The Future of Data Science: Top Careers and Predictions for the Next Decade

Table of Contents

• [toc headings="h2" title="Table of Contents"] Data science is older than many people realize. The concept dates back to the early 1960s, when John Tukey described the merging of statistics with computers using the term data analysis, and by the mid-1980s the name data science was first used.  That said, the field of data science as we know it today really got rolling in the 1990s, and has been in a state of constant evolution in the decades since. The same is likely to be true for the next decade, as well, with emerging technologies like AI poised to revolutionize the field once again. If you're curious what's coming down the pike for data science, and how those changes will impact data science careers, this article will highlight current trends and take a look at what's next for this dynamic industry.

• The current state of the data science industry

• Data scientists have been in high demand across industries for the past several years, and that is a trend that shows no signs of slowing in the next decade. According to the U.S. Bureau of Labor Statistics, the sector is projected to grow by 35% through 2032, a rate more than ten times higher than the average across professions. That equates to roughly 54,000 new data science roles being added every year.  Those data scientist jobs aren't just in technology companies, either. Research from 365 Data Science on the 2024 data science job market shows that, while roughly a quarter (28.2%) of those roles are in the technology sector, there are also a significant number of data analysis roles available in marketing, healthcare and life sciences, manufacturing, and financial and professional services. The truth is, any business that gathers data on their customers, market, or processes can benefit from adding data science skills to their team.  The most in-demand roles for data scientists in 2024 include: 

  • Data Engineer - These are the professionals who build and maintain the data infrastructure that drives analytics. This can include designing databases, writing scripts, and building scalable data pipelines.
  • Database Administrator - Once databases are constructed by data engineering professionals, Database Administrators take over. Common responsibilities of this role include managing access, monitoring database performance, and keeping companies' data secure. 
  • Data Architect - These professionals take a top-level view of data science. They're responsible for building frameworks to enable analysis, as well as creating systems and strategies to organize and use data. 
  • Machine Learning Engineer - This is a role on the practical side of data science. Machine Learning Engineers turn theoretical models into usable applications and systems for gaining data insights.
  • Business Intelligence (BI) Developer - Another practical role in the data science field, BI Developers transform raw data into insights that drive future decision-making.Typical tasks can include constructing data models and analyzing historical data from the business to create reports that will shape future strategy. 
  These are just some of the roles available in the data science field today that are likely to continue growing in the next ten years. 

• Emerging technologies that will impact data science careers

• The continued development of artificial intelligence and machine learning algorithms is already having an impact on the data science field, and that is likely to continue in the future. In the research from 365 Data Science referenced above, 69% of current data science job postings include machine learning. Demand for other AI skills has risen, as well. For example, only 5% of job posts mentioned natural language processing in 2023, but that rose to 19% by 2024. Based on these trends, it's safe to assume that skills in AI/ML technology will continue to be in demand for data science jobs in the next decade.  When AI tools like ChatGPT first emerged, there were concerns that they would replace data analysts and reduce the career opportunities in this sector. This has thus far proven not to be the case. In reality, the growth of AI only makes employees with experience in big data, data collection, and data visualization more necessary for organizations that want to fully harness the power of artificial intelligence and machine learning.  The rise of AI isn't the only technological advancement likely to impact the data science career path in the future. Quantum computing has the potential to prompt revolutionary advancements by dramatically speeding up calculations. Edge computing is another emerging technology of interest for the data science world. By processing data closer to the source, it enables better real-time analytics that can be particularly valuable in IoT applications. The expansion of blockchain technology also has implications for data science, providing a more secure and transparent method for data management. 

• Skills to learn today for data science careers of the future

• In addition to knowledge of the emerging technologies mentioned above, there are other skills that a data analyst can develop today in order to prepare themselves for the future of the field. People with expertise in the following areas are not only in demand today, but will likely remain so for the next ten years. 

• Data visualization

• In today's job market, many employers look for data scientists with expertise in data visualization libraries like Tableau and Power BI. As techniques continue to advance, visualizations will likely become more interactive, incorporating more options for real-time data integration and customized queries. Advancements in augmented reality and virtual reality technology could further expand the options for data visualization tools, adding the possibility of more immersive 3D models.

• Cloud computing

• Cloud platforms are a versatile tool in the toolbox of data scientists today. Scalable storage allows for the management of large datasets, while frameworks like Hadoop and Spark process them more efficiently. These platforms are increasingly integrating AI capabilities, and the coming years will likely see more automation and more sophisticated algorithms that simplify the deployment of machine learning models. 

• Programming languages

• The coding languages that are most in-demand in today's data science field are Python, R, and SQL. These are likely to remain the most useful languages for success in a data science position. Python is particularly valuable as the primary language used in AI/ML development, while R is more often utilized for statistical analysis and data visualization, and SQL is essential for database management. 

