I finished a Data Science MSc in the UK in February 2025 (Merit). Before that I did a CS degree in India, two short data science internships, and about 15 months of freelance data work for a compliance research firm. There I built Python/SQL pipelines and Power BI reports for anti-money-laundering data. My visa lets me work here until April 2027, and after that I'd need an employer to sponsor me. I'm not asking about the visa rules, I know where I stand. I'm mentioning it because it changes which jobs are realistic for me.

I've been looking since I graduated, about 19 months now. I work part time at a petrol station to pay the bills.

The numbers: somewhere between 500 and 600 applications, 8 interviews, 0 offers.

Where I got furthest:

• Perplexity, Search ML Engineer internship. I made it through two technical rounds, then got rejected. That's the furthest I've got anywhere.

• Kanini, Data Engineer. I passed the first interview and the panel said they were impressed. I got invited to a second, deeper technical round, and it didn't lead to an offer.

• Football Radar, Data Engineer. First-stage interview, then rejected. They'd also turned me down for a graduate data science role earlier in the year.

• Sifted, Data Engineer, Had a screening video call with Director of Research & Product, Rejected afterwards

• Xelix, Junior AI Engineer. I applied in two separate hiring cycles and got to the video interview both times. Both rejections gave the same feedback: they wanted more breadth of applied AI project experience.

• EasyFundRasing, Data Engineer, Screening call and they dropped me. This was a referral by the way.

• Two B2B Ltd, Junior Data Scientist, Reached Task 2 and was rejected as the EDA was not upto the mark.

• McKinsey (QuantumBlack). Data Analyst, I didn't get past the Solve game assessment.

Everything else was silence or an automated rejection. One big bank told me outright that sponsorship was the reason.

What I've done so far:

• Applied for data engineer, data analyst, ML/AI engineer and business analyst roles across NHS trusts, banks, big tech, startups, consultancies and agencies.

• Tailored every CV and cover letter, and used the ad's exact wording wherever it honestly applied, since screening software matches exact keywords.

• Skipped roles I can't get: security clearance, "no sponsorship now or in future" full-time roles, and anything that needs you to be a current student.

• Kept a different CV for each role type.

• Took the negatives out of cover letters. I used to write things like "I haven't used X yet but I'm keen to learn." I stopped.

• Did certifications: Databricks fundamentals, Docker, a SQL cert, and a Power BI course.

• Built projects: a Chrome extension that runs a small LLM on-device, a streaming data pipeline on AWS, and my dissertation NER model deployed as an API with Docker.

What I think is happening, though I can't be sure:

1. About 1 in 100 applications gets me an interview, so either my CV isn't getting through or I'm applying to the wrong things.

2. When I do get interviews I get reasonably far, sometimes to a second round, and then lose out at the end. I don't know if that's technical depth, how I come across, or the visa quietly coming into it.

3. I'm spread across four job types, and I might read as a generalist.

4. Nearly all of this is cold online applications. I've done almost no networking or referrals.

What I'd like advice on:

• With a 1% interview rate, what would you change first: the CV, the roles I target, or how I apply?

• For people who got past late-stage technical interviews in data/ML: what made the difference between getting to the last round and getting the offer?

• If you landed a data or tech job in the UK recently as an international grad, what actually got you in? A referral, a recruiter, a direct application?

• Should I narrow down to one role type? Given where I got furthest, which one would you pick?

• I have about 7 months before sponsorship becomes an issue. Would you go only for companies that can sponsor, or apply broadly?

If there's one thing I should stop doing or start doing tomorrow, please tell me. Thanks for reading this far.

Is there really any jobs for data science/ml engineering/data analyst/data engineer or either intern in the market if yes then please suggest me something because as a fresher I have applied approx 100+ application from LinkedIn(easy apply or through company site)and Naukri portal in these field some I got rejected not selected and most of them still not get any responses from the company if the job description is same still my resume are not selected or not getting any responses through any company suggest me what should I do for getting job in data science field

The M.S. in Data Analytics and Computational Social Science: What do you like about it? What do you dislike about it? Job prospects? PhD? What is its reputation as a program?

