Feature
Data science and artificial intelligence are intrinsically linked. AI is trained on large sets of data, which are often cleaned, organized and prepared for the AI training process by data scientists. But data scientists turn to AI, too, to expedite their work. AI can help them find fast answers in large datasets, lighten their workload and much more.
“Tools like Copilot have become indispensable for data scientists, transforming the way we approach complex projects,” explained Sebastien Paquet, VP of machine learning at Coveo. “These tools simplify intricate computations, accelerate experimentation, and provide real-time expertise, enabling data scientists to focus on delivering insights and innovation.”
Data science is labor-intensive work. The fruits of this labor are often used by company executives to make strategic decisions, guide product development and inform everything from hiring to spending. They need quick answers. And, as the data stores grow, this becomes an increasingly unwieldy task.
“In a field where managing vast datasets and optimizing models can be daunting, leveraging AI tools isn’t just an advantage — it’s a necessity," said Paquet.
6 Reasons Data Scientists Use AI
Let’s explore some of the key reasons why data scientists use artificial intelligence.
1. To Make Better Predictions
“Data scientists use AI because it makes their work faster, smarter, and more scalable,” said Gev Balyan, founder of Ucraft. Data scientists, for example, are often tasked with tapping historical data to make informed predictions about the future. This chore, called predictive analytics, is time-consuming and requires the data scientist to comb complex datasets hunting down patterns.
“The beauty of using AI in data science is its ability to simplify complexity,” added Andrew Dunkel, data and automation specialist at DotOneSix. “ChatGPT can look at a dataset and highlight key patterns, anomalies, or trends — things that might take hours to uncover on our own."
Not only does this save time and energy, allowing the data scientist to speed up the analysis process, but it can lead to better predictions. “AI models, like machine learning algorithms, can analyze historical data and predict future trends,” said Balyan. “Whether it’s user behavior, market shifts, or customer needs. This helps businesses make proactive decisions.”
Robin Patra, head of data at ARCO Construction Company, said this ability proved useful when his company’s teams were deciding where to focus efforts at improving safety. “Machine learning was employed to predict which project types and conditions were most prone to accidents. NLP techniques revealed process failures and recurring safety violations from thousands of unstructured reports. AI tools assessed drone and safety camera solutions from vendors, analyzing their capacity to detect and prevent hazards. And AI-based simulations tested the effectiveness of various safety interventions under real-world conditions.
“This initiative not only reduced workplace hazards but also underscored how AI amplifies the capabilities of data scientists. AI didn’t replace the human element; it augmented decision-making, bringing depth and scale to their expertise.”
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2. To Automate Work
When you do a job — like cleaning and preparing data — you often do the same task repeatedly. This is boring for humans, but AI doesn’t mind at all.
“AI tools can automate tedious tasks like detecting missing data, correcting inconsistencies, or labeling datasets,” said Balyan. “It frees up time for more critical work, like building models and interpreting results.”
According to an interview with Alexander Gray, former VP, foundations of AI at IBM, AI is often used to automate hyperparameter optimization (HPO). “The most straightforward place to start is at the end of the data science pipeline, the modeling stage. It is easy and straightforward to automate HPO because you can see immediate gains in your data science projects.”
Gray believes automation creates an opportunity for data scientists, not a threat to their jobs. “Data science automation will actually create new opportunities for many new people to enter data science who previously had limited ways to participate,” he says. “I believe it will enable the creation of entirely new job categories, allowing much wider participation in the AI revolution.”
3. To Find Patterns & Trends
“AI is also useful when it comes to pattern recognition in large datasets,” explained Balyan. “AI could, for example, be used to analyze user interactions and identify patterns, like which design elements lead to higher engagement.”
Because AI can process huge amounts of data quickly, it can identify patterns that are difficult for a human to spot, often finding trends that a person wouldn’t see because the pattern is small, localized, complex or applies only to a certain demographic or geography.
“Machine learning and deep learning algorithms enable data scientists to extract complex patterns from big data,” said Alex Li, founder of StudyX. “AI can automatically select features, optimize models, and achieve predictive analysis through training algorithms, which are more efficient and accurate than traditional statistical methods.”
This is all done under the careful supervision of the data scientist. The AI is an assistant, not a replacement.
4. To Get Faster Answers
Data science is time consuming work. But the corporate decision-makers who turn to data scientists to help them understand trends and patterns need answers quickly. AI can speed up the process, even as data sets grow to unwieldy proportions. “AI accelerates processes like data wrangling, analysis, and visualization, cutting the time to insight significantly,” said Patra.
With AI tasked with pulling answers, finding relevant data and looking for patterns and trends, the data scientist is free to analyze results, bringing human intelligence to the process more quickly and delivering answers to the people who need them faster.
“With AI, data scientists can handle massive datasets quickly, something traditional methods struggle with,” claimed Balyan.
5. To Save Time
AI can study data, process it and spit out a formatted summary or analysis in seconds. This saves data scientists, working in a range of industries, enormous time. When working as a behavioral economist, Ida Byrd-Hill, now CEO of Automation Workz, relied on it. “I utilized ChatGPT to create a 92-page economic report. It can compile data at a much faster rate than I could manually. I shaved off 200 hours of data crunching.”
AI can take over other slow, cumbersome tasks that data scientists would rather not spend time on, too. “AI is designed to take over repetitive tasks, which comprise a big chunk of work for data scientists,” said Dima Eremin, co-founder of BluedotHQ. “Things like data cleaning and formatting can be easily performed by AI, which saves data scientists tons of time.”
It can also do data analysis, under the supervision of a human. “AI can identify patterns and analyze data much faster than humans,” noted Eremin. And it can do it without the human error that is often introduced into the process. “This is especially handy for dealing with large datasets where the chances of human error as a result of manual data processing are quite high.”
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6. For Better Data Analysis
“The first step of any new project is understanding the data,” explained Dunkel. “Two years ago, this meant pulling the data into a Jupyter notebook, cleaning it, and performing exploratory analysis. Today, tools like ChatGPT complement this process by helping us interpret datasets in natural language, answering questions, and suggesting potential analytical approaches. While it does not yet replace traditional tools, it’s a powerful assistant for accelerating initial exploration and decision-making."
That cleaning process is, for the data scientist, a huge, cumbersome and dull project. As such, it is fraught with error and inefficiency.
“AI can greatly improve the efficiency and depth of data analysis,” said Li. “In traditional data science processes, data cleaning is often a time-consuming step. But AI can quickly identify and correct missing values, outliers, and even automate data format conversion. This automates the tedious work in the data preparation phase and saves data scientists a lot of time.”
The AI can also help the data scientist use data that is too uncorrelated to be much use otherwise. Li added, “These models are also capable of processing large amounts of unstructured data such as text, images, and speech, expanding the application scenarios of data science.”
