data science job market reddit

Is the traditional Data Scientist role dying out?

reddit.com › r › datascience › comments › 1ksev5p › is_the_traditional_data_scientist_role_dying_out

Product Data Scientist at any tier 1-3 tech firm will be this type of role. But you'll still be doing lots of SQL Answer from pretender80 on reddit.com

reddit.com › r/datascience › career advice for new grads or early career data scientists/analysts looking to ride the ai wave

r/datascience on Reddit: Career advice for new grads or early career data scientists/analysts looking to ride the AI wave

February 17, 2026 -

From what I'm starting to see in the job market, it seems to me that the demand for "traditional" data science or machine learning roles seem be decreasing and shifting towards these new LLM-adjacent roles like AI/ML engineers. I think the main caveat to this assumption are DS roles that require strong domain knowledge to begin with and are more so looking to add data science best practices and problem framing to a team (think fields like finance or life sciences). Honestly it's not hard to see why as someone with strong domain knowledge and basic statistics can now build reasonable predictive models and run an analysis by querying an LLM for the code, check their assumptions with it, run tests and evals, etc.

Having said that, I'm curious what the subs advice would be for new grads (or early career DS) who graduated around the time of the ChatGPT genesis to maximize their chance of breaking into data? Assume these new grads are bootcamp graduates or did a Bachelors/Masters in a generic data science program (analysis in a notebook, model development, feature engineering, etc) without much prior experience related to statistics or programming. Asking new DS to pivot and target these roles just doesn't seem feasible because a lot of the time the requirements are often a strong software engineering background as a bare minimum.

Given the field itself is rapidly shifting with the advances in AI we're seeing (increased LLM capabilities, multimodality, agents, etc), what would be your advice for new grads to break into data/AI? Did this cohort of new grads get rug-pulled? Or is there still a play here for them to upskill in other areas like data/analytics engineering to increase their chances of success?

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The reality is a lot of platforms have matured and the low hanging fruit on the product roadmaps were completed 10 years ago so SaaS products started making in house analytics tools and focusing integrations with other systems (ex: connect your random ERP to Hub Spot or whatever). This was happening before LLMs took off and the current AI cycle just accelerated it. The writing was on the wall already for data analytics work. The advice for new grads is still kind of the same as it was when the labor market got saturated around COVID, show people what you can build. No one cares about Titanic dataset tutorials or a portfolio of Jupyter notebooks with 30 pages of “report”. People want to see a unique insight on novel data, a project that conveys a sense of architecture, or some job experience that goes beyond homework. New grads and soon to be grads should be hammering every internship possible and working on projects that aren’t just resume padding.

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As a Gen X adjacent to data science at work. Don’t get hung up on titles, or even the specifics of what you “think” you should be doing every day. Try and learn about the business where you work, or if it’s research/non profit/academic, learn about what actually goes on, when people ask you to help them with something, set boundaries, but help, you will learn more and probably be more effective long term than if you are too rigid and only want “strict” data science, math, modeling problems. If you’re seen as someone who can solve a problem with data you’re likely to be asked about math/modeling and other problems later. I work at a major manufacturing company. The grads we hired who dug in and tried to help build solutions with python/r did great, the ones who only wanted to build models struggled. Data science as a title was very popular for 10+ yrs but the work they did was more of a combination of bi/data engineering/data science. I’ve been in analytics for about 17 yrs.

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May 23, 2013 - Would genuinely appreciate honest advice on the best strategy in the current market, especially for someone coming from a strong analytics/BI background in Big Tech. #DataScience #CareerAdvice #Analytics #MachineLearning #BusinessIntelligence #Amazon #Microsoft #TechCareers #DataAnalytics · Harmonic Security - Cybersecurity + AI startup in the UK ... We're expanding our data science team and looking for a Data Scientist with at least three years' end to end production experience, NLP, and classification experience.

reddit.com › r/datascience › is the traditional data scientist role dying out?

r/datascience on Reddit: Is the traditional Data Scientist role dying out?

May 22, 2025 -

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.

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I think data scientist has split into two directions. The more technical ones have been absorbed into the engineering world (AI/ML Engineer), while the less technical ones have been absorbed into more data analyst / BI roles.