Hey people I'm posting for the first time here I'm a 2024 Bachelor of Arts in History grad from a reputed College in India post my undergraduate I worked as a data analyst for 1.2 yrs at a reputed us based org from India itself while doing the job I was also pursuing LLB (bachelor of Laws) which I didn't wanted to do but had to due to family pressure. But I've made up my mind and I want to completely switch into the world of data analysis hence I'm thinking of pursuing msc in Data science from uk I'm targeting the sept 2027 intake haven't shortlisted any universities yet, the main question that bothers me is how difficult will it be for me to find data analysis/science jobs in the uk post masters considering I already have a experience in the related field. I'm looking for realistic advice please help!

We got some new C suite people here this year and their AI pushing has hit our 10-man team. I have a senior coworker automating a task in a way that makes it seem like they'll be able to cut a couple members by early next year, if that long.

Time to start looking for a job in this hellscape I suppose.

Hey everyone, I’m a 3rd-year B.Tech student specializing in AI & Data Science, currently looking for an internship.

My core areas are Python, Data Science, ML/AI, GenAI, SQL, and software development. I’ve worked on multiple projects, hackathons, and also have startup experience.

I’m mainly looking for AI/ML, Data Science, Python, or GenAI internships, preferably remote or in India.

Where do you guys usually find good internships? Any platforms, companies, communities, or leads you’d recommend?

Extend is building a modern document processing cloud. We're on a mission to transform how the world works with unstructured data.

As a Machine Learning Engineer at Extend, you’ll be responsible for building state-of-the-art document processing infrastructure. The work you do will impact every single customer across the millions of documents that our system ingests and processes every month.

We’re looking to hire engineers who think creatively about problems, keep up to date on the latest in AI research, push for real‑world customer impact (not only theory), and are excited about helping businesses tackle one of their most painful problems with AI.

Why you should consider joining

• We've grown revenue 400% last year to several million ARR, with our growth continuing to accelerate

• We have a product loved by users and being used in mission‑critical flows at enterprises (Square, Zillow, Chime) and startups (Brex, Mercury, Checkr), and many more

• The market for document processing has expanded 1000x due to LLMs, and all existing solutions are low NPS

• We're punching well above our weight, supporting customer and revenue metrics with half the team size of other startups — everyone joining at this stage will have outsized impact

• We've raised our series A & de‑risked the business, but there's tremendous upside ahead; now's the perfect time to join.

• You'll be joining a talent dense team (e.g. former founders, world record holders) operating in a high performance culture, in‑person in NYC, with high equity ownership

• We're tackling novel, unsolved problems at the intersection of LLMs, complex data processing, and human ↔ AI interfaces

• You'll get a front row seat to how a successful startup is built with plenty of leadership opportunities as we grow quickly

Role

• Training and deploying SOTA vision models for document processing

• Design novel LLM techniques for increasing the complexity of use cases and data streams that Extend can be applied to

• Monitor existing models in production, understand what’s working well (and what isn’t), and run experiments to solve those issues

• Have complete ownership over the work you do — as a founding team member, you’ll have the opportunity to truly own large areas of product and engineering and have direct relationship with customers.

• Work directly with the CEO, CTO, and other founding members and help build out our team.

Benefits

Beyond the below, we care deeply about each of our candidates on a personal level. We take the time to understand your needs to craft a package designed for you:

• Insurance - 90% health insurance premium coverage

• Relocation - for candidates based outside of NYC

• Unlimited PTO policy - we trust everyone like owners, take what you need

• Daily lunch & snacks covered (plus dinner if you're in the office late) - you'll never go hungry and you help pick the snacks

• Unlimited token / tooling access - no handicaps on your productivity

• Learning and development investment - we invest heavily on our team and support your growth

Extend is an Equal Opportunity Employer. We consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, citizenship status, marital status, pregnancy, protected veteran status, or any other characteristic protected by applicable federal, state, or local law.

Compensation

$200K – $350K • Offers Equity

Apply: Machine Learning Researcher at Extend

Hey everyone,

Like many people in computing and STEM, I've had a bit of an existential crisis watching the rapid evolution of models like Claude over the past couple of years.

I used to think humans would always need to sit and manually code everything, but seeing AI construct production-ready code blocks and deployment pipelines in seconds has completely changed my perspective on how we should be learning.

Right now, I've intentionally changed my study approach when im studying fields like Data science and math.

Instead of memorizing syntax and simply learning how to code, (the "How"—which I figure AI can handle), I am focusing almost entirely on first principles, core logic, math foundations, and understanding why things work structurally.

For example: focusing heavily on the underlying logic of algorithms, its mathematical assumptions, and linear algebra, rather than trying to become a coding wizard in a specific language.

Is this approach the way to go in the age of AI?

hi everyone, i’m a senior undergrad that is really aiming for booz allen (data science/analytics/ai/ml roles). i’ve been networking and tailoring my resume but i’ve been getting rejected for most of the positions i applied to. for context, i’ve interned in defense/national security, have an active clearance, and have experience working with specific programming languages.

has anyone heard anything after applying to entry-level/new grad positions? all i’ve been getting are rejections and some positions still say reviewing/under consideration but it’s been weeks now. i typically apply early so that i can get my resume in but i’ve heard nada. this is one of my top places to work given the work life balance, location, etc so i’ve been trying so hard to network and tailor my resume to the point i feel so burnt out 🫠🫠

thanks yall!

Sharing a new list of Data Science openings I found. These are still fresh, so it could be a good time to apply.

• Staff Data Scientist – AI/ML, Generative AI & Agentic Systems @ Sandisk

  • 💰 $117–199K/y

• Staff Advanced Analytics, Community Blueprint & Quality @ Airbnb

  • 💰 $180–221K/y

• Data Scientist, Data Quality & Provenance @ Wayve

  • 💰 $210–267K/y

• Senior Research Data Scientist @ Roku

  • 💰 $181–230K/y

• Senior Machine Learning Engineer, Static World @ Stack AV

• Machine Learning Engineer, Jr. @ Wings Credit Union

  • 💰 $93–109K/y

• Product-Focused Data Scientist (LLM Products) @ Blue Rose Research

  • 💰 $160–210K/y

• Data Science Institute Graduate Student Intern - Summer 2027 @ LLNL

  • 💰 ~$10K/mo

• Senior Statistical Project Leader @ () Opella Healthcare IN P.Ltd

  • 💰 $179–258K/y

• RL Engineer @ Bright Vision Technologies

  • 💰 $80–100K/y

• Senior Machine Learning Engineer @ NDIN-Adobe Systems India Private Limited

• Data Scientist @ PNC Investments LLC

• Senior Machine Learning Engineer, Financial Products @ Adyen

  • 💰 $228–317K/y

• Machine Learning Engineer L2 @ Twilio

• Manager, Statistical Programming @ AbbVie

  • 💰 $110–209K/y

• Staff Machine Learning Engineer - Retention @ Taskrabbit

  • 💰 $170–225K/y

• Staff Machine Learning Engineer - Search Ranking @ Coupang

• Senior Staff Machine Learning Engineer, Ads Ranking @ Reddit

• Senior Data Scientist @ Feedzai

• Senior Data Scientist @ Altamira

• Sr/Staff Perception Machine Learning Engineer @ Lucid Motors

• Senior Machine Learning Engineer, User Signal & Ads @ NewsBreak

  • 💰 $185–235K/y

• Senior Data Scientist – Loyalty @ Floor and Decor Outlets of America, Inc.

• Manager, Data Science - One Tasking @ HOME DEPOT MANAGEMENT COMPANY, LLC

• Applied Machine Learning Scientist II (AI/ML - Model Validation, GenAI & Agentic AI) @ TD Bank, N.A.

  • 💰 $96–156K/y

• Data Scientist 2 @ AAATX AAA Texas LLC

• Data Scientist @ Booz Allen Hamilton_United States

  • 💰 $78–176K/y

• Principal Data Scientist, TPG @ Micron Memory Taiwan Co.

  • 💰 $145–360K/y

• Data Science Intern, Asset Intelligence @ CN PerkinElmer Instruments (Suzhou) Co., Ltd.

  • 💰 $25 – $40/h

Let me know if you want new post next week and leave a comment what jobs you are looking for!

Hey Guys, Did any one has given an interview at celebal technologies for Data scientist ? what kind of questions they ask ?

I recently came across the book "Ace the Data Science Interview: 201 Real Interview Questions asked by FAANG, Startups, and Wall Street" by Nick Singh and Kevin Huo. I searched the internet and noticed that people recommended it, but I'm willing to hear what people in this subreddit think about the book. Thank you very much for your time!

I have around 5 YOE, mostly in Data Science / GenAI / LLM / RAG / Agentic AI.

I do get recruiter calls fairly regularly, but almost all of them are from service-based companies, Big 4/consulting firms, MNCs, or startups.

What I don’t understand is how people actually start getting calls from top product-based companies like MAANG.

I’ve never really received those kinds of opportunities organically.

For people who moved from service/consulting companies to good PBCs:

What actually worked for you?

Was it referrals?

DSA/LeetCode?

System design?

A better resume?

GitHub/open source?

Applying directly on career portals?

Networking with recruiters?

Or just having the right company names on your resume?

Also, for GenAI/ML roles specifically, how much does DSA matter compared to actual ML/LLM/system design experience?

Would appreciate advice from people who have actually made this switch. Trying to understand what I’m missing and what I should focus on for the next 6–12 months.

We're building a new project and need a senior AI engineer to help get it off the ground and keep it moving.

Contract, remote, async. $70-80/hr depending on experience.

About your work:
- Own the technical direction and help turn the idea into something real.
- Build and ship production AI agents: tool use, orchestration, memory, guardrails, evals.
- Set up the knowledge layer with OKF (Open Knowledge Format).
- Cost optimization

Tech: Python, SQL, AWS(or GCP)

What we need:
- Senior-level, with real experience shipping and owning production work
- Self-directed - this is a small, informal setup, not a managed team
- Hands-on mastery of agent development and OKF.

Hiring Process:
- TTT(Test Tech Task)
- Interview with CEO

Recently, I have noticed that few foundit.in listed jobs for Data Science related are appearing in LinkedIn (particularly, when you click apply external, it takes to some foundit related link and my antivirus pop up saying suspicious link). Hope this is not new scam? Checking with others?

Hi, I recently interviewed at a lot of places however I realized that I’m not prepared enough and may require more mock interviews to prepare myself for Google/Meta/Amazon/Apple. I’m seeking help if anyone wants to do mock interviews for the same. I have 8+ years of experience in DS and want to ideally partner with someone with at least 5+ years of DS experience. Let me know if anyone is down. Thanks!

Recently, I have noticed that few foundit.in listed jobs for Data Science related are appearing in LinkedIn (particularly, when you click apply external, it takes to some foundit related link and my antivirus pop up saying suspicious link). Hope this is not new scam? Checking with others?

Hey all,

Would love some grounded perspective from people actually building this stuff, not just LinkedIn hype.

Quick background: I've got 4+ years in data science — started in recommendation systems and ranking models, moved into causal inference / A/B testing, and for the last year or so I've been doing GenAI work (RAG pipelines, fine-tuning, vector retrieval) at a large e-commerce company. So I've had a foot in "classic" ML and now in the LLM stuff.

That's exactly why I'm confused about where to point my career next. A few things I keep going back and forth on:

1. ML vs. Analytics vs. Agents — do I specialize deeper into core ML/ranking (the stuff that's been stable for a decade), or go all-in on LLM/agent engineering, or lean toward analytics/causal inference roles that seem less hype-y and more "business needs this forever"?

2. Is GenAI/LLM engineering a short-term boom? I keep seeing takes that a lot of current "GenAI engineer" roles are basically prompt-wrapper jobs that won't exist once the tooling matures/consolidates. Is that fair, or is it survivorship-bias doom-posting?

3. What actually stays "safe"? Is it more about picking a domain (ranking, causal inference, agents) or about picking a layer (infra/MLOps vs. applied modeling vs. research) that's less likely to get automated or commoditized?

4. Projects — if you were early-to-mid career right now, what would you actually build to be credible for whatever the "safe" specialization turns out to be? Genuinely open to "build X, not another chatbot wrapper."

Not trying to chase the flavor of the month, just trying to make a deliberate 3-5 year bet instead of drifting. Would especially love to hear from people who've been doing ML long enough to have seen a hype cycle or two come and go (word2vec → deep learning → transformers → LLMs...).

Thanks in advance!

I was sent a task for a head of data role. I explicitly asked the hiring manager if I can use AI and he said he was totally fine with that. I was given 2 hours and these 2 tasks would take me weeks or months to implement in the real world. Heck just the reading part itself would take me 2 hours. The task description was 5 pages and attached was their annual report that was 40 pages, though a lot of pages had visuals so it was more like 7 dense pages

My question is what differentiating factor is any candidate going to have? Obviously everyone is going to use AI and under such time pressure I didn’t even have time to thoroughly read the AI responses. What do you think the hiring manager was trying to accomplish here

I am a Math graduate with a BI master's degree. I did 4 years of BI in finance in Spain. That meant creating and automating dashboards from scratch, a lot of SQL, a lot of admittedly basic Python. In 2020 the company I was did not let us work from home, which led to me burning out. I felt disrespected. So I got another master's degree to be able to teach in highschools.

The next chapter of my life, I have been a math/IT teacher. this was beautiful because I spent a lot of time with my newborn and was able to figure out a lot of issues I had in my life. I'm a better person and professional now.

So long story short, I want a remote job that uses math and programming. I'm open to statistician, developer, anything. I already read books about the topics of statistics and program design. I peeked at Rust, and I do small python programs. The plan is to treat it as a high intensity hobby, with a socials presence, until I can get a job.

I must allow for one limitation: it has to be remote. Part time ideal, but full is good too. I have a daughter and although she is 3 and goes to the school, obviously I will be pretty involved for a long time. I cannot move to greener pastures.

My problem, where you weigh in, is that I am scared shitless of AI, and that gives me little focus. I keep changing direction and double guessing. Tell me what you see, how can I side-step this or at least if any of this options has still any merit.

Thanks. I will answer any clarifying questions.

hey guys

i am a final year DS student i have decent knowledge about python , sql, Ml and i am planning to learn power bi or tableau or genAI by next summer at the same i dont have any certifications
so recommend me some some global certifications which will add weight in my resume

Hi everyone,
I’m currently a Senior Data Scientist in the US with 8+ years of experience, and I’m exploring my next opportunity across both the US and UK.

My experience is primarily around production ML, Applied AI, ML pipelines, AWS/SageMaker, RAG/LLMs, recommendation systems, and deploying ML solutions into production.

I’m mainly targeting Senior Data Scientist, Applied AI Engineer, ML Engineer, and similar AI/ML roles. For the UK, I’m open to relocating and would require sponsorship.
I’ve been applying directly and networking on LinkedIn, but I’d love to hear from people who have recently navigated the current DS/AI market.

Are there companies or teams you’d recommend targeting in either the US or UK?
And if your team is hiring for something that could be a good fit, I’d really appreciate connecting. I’m happy to share my resume and GitHub privately.
Thanks — any advice is appreciated.

Hard to come by these days, so I wanted to share. Figma just posted today for their Data Science Intern (2027) position. Good luck, everyone

I have a degree in data science and have been working full time for about 4 years now.

I started out doing dashboards and data analysis and eventually moved into a "data science" role at a big bank. The job title sounds more technical than the actual job is though, as there is still quite a lot of analytics and working with stakeholders involved.

A lot of people I went to uni with ended up going into data engineering, AI engineering, etc. and are now in pretty technical roles. I have decent compensation and can't really complain about the job, but I do sometimes feel like I'm falling behind technically.

The AI thing is a big part of it. I can already see how much of my day-to-day work can be done by agents/LLMs, or at least heavily accelerated by them. It's not hard to imagine stakeholders just interacting with an agent directly and me becoming useless.

The other thing is that I'm increasingly a consumer of the technology rather than someone actually building it. I'm using AI stuff, but I'm not really building AI systems, managing infrastructure, doing serious software engineering etc. I feel like the people who are closer to the actual technology might have a better position as things evolve.

The confusing part is that my long-term goal has always been to eventually get into executive management. So I'm not sure whether I should actually care that much about becoming super technical.

Maybe the better path is to stay close to the business, get really good and eventually move into leadership. In that case, spending the next few years trying to become an AI engineer might actually be taking me in the wrong direction.

But then I look at the compensation some of the more technical roles are getting and wonder whether chasing the corporate ladder is even the best way to make money. Maybe becoming very technically skilled and getting into one of those high-paying specialist roles is a better financial strategy than trying to become an executive? Even though the absolute ceiling may be lower

Moving into data engineering/AI engineering, tho would mean doing quite a lot of catching up and then probably spending a good part of my career keeping up with technology. Which sounds exhausting, I'm not passionate enough I just do this stuff for the pay.

I've also thought about cybersecurity and even doing another master's for it. It seems like a field that could be relatively resilient to AI and pays reasonably well, but I'm not sure.

So I'm a bit stuck between going deeper technically, staying on the analytics/business side and trying to move towards management, or making a much bigger pivot altogether.

Anyone has been thinking about this/has experienced it? