I’ve heard multiple sentiments from reddit and irl that DS is a dying field, and will be replaced by ML/AI engineering (MLE). I know this is not 100% true, but I am starting to worry. To what extent is this claim accurate?

From where I live, there seems to be a lot more MLE jobs available than DS. Of the few DS jobs, some of the JD asks for a lot more engineering skills like spark, cloud computing and deployment than they asked stats. The remaining DS jobs just seem like a rebrand of a data analyst. A friend of mine who work in a software company that it’s becoming a norm to have a full team of MLE and no DS. Is it true?

I have a background in social science so I have dealt with data analytics and statistics for a fair amount. I am not unfamiliar with programming, and I am learning more about coding everyday. I am not sure if I should focus on getting into DS like my original goal or should I change my focus to get into MLE.

I am studying CS (2nd year) but my passion is for data science, not SWE. I'd like to work with analysing data, writing reports and coding, but it appears this field is sadly stale. Are there any signs it's gonna get better, or should I just change my career plans entirely?

I am currently studying Data Science and really fell in love with the field, but the more i progress the more depressed i become.

Over the past year, after watching job postings especially in tech I’ve realized most Data Scientist roles are basically advanced data analysts, focused on dashboards, metrics, A/B tests. (It is not a bad job dont get me wrong, but it is not the direction i want to take)

The actual ML work seems to be done by ML Engineers, which often requires deep software engineering skills which something I’m not passionate about.

Right now, I feel stuck. I don’t think I’d enjoy spending most of my time on product analytics, but I also don’t see many roles focused on ML unless you’re already a software engineer (not talking about research but training models to solve business problems).

Do you have any advice?

Also will there ever be more space for Data Scientists to work hands on with ML or is that firmly in the engineer’s domain now? I mean which is your idea about the field?

I've been casually browsing job postings lately just to stay informed about the market, and honestly, I'm starting to wonder if the classic "Data Scientist" position is becoming a thing of the past.

Most of what I'm seeing falls into these categories:

• Data Analyst/BI roles (lots of SQL, dashboards, basic reporting)

• Data Engineer positions (pipelines, ETL, infrastructure stuff)

• AI/ML Engineer jobs (but these seem more about LLMs and deploying models than actually building them)

What I'm not seeing much of anymore is that traditional data scientist role - you know, the one where you actually do statistical modeling, design experiments, and work through complex business problems from start to finish using both programming and solid stats knowledge.

It makes me wonder: are companies just splitting up what used to be one data scientist job into multiple specialized roles? Or has the market just moved on from needing that "unicorn" profile that could do everything?

For those of you currently working as data scientists - what does your actual day-to-day look like? Are you still doing the traditional DS work, or has your role evolved into something more specialized?

And for anyone else who's been keeping an eye on the job market - am I just looking in the wrong places, or are others seeing this same trend?

Just curious about where the field is heading and whether that broad, stats-heavy data scientist role still has a place in today's market.

It seems like either you need to transition to ML Engineer, which needs to pick up Software Engineering skills, or you need to accept that you are a Data Analyst who can formulate business problems into technical solution, but you may not get to work too much on real ML problems all the time.

Data scientist work is either getting more automated, or you gotta pivot into some other role.

DS teams are starting to lose the essence that made them truly groundbreaking. their mixed scientific and business core. What we’re seeing now is a shift from deep statistical analysis and business oriented modeling to quick and dirty engineering solutions. Sure, this approach might give us a few immediate wins but it leads to low ROI projects and pulls the field further away from its true potential. One size-fits-all programming just doesn’t work. it’s not the whole game.

Is Data Science Really a Dying Field?

Hey everyone, I've been seeing a lot of talk lately about data science being a "dying field" or reaching a saturation point. As someone who's been working in the industry for a few years now, I wanted to share my thoughts and spark a discussion.

Is there any truth to these claims?

On the one hand, it's true that the initial hype surrounding data science has cooled down. The days of "data scientist" being the sexiest job of the 21st century are probably over. However, I believe this is a natural progression as the field matures.

The demand for data skills is still incredibly high. Companies are generating more data than ever before, and they need people who can analyze it and extract valuable insights. In fact, the Bureau of Labor Statistics projects a 22% growth in data science jobs over the next decade, which is much faster than the average for all occupations.

However, the landscape is definitely changing.The days of "jack-of-all-trades" data scientists are fading. Companies are now looking for specialists with deep expertise in specific areas, such as machine learning, natural language processing, or data visualization. Additionally, the barrier to entry is getting lower as more and more educational resources and tools become available.

So, is data science dying? Absolutely not. It's simply evolving. The field is becoming more specialized and competitive, but the opportunities for those with the right skills are still immense.

What do you guys think? Is data science a dying field? What are your thoughts on the future of the industry?

Let's discuss!

P.S. I'm also curious to hear from people who are just starting out in data science. What are your biggest challenges and concerns?

