Will Data Science Be Replaced By AI? (Updated in 2024)

The adoption of AI shows no signs of slowing down, with 65% of respondents to a recent McKinsey survey stating that generative AI is regularly used in their organization. This may be exciting news for some, but others are concerned about the possibility of AI taking their place at work. This is also true in data science where AI tools have been used for years.

As AI improves, it is posed to become a major disruptor in the job market. However, some talk on the potential effects of AI on job roles can be misleading.

In this article, we’ll answer the question: Will data science be replaced by AI? We’ll examine the effects this tech is having in the broader job market, what it can and can’t do in data science, and whether this technology can actually replace data science in the near future.

Based on the current state of AI, it is unlikely that it will be capable of replacing data science in the short or medium term. It’s more likely that AI will remain a powerful tool that’s only useful in the hands of those who understand it.

Although certain job roles across different industries are being removed because of AI, in most cases, it cannot take on the full range of responsibilities a person can. It may excel in certain tasks but fail miserably in others. There is also evidence of AI making significant errors in tasks it’s believed to excel in. Therefore, despite the rush of AI adoption, it’s becoming clear that delegating everything to AI can backfire, especially without appropriate human oversight.

AI tools have also been in use in data science far longer than in other industries. In that time, the demand for data scientists only grew thanks to a need for experts who can use such tools effectively. The fact that AI is better at certain tasks has only expanded the scope of challenges data scientists can take on.

No one knows what the future of work looks like with AI in the picture. However, the technology is already affecting workers and the job market, and we may see the same replicated in data science.

Direct Job Losses

According to one report, out of the roughly 80,000 jobs cut in May 2023 in the US, 3900 were directly caused by artificial intelligence. Since it’s still early in the AI revolution, there isn’t any concrete information on whether this has trended up or down since then.

There also isn’t much information on the industries or roles being affected the most by AI. It was expected that writing roles would disappear with the rise of LLMs such as ChatGPT. However, at least one report indicates that AI has had less impact on writing roles compared to others.

AI-Powered Productivity Improvement

While AI is seen as a threat in certain fields, many are embracing it as a useful tool that can enhance productivity.

A survey by GitHub revealed that by early 2024, approximately 92% of developers in the US were utilizing AI coding tools. Around 70% also believe these tools would help them improve the quality of their code and complete projects faster. AI-powered tools like Tableau, PyTorch, and Akkio remain popular in the data science community.

Uncertainty and Worry Over AI

Unfortunately, 22% of employees today fear losing their jobs because of technology. This is 7 percentage points higher than in 2021 and not surprising given the number of reports that predict millions of jobs will soon be displaced due to technologies like AI. This anxiety over job loss and not actual job loss is, perhaps, the most significant effect AI has had.

It’s important to note that we are in an unusual post-pandemic job market, characterized by mass layoffs and speculation over the future of technology. There is no clear picture yet of whether AI is truly capable of taking over certain roles in a healthy work and economic environment.

AI Upskilling and Reskilling

Many workers are upskilling or reskilling in preparation for AI adoption in their organizations and beyond. Ongoing efforts have had mixed results due to a lack of a clear AI strategy and uncertainty over which tasks will be automated in the future. However, many workers are proactively learning to use AI tools.

An important question to ask when considering whether data science can be replaced by AI is: What jobs can AI actually do in data science? Currently, AI is being used for:

• Generating Code and Text: Large language models (LLMs) are good at generating text and functional code. The generated information is not perfect, but many have reported that these AI tools are accelerating their development cycles.
• Data Processing: Data is typically taken through a series of processes to ensure it’s of the right quality before being used in analysis or to train machine learning algorithms. AI is now being used to automate these processes.
• Data Analysis: AI is also used in data analysis thanks to its ability to recognize patterns and go through large amounts of data faster than humans ever could.
• Sentiment Analysis: LLMs can be used to quickly infer general user sentiments.
• Visualization: AI tools like Tableau enable you to generate visualizations from your data using English prompts.

Despite the significant level of human involvement when AI handles these and other tasks, the danger of over-reliance on AI is already an important emerging issue.

Current-gen AIs are incapable of replacing data scientists because they can’t handle tasks such as:

• Critical Thinking: Data scientists make decisions based on their own reasoning at every stage of solving data science problems, sometimes considering factors that may not be quantifiable by AI. Solutions from an AI tool may be complex but are limited by the data used to train it. There are perspectives it can’t factor in, which is why decision-making is often left to a person.
• Communication: AI doesn’t have the ability to properly articulate information meant for humans. This is why AI-generated text often needs to be “humanized.”
• Creativity: AI is incapable of having original thoughts and can’t come up with a creative approach to a problem. This limits its usefulness when confronted by a new problem or an old problem in a new setting. This also limits its ability to infer novel insights from data.
• Ethical Decision-Making: Working with data comes with many ethical considerations, including how the data was sourced, if the data has biases and the impact of these biases, the social impact of a data-driven decision, etc. AI can’t take these considerations into account.
• Contextualizing Data and Insights: Data scientists have a deeper understanding of the fields they work in. They can assess the data and results from analysis in the right context and come up with more meaningful insights.

A common expression in this age is, “AI won’t take your job, but someone using AI might.” As powerful as current-gen AI appears to be, they are still tools that require human beings to wield them. By embracing AI instead of worrying about whether it’ll replace them, data scientists can put themselves in a position to thrive in the coming years.

Some of the steps you can take to make AI an invaluable tool in your data science work are:

• Master Your Craft: Becoming a better data scientist in your domain and becoming proficient in different aspects of data science will help you get the most out of AI and your own skills.
• Understand AI/Upskill: Get familiar with different AI concepts to understand how AI works, its capabilities, and its limits. This is the key to leveraging the power of AI tools.
• Identify Opportunities to Incorporate AI: Once you have a solid understanding of data science and what AI can do for you, assess your work and workflow and see if there are tasks that can be performed better or faster with the assistance of an AI tool.
• Develop Your Own AI Tools: Although they recognize its power, many companies lack the expertise to make good use of AI. Being able to create AI tools that are tailored to solve your organization’s unique problems will make you an asset.
• Understand the Limits and Risks of AI: As many companies rush to adopt AI, they will find themselves facing certain challenges because they don’t fully understand the limits and risks of this technology. Understanding these issues means you’ll be able to avoid them in your work.
• Commit to Continuous Learning: AI is still evolving, and so are the skills you’ll need to work with them. Take the time to discover which new technologies are being developed and figure out how you can use them when the time comes.

There has been a lot of speculation over which jobs AI will replace, and data science roles have been mentioned. Many AI tools are already used in this field, and some wonder if they will one day perform everything a data scientist does. As powerful as AI is, it’s still lacking in critical aspects and relies on humans to achieve the impressive results we see. By approaching AI as just another tool, data scientists can learn to leverage its power and improve their job security.

If you’re looking to dip your toe into the world of AI as a data scientist, Interview Query can help you get started. Our modeling and ML learning path offers a solid introduction to this field while giving you an idea of which AI skills employers are searching for. If you’re interested in job roles that require using AI tools, visit our job board and find out who’s hiring. You can also try your hand at some machine-learning interview questions and see how you might be tested. Check out this article to find out more about the state of data science amid the rise of AI jobs.

Witnessing AI’s power can be exciting and worrying, but at the end of the day, it’s just a tool that can benefit your career in data science if you learn to use it.