• Predictive modeling

• Predictive modeling is one of the key methods for forecasting future outcomes and making informed decisions based on past data. Today, it's often used in customer analytics, risk management, and healthcare analytics. Emerging technologies like deep learning, edge computing, and automated machine learning will likely have an impact on the predictive modeling of the next decade.

• Industries and sectors with growing opportunities

• We mentioned in the previous section that technology organizations are far from the only ones employing data scientists today. The need for data science professionals in non-technology sectors is only likely to increase in the coming decade as more businesses turn to data insights to guide their decision-making and find solutions to the challenges they're facing. Data science is likely to grow in importance in a wide range of fields in the coming decade, including: 

  • Healthcare - Data science could prove the advent of a new era of healthcare thanks to its growing use in personalized medicine, early detection, and chronic disease management. Positions like Health Data Scientist, Health Informatics Specialist, Bioinformatician, and Clinical Data Analyst will likely grow in the next 10 years.
  • Finance - Banking and finance companies increasingly use data science techniques in areas like risk management, fraud detection, and algorithmic trading. This will mean demand for roles like Risk Data Scientist and Quantitative Analyst.
  • Education - Data on student performance can be analyzed to identify at-risk students so intervention can happen earlier, while AI tutoring and personalized learning expand the ways students can learn. Job titles like EdTech Data Analyst, Student Success Analyst, and Education AI Specialist will likely grow in the coming years. 
  • Marketing - With data science methods, marketers can dive even deeper into customer behavior and demographics to optimize and personalize campaigns, and can do it in less time thanks to automation. Expect to see more openings for NLP Data Scientists, Customer Insights Managers, Marketing Automation Specialists, and similar roles.
  • Energy - Predictive analytics allows for better forecasting of energy demand, helping to refine its production and distribution. The next decade will likely see the growth of roles like Energy Systems Modeler, Smart Grid Data Analyst, and Environmental Data Scientist.
  • Manufacturing - From predictive maintenance of equipment to energy management and defect detection, data science can enhance manufacturing operations in an array of ways. This will generate demand for roles like Process Optimization Engineer, Robotic Process Automation Developer, Predictive Maintenance Analyst, and Industrial Data Engineer.
  • Logistics - The power of data science techniques to identify past trends and forecast future outcomes can be very valuable for supply chain optimization. Positions in logistics analytics, inventory optimization, and supply chain risk analysis are likely to increase. 

• Current trends in data science careers

• Demand for multi-faceted data science experts. 

• Many job postings today are looking for data engineers with a wide variety of skills. This is particularly true in smaller companies with correspondingly small teams, who often look to hire one person to see to all their data needs. Because of this, developing a diverse skill set could open up more job offers and opportunities than having a tight specialization. If you have expertise in a domain like data privacy, cybersecurity, cloud computing, data architecture, or big data, or a combination of the above, that boosts your adaptability and potential value for employers. In a similar vein, the growth of data science outside the tech sector means a higher demand for interdisciplinary skills, such as professionals who are experts in finance or healthcare in addition to data science.

• More remote and flexible opportunities.

• The majority of jobs in data science can be performed just as well remotely as they can in the office. Data science projects often have a global scope, too, requiring the collaboration of people from around the world, which has accelerated the rise of virtual teams. Given the high demand for this talent, this makes the sector a prime candidate for remote work, and a growing number of data science positions on the job market today are remote or hybrid, some of which are also location-independent. This trend can dramatically expand the opportunities available in the field since data scientists can take jobs from anywhere, not just in their immediate area. 

• Increased focus on data ethics.

• As the data science field has grown, it has come under more scrutiny from both regulatory agencies and individual consumers. The question of where data is sourced, and the ethical implications of its use, are increasingly at the forefront of data science plans. Many organizations are developing and adopting ethical frameworks and guidelines to govern their data practices, from the individual company level to international policies like the EU's General Data Protection Regulation (GDPR). The question of bias and fairness in algorithms is another ethical concern, and has led to the development of new techniques like bias audits and fairness-aware machine learning.

• Soft skills are important, too. 

• Data science is a very technical field, and the majority of skills listed in job postings today are related to the nuts and bolts of gathering, analyzing, and modeling data. As the use of data science across industries expands and evolves, though, there is more need for these professionals to work within multidisciplinary teams. This means a growing demand for data scientists who are strong communicators, good team players, and able to explain technical concepts in clear language that non-technical audiences can understand. 

• The future of data science in summary

• While there were some fears that the growth of AI would eliminate data science jobs, in reality the field seems to be not just surviving, but thriving. Over the next decade, data scientists are likely to have a transformative impact on a range of industries, meeting the ever-increasing demand for more data-driven decision-making. For those starting data science careers today, developing an interdisciplinary skill set, as well as knowledge about ethical data use, will ensure you're ready to take on whatever opportunity comes your way.