an applications architect is $129,000.
Application architects have a high degree of proficiency in programming languages like C, .NET, and Java. They are also well-versed with SQL and other database technologies. Extensive experience as part of a development team is required to work as an applications architect.
Data Science Roles & Responsibilities FAQ
How Hard Is It To Get a Data Science Job?
If you have the right skills and experience for the role, it is not hard to land a data science job. Competency in statistics, probability, algorithms, and programming languages like Python is essential. Make sure that you customize your resume to reflect your strengths in data science when you apply for a job.
Can I Get a Data Science Job Without a Degree?
Some companies hire data scientists without a degree. If you don’t have a relevant degree, you need to demonstrate that you have the mathematical and computation skills required for the job. Completing bootcamps or courses can help show this. Recruiters are also impressed when candidates have worked on personal projects.
Can You Get a Data Science Job Without Any Experience?
It is possible to get a data science job without experience. Several entry-level roles hire candidates right out of college or people who have completed certain certifications or bootcamps.
If you are curious to know further how your learning journey would pan out, read on about different data science career paths to understand which one matches your goals and aspiration.
Since you’re here…
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