Data Science Professionals: Types, Roles and Responsibilities, Explained

It is currently one of the most sought-after careers across the world with demand constantly overpowering supply. Data Science has also proved to be one of the most progressive and lucrative careers in any form. Becoming a Data Scientist is something similar to walking on a tightrope. No bachelor’s or master’s degree is required, but perseverance, skill set, experience, and determination to reach the other end.

To begin with, an aspiring Data Scientist must self-check certain prime qualities to essay a successful career in the field. Skills such as analysis, machine learning, statistics, critical thinking, patience, and spontaneity are a must. Above all, a strong penchant and knack for storytelling will exalt the role to new heights. However, the term “Data Scientist” is simply not enough to perfectly describe the role. Instead, it is an umbrella term that shelters various skillful roles to pursue. There is a bevy of programs that solely enhance the course you are interested in. Hence, it is essential to pick the right set of skills to embark on a successful journey in the realm of Data Science.

Here is a closer look at the 7 types of Data Science professionals and their roles:

1. Data Scientist:

Data analysis and data processing are the two important weapons a Data Scientist must have in their arsenal. An ideal Data Scientist is expected to forecast the challenges of the business and come up with premeditated solutions to counter them. Data Scientists must be candid in bringing out productive insights from disorganized data using trends and patterns.

The 4 most important responsibilities of a Data Scientist are as follows:

• Data collection and processing
• Finding data collection sources for business
• Collaborating with team, business, and engineers
• Constantly using/furnishing Data Science tools to ease up the process

Checklist: R, MatLab, SQL, Python,

2. Data Analyst:

A Data Analyst is required to create, develop, and modify algorithms that can be used to capture information from some of the biggest databases without corrupting the data. Optimization is the crux of the whole role, besides munging, visualization, and processing of data from large resources. An ideal Data Analyst is required to perform multiple tasks at the same time using data for business growth.

The 4 most important responsibilities of a Data Analyst are as follows:

• Preparing reports with recommendations and performing data analysis
• Tracking web analytics and A/B testing analysis
• Maintaining, developing, and tracking the database
• Extracting data from various sources mostly using automated tools

Checklist: Problem-solving, SAS, SQL, R, and Python

3. Data Engineer:

As the name suggests, Data Engineers are the prime builders of the ecosystem for business. A Data Analyst’s job begins when a Data Engineer fulfills the responsibilities of building scalable and optimized data systems.

The 4 most important responsibilities of a Data Engineer are as follows:

• Updating existing data systems to improve efficiency
• Acquisition, collection, and administration of data
• Keeping the track of analytics to update stakeholders
• Researching on primary and secondary databases.

Checklist: Previous experience, Hive, NoSQL, R, Ruby, Java, C++, and Matlab

4. Database Administrator:

Easy Functioning, Accessible Functioning, Effective Functioning: These are the roles of a Data Administrator. Besides taking deep care of the functioning of the database system, a data administrator is also responsible for recoveries and data backups.

The 4 most important responsibilities of a Data Administrator are as follows:

• Designing and developing database
• Performing tasks on database software to store and manage data
• Archiving data from the company
• Ensuring security measures for the database systems

Checklist: Data management, data backup, and recovery, design, data modeling, data security.

5. Machine Learning Scientist:

Every tech behemoth on the planet is looking for a skilled Machine Learning Engineer, making it the most sought-after position in the Data Science and Analytics field. A Machine Learning Engineer, also known as an ML Scientist, must be well-versed in cutting-edge technologies such as SQL and REST APIs. They must also have a strong appetite for building data pipelines, conducting A/B testing, and implementing algorithms like clustering, classifications, and more.

The 4 most important responsibilities of a Machine Learning Scientist are as follows:

• Constantly testing Machine Learning systems
• Researching Machine learning algorithms
• Developing applications.
• Focussing on frameworks of Machine Learning to develop the libraries

Checklist: Mathematics, Java, Python, JS, Statistics.

6. Data Architect:

Data Architecture is the most indulging, meticulous, and elevating vertical that requires creating blueprints for data management systems so as to protect, integrate, and centralize data in a desirable form. Data Architects function closely with Data Engineers to ensure they have the best tool to build the above-mentioned ecosystem.

The 4 most important responsibilities of a Data Architect are as follows:

• Administration, management, and planning of all end-to-end data architecture
• Developing data strategy for companies
• Collaborating with data-oriented teams like Engineers and stakeholders.
• Maintaining the efficiency and security of database systems/architecture

Checklist: Hive, extraction transformation, and loan (ETL), Pig, Spark, Data warehousing, Data modeling.

7. Business Analyst:

This particular role treads slightly away from the classic interpretation of Data Scientists. However, on a contributor scale, a Business Analyst is responsible for understanding the large amount of data that can be harnessed to empower businesses in all fields. They have a comprehensive knowledge of database systems and their functioning. A Business Analyst serves as a filtering system that separates high-volume data from low-volume data.

The 4 most important responsibilities of a Business Analyst are as follows:

• Acquiring an acute understanding of business processes
• Coming up with opportunities and imminent problem-solving techniques ensued from the detailed business analysis
• Predicting and budgeting the business requirements
• Improving business models in correspondence to the data

Checklist: In-depth knowledge of IT technologies, business processes, and analysis.

Explore the specifics of "Business Analyst Salary" in our latest blog!

Conclusion

Data is rampant, and the need for skillful Data Scientists is increasing by the day. The realms of Data Science deal with bountiful responsibilities like analysis, statistics, modeling engineering, and more. Digitalization has completely changed the way things are done. With that came a massive requirement for Data Scientists across the world of business. To partake in globalization and to serve as a channeler of data into successful production, if opportunities arise, this is the right time.

To learn more about the multi-faceted roles in Data Science and Analytics, enroll in AlmaBetter’s Full-stack Data Science course or Masters in Data Science program to make a mark of your own in the data-driven world.