We live in the technological world, and data scientist is the backbone of the digital world. Consumer behavior, customer actions, business operations, and decision-making are at their core and governed by data points for the enterprise. By seeing this growth, data science continues to be a promising career for skilled professionals.
Undoubtedly, a data science career is lucrative and rewarding, but starting a career is more complex than it seems. A bachelor’s or master’s degree is not required to become a data science professional. With the proper skill set and experience, you can become an expert in this field. So, if you want to leverage your data boot camp knowledge or jumpstart a career in data science, the 10 best jobs are compiled below. It enables you to determine which one is suitable for you.
Top 10 Job Titles for Data Scientists
Take a look at some of the in–demand jobs in this data science career guide and better understand how they fit into your criteria.
1. Data Engineer
The data engineers are responsible for building, designing, and maintaining data pipelines. They have the responsibility to test the ecosystem for the business and prepare them for a data scientist to rum algorithms. Also, the data engineers work on the batch processing of gathered data and match its format to stored data. In a nutshell, they need to ensure data is ready for further analysis and processing.
2. Business Intelligent Analyst
Business Intelligent Analyst discovers patterns and value in their companies’ data, similar to the data analyst role. BI works with SQL and is comfortable with data visualization and modeling. They extract data from the warehouse using querying techniques, evaluate the data and create summary reports for companies’ current situations.
The BI will provide suggestions to management to enhance the efficiency of the business and prepare new analytics models to standardize data collection.
3. Machine Learning Engineer
Machine learning is relatively high in demand. Thus, it is mandatory to be familiar with various machine learning algorithms like categorization, clustering, and classification and stay updated with the latest innovations. To excel in their career, machine learning engineers need to have a strong command of statistics and programming and fundamental knowledge of software engineering.
The machine learning engineer needs to run tests (like A/B) while monitoring different system performances and functionality.
4. Business Analyst
The role of a Business Analyst is different from other data science jobs. They have a solid understanding of data-oriented technologies and how they handle large amounts of data. However, they can separate high-value data from low-value data. They identify the Big Data that can be used to provide business insight for growth.
A few critical roles and responsibilities of a business analyst include the following:
· Understanding the business side of an organization
· Improvement of existing business processes
· Forecasting and budgeting
· Pricing analysis
5. Data Analyst
Data Analysts are typically an entry-level job. Their primary task is to create systems that gather and analyze company data. Then, they use that data to find insights to answer business questions. This role requires a keen eye for detail and the ability to think up new ways to analyze data. Data analysts are often required to collaborate with many departments and individuals. This is especially true when explaining technical ideas to non-technical people.
6. Data Storyteller
Data storyteller means sharing information in story format. They visualize data, prepare reports and find the descriptions that describe the data in the best way, and develop creative ways to express that narrative. It is a creative role that comes between data analysis and effective human communication. The data storyteller will simplify the data that helps businesses analyze and create a story that enables others to understand. They possess various skills like design thinking, design tools, data visualization, and design tools, along with that soft skill like communication, creativity, and the ability to draw meaningful narratives.
7. Data Architect
Data architects and data engineers share similar responsibilities. They both need to ensure that data is well–formed and accessible for both and enhance data pipeline performance. Furthermore, data architects must design and create new datasets that match the needs of particular business models and job requirements. Data architecture
requires streamlining database systems both in the form of functionality and administration. Thus, they must record the data and decide who can view, use, and manipulate various data sections.
8. Database Administrator
It manages datasets in organizations. Also, they are responsible for constantly monitoring the database to ensure correct functioning, security of data, and user access and ensure data availability for backups and recovery. The administrator works on the database to save and streamline the data and enables them to design and develop databases.
However, they are responsible for a testing database that ensures trustworthy options. Also, they know SQL and Scripting.
9. Technology Specialized Roles
Data science is still a new field. More specific technologies like AI and ML algorithms will become available as it develops. As the area grows, there will be unique specialties, such as Deep Learning specialists, NLP experts, and AI specialists. These roles also apply to data scientists, analysts, and other professionals.
10. Statistician
The responsibility of gaining valuable insights from data is the responsibility of a statistician. They are well-versed in statistical theory, data organization, and methodologies. They work with all kinds of data to turn it into knowledge. They are skilled in acquiring, organizing, presenting, analyzing, interpreting, and evaluating data to make good decisions and reach valid conclusions. You must have both domain and statistics knowledge to become a statistician. They use statistical analysis tools for data analysis to identify trends and patterns and to interpret those using data visualization tools, reports, or other data visualization tools.
Conclusion
Data science is in high demand. Employers are spending significant time and money to support this profession. You can make a successful career in data science by taking the proper steps and obtaining the best data science certifications. We hope you found this blog helpful and helped you understand how to get started in data science.
