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?

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.

Hey everyone — I'm finishing up my MSc in Data Science this fall (Fall 2025). I also have a BSc in Computer Science and completed 2–3 relevant tech internships.

I’m starting to plan my job hunt and would love to hear from working data scientists or others in the field:

• Should I be applying in bulk to everything I qualify for, or focus on tailoring my resume with ATS keywords?

• Are there other strategies that helped you break into the field?

• What do you wish someone had told you when you were job hunting?

• Is it even heard of fresh graduates landing data roles?

I know the market’s tough right now, so I want to be as strategic as possible. Any advice is appreciated — thanks!

I have seen people in this thread talking about how bad the DS market is right now and how there is an influx of graduates in DS field but there are no jobs to hire them. I would like your insights on the data jobs which still have a decent market now and in the near future.

I'm an AI engineer who oversees hiring at my company. The gap between what candidates show and what gets them hired is honestly depressing.

What job postings say:

• PhD or Master's preferred

• 5+ years ML/DL experience

• Publications a plus

• Expert in PyTorch, TensorFlow, scikit-learn

What actually gets people hired:

• Can you clean messy data without complaining?

• Can you explain your model to someone's VP who doesn't code?

• Can you ship something in production?

• Do you know SQL well enough to not break things?

• Are you pleasant to work with?

IMO, most "data science" jobs are 70% data engineering. The modeling is maybe 20% of the actual work. If you can't wrangle APIs and build pipelines, you're going to struggle.

Kaggle portfolios might hurt you. Hiring managers see "Kaggle competitions" and think "this person optimizes for leaderboards, not business problems." Show me something that solved a real problem, even a tiny one.

The PhD requirement is mostly BS. Companies write "PhD preferred" because they think that's what serious roles need. Then they hire the person who actually shipped something.

Entry-level doesn't really exist anymore. When postings say "3-5 years," they mean it. The "we'll train you" era is over.

What actually works:

• End-to-end projects (problem → data → model → deployed result)

• GitHub with real code, not just notebooks

• Proof you can work with engineers

• Blog posts or anything showing you can explain technical stuff to humans

• Referrals (still 80% of how people actually get jobs)

So, if you're applying to 100+ jobs with no response, it's probably not your skills. It's that you're showing academic credentials when companies need proof you solve business problems.

The market sucks right now. But the people getting hired are the ones who can demonstrate impact, not just knowledge.

Am I wrong? What's your experience? What's actually working for people landing DS roles?

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?

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.

Evry time I search remote data science etc jobs i exclusively seem to get hybrid if anything results back and most of them are 3+ days in office a week.

Do remote data science jobs even still exsist, and if so, is there some in the know place to look that isn't a paid for site or LinkedIn which gives me nothing helpful?

Data science careers often feel like they funnel into the same few paths—FAANG, ML/AI engineering, or analytics leadership—but people actually branch into wildly unexpected directions. I’m curious about those off-the-beaten-path exits: roles in unexpected industries, analytics-adjacent pivots, international moves, or entirely new ventures. Would love to hear some stories.

P.S. Thread inspired from a thread in the consulting subreddit but adapted to DS.

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?

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

Data science is supposedly gonna become more and more of one of the most sought after professions, but for graduates, the job hunt is rough let's be honest. Most entry-level roles still ask for 2–3 years of experience, and even internships are insanely competitive. At the same time, bootcamps, online certs, and university programs are flooding the market with new grads all chasing the same limited pool of junior roles.

The U.S. Bureau of Labor Statistics predicts 35% growth in data science jobs by 2032, but some recent estimates suggest that up to 50% of DS graduates remain unemployed or underemployed months after finishing their programs. And the roles that do exist often require a massive list of skills—cloud, ML, SQL, dashboards, stats, and production-level code—basically expecting a full-stack ML engineer for a junior salary.

The growth is there, but anyone else feel like it's only if you're already in the industry?

I studied Data Science at a massive SEC school and got my degree in May of 2024, and despite getting glowing reviews at my internship, I couldn’t secure a full time role due to budget cuts at the company I interned at.

Fast forward 9 months, 800+ applications, and a part-time job at my local grocery store later, I finally land a role in B2B sales at a Fortune 500.

It didn’t take me very long to figure out that I am not good at sales and that I wouldn’t make it very long. I started this job in May and the fact that I made it this far is a shock to me, because they love to fire due to underperformance. I can only coast for so long. I need to get out before I get put on a PIP, because if I get put on a PIP and I have nothing lined up, I’m cooked.

I’m mainly looking in Houston, but am open to remote/hybrid roles, and am willing to relocate.

I feel defeated because even though I have work experience now, it’s not in anyway related to data science.

I’m kind of ranting on Reddit because networking has been hit or miss for me.

Ask me anything, and I can elaborate.

My top technical skills are in Python & R, but I have experience in Tableau, SQL, ArcGIS, and Java.

EDIT: Fixed for typos

Hey everyone,

I'm constantly hearing news of layoffs and was wondering what areas you think are more secure and how secure do you think your job is?

How worried are you all about layoffs? Are you always looking for jobs just in case?

I quit my job in an unrelated field to pursue my dream and failed. I thought I would make it but I didnt.

This is not a rant. Im looking for advice because I feel pretty lost. I honestly dont feel like going back to my field because I dont have it in me. But I cant stay jobless forever. Im having a mental breakdown accepting I may not get into DS so soon because Ive made so many projections about future me as a data guy. Its not easy to let go of them.

I have been hunting jobs for almost 4 months now. It was after 2 years, that I opened my eyes to the outside world and in the beginning, the world fell apart because I wasn't aware of how much the industry has changed and genAI and LLMs were now mandatory things. Before, I was just limited to using chatGPT as UI.

So, after preparing for so many months it felt as if I was walking in circles and running across here and there without an in-depth understanding of things. I went through around 40+ job posts and studied their requirements, (for a medium seniority DS position). So, I created a plan and then worked on each task one by one. Here, if anyone is interested, you can take a look at the important tools and libraries, that are relevant for the job hunt.

Github, Notion

I am open to your suggestions and edits, Happy preparation!

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?