Will AI Replace Data Scientists: Future of Data Analytics
Introduction:
Whether you’ve built a career in data science or are just entering the field, the conversation around Generative AI can be unsettling. As more GenAI models gain the capability to analyze data and present insights in various formats, it’s easy to feel concerned about what that means for data scientists.
However, based on my experience witnessing the ups and downs of Silicon Valley, I want to emphasize that anyone who believes Generative AI will replace data scientists doesn’t fully understand what data scientists actually do. Data science is more art than science—more than most people realize. Below, I’ve broken down some of the roles data scientists typically play, how Generative AI might affect them, and a bit of historical context on automation in data science, its impact on the job market, and how to navigate today’s tough job landscape.
Extracting Data and Creating Visualizations for a Given Metric:
When people say Generative AI will replace data scientists, this is usually the part they’re referring to. For many business partners, data scientists are seen as “SQL monkeys” who pull data and create charts—e.g., “What % of our customers do X?” or “Create a bar chart showing retention over weeks since first interaction.”
It's true: this area is most likely to be disrupted by Generative AI. But how many data scientists actually enjoy doing simple data pulls and chart creation just to feed into strategy docs? Over the past 15 years, every analytics team I’ve worked on had a side mission of educating PMs and business stakeholders to self-serve their own data. Most data scientists will likely welcome GenAI taking over this part of the job.
Gathering Requirements from Business Stakeholders:
This is arguably the most underappreciated part of a data scientist’s role. I’ve lost count of how many meetings I’ve been in where a PM says:
“Can you look at the data and come up with ideas for which product we should build?”
or
“Why does the product have low conversion?”
Data scientists have to strike a delicate balance: sounding supportive while politely explaining that this isn’t how data science works. We often have to coach our partners through hypothesis generation, determine whether the question can even be answered through analytics (“We can’t tell you WHY customers dislike the page design”), and assess whether we’re capturing the right data to validate a hypothesis.
The collaborative spirit, understanding of a partner’s analytical maturity, urgency of the question, and our own familiarity with the product—all of this makes it very difficult for GenAI to replace this part of the job.
Ever found yourself yelling “Representative!” at an automated customer service system? That’s exactly what will happen if we try to automate this part of a data scientist’s role.
Formulating Hypotheses for Open-Ended Questions:
A question like “Why is the conversion rate down after 'Add to Cart’?” must be broken down into testable hypotheses.
Example: “Maybe adding the ‘Buy It Now’ button for each item in the cart is causing users to purchase individual items instead of completing the whole cart.”
This kind of thinking requires deep product knowledge, creative thinking around user behavior, and hands-on use of the product to identify UX issues. I remain highly skeptical that GenAI will replace this aspect of data science anytime soon. Understanding why a customer might click “Buy It Now” despite having other items in their cart requires empathy and a perception of human emotions.
(Yes, this is based on a real analysis I did.)
Creating Metrics:
Not all metrics are created equally—or with the same level of effort.
GenAI is great for coming up with high-level product metrics, and I’ve used it myself for early-stage brainstorming. It’s also helpful for generating goals or ballpark targets.
However, it tends to fail in two key areas:
Contextual goal-setting: Setting metrics that align with a company’s history, priorities, and past product decisions often requires nuanced understanding. Even if you feed GenAI every meeting note and product brief, it will likely fall short.
Deep-dive metrics: These require in-depth product knowledge and sensitivity to user sentiment—similar to what I described earlier.
In short, while business stakeholders will increasingly be able to create basic metrics and pull data themselves, setting strategic goals and crafting deeper metrics will still fall under the data scientist’s domain.
Preparing a Summary:
Early-career data scientists often make the mistake of spending too little time on summaries. I’ve worked with many analysts on this.
An analytics summary isn’t just a recap of chart insights. It includes recommendations for business or product changes, the confidence level behind those recommendations, and any assumptions made.
Without full product context, this is hard to do well. Even OpenAI’s advanced research models still struggle when multiple charts need to be integrated into a coherent story. That said, Generative AI can be a great starting point—use it to brainstorm and help with drafts.
For Those New to the Field: It’s Not the End of the World
For newcomers, this GenAIpocalypse might feel like the end. But I assure you, we’ve been here before.
When automated dashboards came along, people asked: “What will analysts do now?” (My first job was literally making charts based on a manager’s instructions.)
When natural language SQL tools appeared, the same fears surfaced.
Silicon Valley goes through hype cycles: crypto was going to upend world economies, VR was supposed to replace phones, NFTs were the next big thing. (Okay, mobile apps did live up to the hype.)
My point is—most advances in data science have actually helped data scientists become more efficient and more strategic. There's a reason the term “SQL monkey” is now mostly a thing of the past.
Career Advice for Today’s Data Science Market:
Let’s be honest: the job market is tough. But that’s not because data scientists aren’t needed. It's because companies are still figuring out what the post-GenAI world looks like. (Other job functions are going through this too.)
The best thing you can do?
Level up your skills.
Position yourself as someone who understands GenAI in analytics.
Make sure companies know they can rely on your expertise no matter how things shake out.
Update your resume using free AI resume tools to include relevant GenAI keywords—recruiters are scanning for them, even when the job description doesn’t mention it explicitly.
All the best to job seekers out there! You've got this.