Im an industrial engineer whos gonna graduate by the end of the month. Ive been studying data science from the past 6 months (took ibm data science speciality, jose portilla's udemy course machine learning for data science masterclass, python, sql)

Im currently lost on what steps to take next

I sat down with a data scientist today and tried to ask for advice, he told me he doesnt even think that data science will stay, its gonna be replaced by AI. Especially the machine learning algorithms and classification methods (trees,boosting,etc) they aret being built from scratch anymore

Im totally lost now and dont know what next steps to take and what to learn next. Should i pursue business analysis/data analysis/what courses to take/what skills to learn, and you see how my brain is exploding

I’ve seen a lot of "doom-posting" lately claiming that AI has automated Data Science into extinction. If you listen to the hype, ingestion is automated, models are AutoML-ed, and inference is just an API call.

As someone in the trenches at a FAANG company, I want to clear the air. Is the "traditional" role dead?

I was thinking of applying for OMSA but seeing the recent posts on this sub about how people with a lot of experience and credentials can't find jobs anymore makes me doubt my choices. I work as a Data Scientist now, but should I be actively working towards transitioning into software developer roles to have more job security in the future? Or the data science job market is slow now because of the fears of the upcoming recession and will improve in the future?

Read an intresting article today, Thought I would share it here.

https://analyticsindiamag.com/is-data-science-a-dying-field/

What are your views?

Calling all data scientists, ML engineers, AI researchers, and anyone working in the data/AI ecosystem — I’m hoping to get honest insight from people in the field.

I’m currently deciding my career direction, and Data Science has been one of the main areas I’ve been considering. But with the rapid rise of automation, LLMs, and AI-driven tools, I keep hearing discussions about data science roles shrinking or becoming obsolete. This has made me question whether it is still a reliable long-term path.

I want to understand whether Data Science is still worth entering in 2026, or whether the field is becoming too automated for stable career growth. Are companies reducing traditional DS positions, or are the roles simply evolving into something more technical, such as ML engineering, AI engineering, data engineering, or AI-focused product roles?

If the field is changing, I would also appreciate guidance on which skills someone starting in 2026 should prioritize to remain relevant by 2030 and beyond.

I’m also interested in a realistic view of opportunities both in India and abroad. Is Data Science still stable and in demand worldwide, or is the market becoming saturated and uncertain?

Any genuine insight or experience would be extremely helpful as I try to make an informed long-term decision.

dinner encourage toothbrush airport future strong person placid merciful exultant

This post was mass deleted and anonymized with Redact

Hi everyone, I’m currently studying data science, but I’ve been hearing that the demand for data scientists is decreasing significantly. I’ve also been told that many data scientists are essentially becoming analysts, while the machine learning side of things is increasingly being handled by engineers.

• Does it still make sense to pursue a career in data science or should i switch to computer science? I mean i dont think i want to do just AB tests for a living

• Also, are machine learning engineers still building models or are they mostly focused on deploying them?

I’ve been working in data science for the last ten years, both in industry and academia, having pursued a master’s and PhD in Europe. My experience in the industry, overall, has been very positive. I’ve had the opportunity to work with brilliant people on exciting, high-impact projects. Of course, there were the usual high-stress situations, nonsense PowerPoints, and impossible deadlines, but the work largely felt meaningful.

However, over the past two years or so, it feels like the field has taken a sharp turn. Just yesterday, I attended a technical presentation from the analytics team. The project aimed to identify anomalies in a dataset composed of multiple time series, each containing a clear inflection point. The team’s hypothesis was that these trajectories might indicate entities engaged in some sort of fraud.

The team claimed to have solved the task using “generative AI”. They didn’t go into methodological details but presented results that, according to them, were amazing. Curious, nespecially since the project was heading toward deployment, i asked about validation, performance metrics, or baseline comparisons. None were presented.

Later, I found out that “generative AI” meant asking ChatGPT to generate a code. The code simply computed the mean of each series before and after the inflection point, then calculated the z-score of the difference. No model evaluation. No metrics. No baselines. Absolutely no model criticism. Just a naive approach, packaged and executed very, very quickly under the label of generative AI.

The moment I understood the proposed solution, my immediate thought was "I need to get as far away from this company as possible". I share this anecdote because it summarizes much of what I’ve witnessed in the field over the past two years. It feels like data science is drifting toward a kind of pseudo-science where we consult a black-box oracle for answers, and questioning its outputs is treated as anti-innovation, while no one really understand how the outputs were generated.

After several experiences like this, I’m seriously considering focusing on academia. Working on projects like these is eroding any hope I have in the field. I know this won’t work and yet, the label generative AI seems to make it unquestionable. So I came here to ask if is this experience shared among other DSs?

I feel like the ML tooling and infrastructure are improving a lot, and in the near future, we wouldn't need ML engineers. Data scientists will directly deploy their models. Software engineers and data engineers will handle the rest

I love analyzing data and building models. I was a DA for 8 years and DS for 8 years. A lot of that seems like it's gone. DA is building dashboards and DS is pushing data to an API which spits out a result. All the DS jobs I see are AI focused which is more pushing data to an API. I did the DE part to help me analyze the data. I don't want to be 100% DE.

Any advice?

Edit: I will give example. I just created a forecast using ARIMA. Instead of spending the time to understand the data and select good hyper parameter, I just brute forced it because I have so much compute. This results in a more accurate model than my human brain could devise. Now I just have to productionize it. Zero critical thinking skills required.

As the title say ,I saw it in few pages that the demand of data analyst are going down, as a 3rd year data science engineering should I be worried about the future. I have done an internship in Market analyst field and I really wanted to work in Data analyst field,can anyone suggest me some tips???