Data science has transformed business operations by making task completion easier and more efficient. It is found to be highly valuable for market research, decision-making, product novelty, and innovative marketing strategies. The US Bureau of Labor Statistics predicts data professionals demand to increase by 27.9% by 2026. Anyone with the best certifications and hands-on experience can make a career in data science by getting top jobs in terms of growth, demand, and pay.
- Data Privacy Experts
These professionals advise companies on data privacy and security to protect their sensitive data. They ensure enterprise-wide regulatory compliance and maintain data privacy standards.
What do data privacy experts do?
Inform an organization and its staff about data processing and storage and essential compliance requirements; perform audits to identify any possible issues; and confirm the safety of crucial information.
Average salary yearly: $72,947 (ZipRecruiter)
Skills required: Data protection expertise, analytical skills, knowledge of international privacy laws, communication, risk assessment, and legal knowledge.
- Cybersecurity Analysts
This is among the entry-level data science jobs that protect computer networks and systems of a company from unauthorized access and cyberattacks.
What do cybersecurity analysts do?
Analyze security data, monitor network traffic for security breaches, investigate breaches, implement security measures, fix vulnerabilities, and write complete incident response reports.
Average salary yearly: $120,360 (BLS, Bureau of Labor Statistics)
Skills required: Basics of cybersecurity, knowledge of the data science foundation, critical thinking, communication, intrusion detection, network security, programming languages, and endpoint management.
- Database Administrators (DBAs)
Database administrators monitor and manage databases to ensure the smooth and efficient operation of organizational databases. This helps organizations with easier access to information all the time.
What do database administrators do?
Maintain, secure, and operate databases; administer database objects; perform database housekeeping; implement security measures; and find, report, and manage issues.
Average salary yearly: $1,17,042 (Glassdoor)
Skills required: Adaptability, communication, problem-solving, negotiation, familiarity with SQL and popular database technologies, and analytical skills.
- Business Intelligence (BI) Analysts
When it comes to extracting actionable insights from raw data, companies often need business intelligence analysts. This is one of the top data science jobs that will be in high demand in the future to help organizations with data-driven decision-making.
What do business intelligence analysts do?
Writing process for data collection and processing, data analysis, monitoring data collection, developing BI tools or systems, managing BI solutions, and reporting business data findings to management.
Average salary yearly: $1,34,789 (Glassdoor)
Skills required: Analytical, data visualization, knowledge of BI tools, statistics, data analysis, critical thinking, communication, and problem-solving.
- Data Translators
They are channels between data scientists and executive decision-makers, translating data science concepts to achieve desired data science outcomes. Since they have detailed knowledge of the technical aspects of data science, they facilitate efficient decision-making and business functions.
What do data translators do?
Allow precise communication between technical and non-technical people; translate challenging data insights into useful strategies.
Average salary yearly: $57200 (ZipRecruiter)
Skills required: Knowledge of data visualization tools, data analysis skills, stakeholder management ability, good communication, and interpretation abilities.
- Machine Learning Engineer
This is a more practical job that involves creating algorithms and data sets, integrating external datasets, and transforming theoretical data science models into production-ready applications.
What do machine learning engineers do?
Create data funnels, develop ML systems, create and implement ML algorithms, and perform tests and experiments to monitor and improve the performance of ML systems.
Average salary yearly: US$165,270 (Glassdoor)
Skills required: Strong programming and statistics skills, software engineering and cloud platform knowledge, and proficiency in popular ML frameworks.
- Data Architects
Data architects, or big data architects, are among the most relevant roles in the data-driven world. They design, create, deploy, monitor, and manage organizational data architecture for improved strategic planning and precise decision-making.
What do data architects do?
Develop and implement data strategy, coordinate and collaborate with others, build and maintain data architecture, define and manage data flow, and visualize and design the enterprise data management framework of an organization.
Average salary yearly: $132,548 (PayScale)
Skills required: Good knowledge of databases and cloud, expertise in data modeling and design, NLP, ML, proficiency in system development, data mining, programming languages, data visualization, and stakeholder and team collaboration.
- Machine Learning Scientist
This role is similar to machine learning engineering, except that it is highly research-based and focuses more on algorithms than data. They develop robust training models to be used for various purposes.
What does a machine learning scientist do?
Create AI and ML systems, perform ML tests and experiments, design and implement adaptive algorithms, develop ML applications, and troubleshoot issues.
Average salary yearly: $1,88,954 (Glassdoor)
Skills required: ML algorithm, programming, data modeling and evaluation, software engineering, AI knowledge, model deployment, NLP, communication, and problem-solving.
Conclusion
Since data science is an emerging and popular field, there is a need for data science professionals in almost every industry. The higher demand for top data science jobs is resulting in lots of competition. Certification in data science is highly recommended to develop essential skills, build a strong portfolio, and validate proficiency. Choose a desired career, leverage all new opportunities, and embrace constant learning to secure your place in the exciting world of data science.