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The "traditional" (I'm not that old and remember before businesses knew what this was, how is it a tradition already?) role barely ever existed. The number of businesses with the type of problems and data where you need data science are very few. But data was the new hotness and FAANG uses it, so businesses started hiring data scientists. So 'data science' as a business term often was shorthand for something like 'guru rockstar ninja business intelligence person'. Or maybe 'full stack data person' in some cases. And now we're on the wrong side of the hype cycle and businesses are being more careful about how they throw money into data, and hirers can be more picky and don't need to use a flash term like scientist to entice applicants. So the fake data science roles aren't as common. I doubt there's a dropoff of actual data science getting done in those companies that have a genuine use for it. But there are definitely fewer roles advertised for data science, which is why I think it's mostly the 'fake' data science roles that have gone.

reddit.com › r/datascience › job market getting any better or nah?

r/datascience on Reddit: Job market getting any better or nah?

August 13, 2025 -

I’ve been staying in my role and refusing to leave for the last several years. I’m wondering if there’s any signs yet the job market is coming back yet or if we’re still stuck in the slog

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It doesn’t seem too bad for experienced professionals. Entry level is just brutal though.

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I have more than 5 years of having data scientist in my job title and have gotten some interviews and some second or third interviews, but no offers. I'm also only willing to accept remote roles for personal reasons, but I'm around 300 applications in without success. I'd be interested in people's experiences with remote roles this year.

reddit.com › r/careerguidance › should i pursue data science in 2026, or is the field at risk because of ai?

r/careerguidance on Reddit: Should I pursue Data Science in 2026, or is the field at risk because of AI?

December 12, 2025 -

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.

Top answer

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Data Science, in 2026 and beyond, will likely evolve rather than disappear. Automation and AI may handle repetitive tasks, but they can’t replace the critical thinking, domain knowledge, and creativity that a data scientist brings. Instead of focusing solely on traditional data science skills, consider enhancing your ability in data engineering, ML, AI, and product-orientated roles. Worldwide, data science remains in demand, but roles may become more specialized. In the ever-evolving tech industry, adaptability is key.

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With network and connections yes. For the opportunity to learn and expand yes. As a financial investment no, you're better off putting the money in the sp500, the payout is more secure.

reddit.com › r/datascience › indeed: tech hiring is down 36%, but data scientist jobs held steady

r/datascience on Reddit: Indeed: Tech Hiring Is Down 36%, But Data Scientist Jobs Held Steady

January 19, 2026 - Masters degree in data science. I’ve applied for 50 jobs and have only gotten three interviews. One of them ghosted me during the third round which was so odd ... i know the market has only gotten worse, but i applied for 400+ jobs in late 2024 and only got 4 interviews

reddit.com › r/leavingacademia › is something up with the data analyst/data scientist job market right now or is it just me?

r/LeavingAcademia on Reddit: Is something up with the data analyst/data scientist job market right now or is it just me?

December 15, 2024 -

Background: I have a PhD in social psychology that I completed in Spring of 2023. The last few years of my doctorate, I worked full time for ~2.5 years as an evaluation coordinator for a process evaluation of a statewide gun violence reduction program. After this (and most recently), I worked ~2 years full time in a supervisory role at a state office focused on criminal justice programs working with data, writing legislative reports, and doing some grant management.

Miscellaneous skills: I know R, SPSS, Power BI, and some SQL. I’m well-versed in multivariate stats, psychometrics, and even some Bayesian inference. I’m used to working with lots of forms of data, ranging from survey data to public datasets from the census bureau/FBI to SQL databases accessed through ODBC connections. I only have 4 peer-reviewed publications and only taught 2 classes during my PhD, but that’s largely because I pivoted towards acquiring non-academic work experience somewhat early in my program.

Problem: I’ve been aggressively applying to multiple positions for the past six months with very disheartening results. I’ve mostly focused on the public sector plus some non-profits and think tanks (I’m geographically close to the DMV, so the government-industrial complex is really THE big employer where I am). I’ve recently started applying to more private sector jobs too, though. Out of the dozens of positions I’ve applied to, I’ve only gotten one real interview. It’s rough…

Has anyone else in a similar position who left academia been experiencing this? Any advice to improve my search and/or prospects?

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Oh, PhD in Physics here from a good US university. 10 years of experience, with python, c++, statistics, a bunch of papers, all about analyzing data. No jobs. A PhD won't get you automatically a job in DS.

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The myth that there are plentyfull data jobs out there in industry for PhDs to pivot into was true 10 years ago but no longer. Yet it keeps being perpetuated in academic circles, because academia wants you to believe there are paved transition paths. 10 years ago, machine learning was new and companies went all in on hiring anyone they could as a data scientists, because they believed that they were sitting on untapped riches: their operational data. The frenzy gave rise to data science programs to cater to the demand and tons of PhDs who reskilled to get into the market. Then companies came to realize they can't do much with their expensive data scientists because their data is scattered, inconsistent, and low quality. Data scientists spend 90% of their time trying to get data and clean it up. Even when data science delivers a result, the impact is often disappointing in terms of ROI (see every churn model). Rarely do companies need fancy ML beyond a basic linear regression. Spagetti code from data scientists could not be reused. The focus shifted to building better technological foundations to support data operations. Roles in data engineering, platform engineering, ML engineering replaced a lot of data science. People with a software engineering background are better suited to these roles than academics or data science graduates. For the insights part, companies realized data analysts who run an ad-hoc SQL query on the data warehouse or work in Excel is enough. The data science hype died down, the market dried up, but the graduates and academics kept coming. For the data science positions that remained, companies could afford to be much more selective. Generally they prefer to hire those who worked in the industry; everyone else is considered a starter. Now the situation is even worse. There is a lot of economic uncertainty. For most companies, data is just a nice to have, not core business. Many are looking at their expensive data department as a source of savings. There are layoffs across the industry. So I can totally imagine you are struggling. The market for data science has been bad for years, and now the market for data roles overall is down too. Even with the technologies you say you know, companies look at your academic background and see a risk. Consider whether the following are an option for you: Expand your scope for roles & industries. Look for data analyst, data engineer, data manager, consultant, business analyst roles. Adjust based on whether you prefer technical work or working with stakeholders. Expand your geographic search radius Try to get a referal. This works best in smaller companies, and is so much more effective than applying through job boards. Do you know anyone who works where you would also consider working, and who could vouch for you and your skills? Reach out personally and ask whether they are hiring, and tell them about the value you bring to the table. This is how I got my first job as a data engineer after my post-doc. I came from applied physics. Best of luck!

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reddit.com › r/datascience › what's up with the data scientist/statistician job market?

r/datascience on Reddit: What's up with the data scientist/statistician job market?

December 13, 2023 -

Looking at the job boards on LinkedIn - there is a Biostatistician position with Orbis Clinical which has 1,697 applicants already. Saw another posting with over 500 applicants. Insane.

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An excess of entry level candidates driven by the endless bootcamps/micromasters/cash grab masters Layoffs in the industry leading to lots of experienced candidates looking for jobs Coordination failure leading to an arms race between candidates having to apply for more and more jobs and hiring managers becoming increasingly stringent with their ATS LinkedIn tracking click through and not actual applications

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80% of those are not qualified candidates.

reddit.com › r › datascience

Data Science

August 6, 2011 - On average, I have received a 0.75% salary hike over the last 5 years, which I know is pretty unreasonable. I have been looking for a new job, but given the current market, I cannot say for certain when I will find a new role.

reddit.com › r/datascience › my data science dream is slowly dying

r/datascience on Reddit: My data science dream is slowly dying

June 18, 2025 -

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?

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806

Principal DS here in healthcare (population health). Obviously I don’t know enough about you but here are my thoughts. 1) Be open-minded. Product analytics can be really cool. You might learn things along the way that will excite you and open up new paths, so don’t box yourself in. 2) Stop thinking like “MLEs do this, DAs do that, etc”. Instead think like “what does my company/project need and how can I add value?”. Hunt for opportunities to add value, and if you discover a ML opportunity, try to build it quickly and take ownership. Your leaders will thank you. 3) my day-to-day is diverse involving a little ML, basic analytics, AWS infrastructure management, LLMs, control group studies, etc. I LOVE the variety as it keeps me fresh and always learning

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I’m sorry to break it to you but you need to learn and be very comfortable with software engineering as a discipline. The need for data scientists that just research and apply ML modules in a non production capacity is gone. They might still need them in research but that’s a niche at this point and any corporation that is looking to leverage data science will not put up with a data scientist who just researches and can’t build production quality code

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schiller.edu › can you work in ai without a computer science degree?

Can You Work in AI Without a Computer Science Degree?

March 3, 2026 - Learn basic data concepts: Understand terms like training data, model accuracy, overfitting, and bias. You do not need to code; just comprehend. See how AI translates into careers: Explore 'What Can You Do with an Artificial Intelligence Degree in Today’s Job Market?'. This helps you connect your learning to tangible roles, industries, and opportunities.

reddit.com › r/dataanalysiscareers › job outlook for data analysts: really good or really bad?

r/dataanalysiscareers on Reddit: Job Outlook for Data Analysts: Really good or really bad?

April 4, 2025 -

I graduated a few years ago with an unrelated bachelor's degree and have held a variety of low-level roles since then. I'm thinking of making a career change to try to make a more stable career/income for myself. One of the career areas I am considering is data analytics.

My goals for a career are that:

--It will be reasonably possible to get an entry level job paying $50,000 or more

--I can get the experience I need to apply for that job within about a year

However, I seem to be getting mixed signals as to whether or not data analytics fits that bill. On the one hand, the U.S. Bureau of Labor Statistics says the job outlook is really really good, projected to grow at about 36% per year between 2023-2033--well over the average for other careers(1) .

This makes me think that, with the Google Data Analytics cert, some self study, and maybe a cheap grad program (I was thinking Eastern University's MS in Data Analytics), it would be fairly easy for me to get an entry-level position.

However, when perusing this reddit, I see a lot of people commenting about how the job market is terrible, especially for entry level positions. And frankly, I seem to see that for virtually every career that is out there.

Are these reddit fears overblown or only relevant for those pursuing certain specific specialties? Or is the U.S. Bureau of Labor Statistics only showing a very deceptive sliver of the story?

I would appreciate any insight you can give me on this. (Or on anything else you feel is pertinent to comment on about my plans/trajectory, etc).

Thanks y'all :)

1: https://www.bls.gov/ooh/math/data-scientists.htm#tab-1

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I personally know a guy, in his mid 30s, who had no tech experience, a degree in history and who had been teaching English in China, who managed to become a python developer in a startup tech firm. I also know people with advanced stem degrees and multiple years of tech experience struggle to get a job. It depends on you. Your ability, your perseverance. No one can say if you'll make it or not, nobody is certain. All a person can do is try. But trying almost always has a better chance of success, than not trying at all. As far as personal advice goes, work on some decent portfolio projects, get a reputed certification (Google data analytics cert is good and beginner friendly) and try networking. Reach out to recruiters and senior tech people in small to mid level companies and show your interest in working for them. And keep learning something new everyday on the side. They will increase your chances.

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I’m DA and recently was a lead on a hiring team. I’ll share my perspective. First, yes, the fear mongering on this and many subreddit is ridiculous. If you want to be an analyst, understand good reports and trust data. DOL is a reliable source and usually has a good handle on these projections. I personally believe with AI, analyst positions will really take off. The bad with that is the market is kind of shitty now. For two reasons, 1 is the overall economy isn’t great. 2, most of those entry level jobs are FLOODED with apps. A vast majority of them don’t even qualify for the job. Everyone is just desperate to be an analyst right now. I’m in a semi-management position now and possibly getting a new role as a lead and compensated quite well. My ba was in history, and my masters program had some coding/statistical modeling but not a traditional program like you’d think. I also have no certs. I got here because of networking, and taking a role that was adjacent to analytical work, and doing everything I could to work with the analyst and data scientist in the other offices. They liked me, gave me an opportunity. Now I’m here. I’d focus on building some technical skills like sql, a visualization like tableau, and basic modeling like regression analysis. Do side projects that interest you and keep the work saved for interviews. I would also take jobs that cross paths with data analysis people and try to work with them. It’s easier to get that job if you’re already in the company and like them. Last it’s always good to network. LinkedIn sucks for a lot of things, but it’s an easy way to meet people in roles or companies you’d want to be in. Don’t be afraid to add them and try and meet with them virtually. Some won’t respond, but people tend to like to help others

reddit.com › r/datascience › is data science oversaturated now? | job market

r/datascience on Reddit: Is data science oversaturated now? | Job Market

July 12, 2023 -

Whenever I've scrolled through Linkdin, I'm seeing heinous ratios like 60-200 applicants: 1 opening. I mean I just started my DataCamp tracks last September! Am I looking in the wrong places or am I just fucked?

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If your only technical credentials/experience/education are free online courses, you will have a tough time getting an interview

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The field is flooded with people like yourself who think they can break into data science with some online certifications. I understand why everyone is interested in DS but it's going to be a hard road without any formal education or experience.

reddit.com › r › DataScienceJobs › comments › 1ohu39s › is_the_data_science_job_market_real_right_now

Is the Data Science Job Market Real Right Now? Feeling ...

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reddit.com › r › analytics › comments › 1rnp2sb › should_i_job_hop

Should I job hop : r/analytics

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reddit.com › r/careerguidance › should i finish a master's degree in data science or in management ?

r/careerguidance on Reddit: Should I finish a master's degree in data science or in management ?

March 7, 2026 -

I'm about to finish my bachelor's degree in software engineering and I'm thinking about applying for a master's. With the rise of AI, it feels like a lot of jobs are going to change and the market will shift significantly.

I'm really unsure which master's to pursue: AI, data science, cybersecurity, or bioinformatics. At the same time, the tech job market seems increasingly competitive and crowded, and it feels like opportunities are shrinking.

This has made me question whether I should continue in tech or switch to something like a master's in management. I’m not sure how much AI will affect that field, but I assume it will still require human decision-making.

I’d really appreciate any opinions or advice that could help me make a clearer decision.

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ai degrees are already kinda overcrowded and a generic management masters doesnt really help much either unless its top tier and with experience already data / ai stuff is closer to your cs background so cheaper risk overall but hiring is rough everywhere right now, tech or not, getting any decent job is just hard now

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Neither, masters right out of undergrad is the biggest waste of money. Just more expensive entry level people, zero mastery of anything. Go enter the workforce. If in 5 years you like whatever you do, go get a relevant grad degree.

reddit.com › r/careerguidance › should i stay with computer science or switch to data science for better job prospects?

r/careerguidance on Reddit: Should I stay with Computer Science or switch to Data Science for better job prospects?

November 2, 2025 -

Hey everyone,

I’m 17 and just started my first semester after finishing my associate’s degree early. I’m currently taking CS1 and Computer Architecture (CDA) as I work toward a bachelor’s in Computer Science.

Recently, I’ve been noticing something that’s honestly making me nervous — several people I know (friends and family) have graduated with CS degrees, worked hard to get internships, and still aren’t finding jobs afterward. It also feels like the number of internship opportunities has gone down compared to a few years ago.

I’m wondering if switching to a Data Science degree might give me a better shot at getting an internship and a job after graduation. Would it be easier to find work in that field, or are things just as competitive there too?

I really enjoy both programming and math/statistics, but I want to make the most practical choice for the long run. For anyone who’s been through either program or is already in the industry — what would you recommend? Is CS still the best route, or is Data Science a safer bet for the future?

Thanks in advance for any guidance!

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Data Science does not have better job prospects. At the undergrad level, you’re very unlikely to land DS roles. You’re only looking at Data Analytics. The DS positions open up once your start a graduate program. Stick to Computer Science, do some independent learning in other areas of interested (ie., Data Science), and you’ll be able to land roles in both spaces much easier than if you did a Data Science degree.

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I'm an Analytics and Data Systems manager and to be honest its very hard to say what future data jobs will be after AI. But its not looking great. AI is getting more integrated with all of our workflows and getting more advanced quickly. AI can do alot of the backend engineering, analysis, modeling and visualizations that we historically needed skilled workers to do. Can it replace everyone now? No, but I can see it happening with time. If I were to guess we will still need some people to help manage and analyze data but way less than before and much more of thier job will be human interaction, overall strategy, project management focused and not as much technical skill focused. If I were young now I would focus on professional skills that are human focused and/or legally cannot be replaced by a computer. Or I would focus on the new AI centric jobs that will be coming out as companies change thier work processes.

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March 3, 2026 - “During an energetic and candid conversation with alumnus Fredrick Cho, I found myself inspired to declare a second concentration in Applied Data Science and deepen my technical skills,” she said.

reddit.com › r/datascience › the data science job market is disappearing

r/datascience on Reddit: The Data Science Job Market is Disappearing

November 28, 2022 - So now with a shift from growth to profit, we can clearly see that companies stop investing that aggressively. That means the data science job market is disappearing, BUT only in high growth (high burn) environments.