With the rise of AI in the workplace, many students have questions about finding a job, especially if they are just entering the field. Data science, like other fields, has begun integrating AI to help streamline processes and increase productivity, but it still has numerous areas of improvement.
While AI will not take over data science (at least not anytime soon), it is changing key aspects of the discipline. In this article, we will discuss how AI is being used in data science and how that will affect the job market, specifically entry-level jobs.
AI in Entry-Level Roles
AI is already affecting daily workflows for data scientists at every level of employment, but has the biggest effect on entry-level jobs. AI excels at gathering huge amounts of data and checking for common bugs, so jobs that involve menial, routine, and repetitive tasks are the most likely to be supplemented with AI.
Tools like ChatGPT can assist with various tasks that were once delegated to junior workers, such as routine dataset cleaning, writing scripts in programs like Python or R, and building simple regression models. This reduces manual repetitive tasks and time spent on low-level coding but also reduces the traditional job opportunities for new graduates as fewer employees will be needed to complete these tasks and oversee AI work.
What is considered an entry-level job is changing with the integration of AI in the workplace. Many basic coding jobs are harder to get and hiring managers expect workers to have more hard and soft skills than has been previously expected. Because of the rising expectations of graduates going into the workforce, entry-level jobs will likely become more competitive.
Employers will most likely hire less workers initially and expect the ones they do hire to take on more complex tasks sooner. This includes opportunities to work on more human aspects of the job, like explaining data, or understanding how to work with AI to prompt it and validate its outputs. Even with these changes, the job outlook for data scientists is projected to grow.
Human-AI Collaboration
In its current state, AI cannot perform well without human monitoring and training. Mistakes are still being made by AI, for example, Large Language Models (LLMs) like OpenAI or Gemini use predictive text patterns to generate text. This raises the possibility that the data the AI is putting out is confidently wrong or inaccurate.
Data science needs humans who can come up with unique solutions to new and unforeseen problems, as AI can struggle with these topics. It has saved a lot of time and is a useful tool in a multitude of ways but cannot run successfully with zero input from humans.
Not only does AI still often need human supervision and correction, but it often can only handle automated systems. Communication, for example, with other workers and especially with stakeholders about data findings, is an important skill that can only be successfully completed by humans.
Jobs that require critical thinking, problem solving, and where human judgment and creativity are key are the most likely to keep employing humans. This includes jobs where graduates interpret data, explain results, and validate AI outputs.
Data science professionals are still the backbone of their field, and should consider AI to be another tool in their toolbox, not another worker. Human-centered skills are more important than ever to hone when going into the field, as these are the skills that AI does not possess. Humans can have a strong understanding of the business they are working for and the specific needs that need to be addressed.
They can identify the right problem to solve and handle unexpected or messy datasets that AI might not be able to understand. Skilled workers are also able to make ethical decisions about data use that could affect major company decisions. In the long run, these skills have long-term value for companies and show that AI has not negated the need for data science professionals in the workplace.
The Bottom Line
Data science is not the only field that is changing because of the effect of AI. However, change is always more gradual than often expected. Because there is limited historical data on Generative AI (GenAI) in the workplace, it is difficult to accurately predict the timing and scale of a technology that is still relatively new.
The ways in which organizations employ AI can change dramatically as more information comes out about GenAI, and workers learn to adapt to the use of this new technology in the workplace. While AI will change many aspects of data science, it is highly unlikely that it will completely replace the human workforce anytime soon.
Citations
AI Impact on Entry-Level Jobs: Why Junior Roles Are Vanishing - Digital Digest
Will AI wipe out entry-level jobs? - Computer Weekly
AI Is Causing Entry Level Roles to Evolve, Not Vanish - SAP
Incorporating AI impacts in BLS employment projections - U.S. Bureau of Labor Statistics
Why You Should Stop Worrying About AI Taking Data Science Jobs - Towards Data Science
Data Science Isn’t Dying — It’s Evolving: How AI Is Reshaping the Role - Medium
What Data Scientists Do - U.S. Bureau of Labor Statistics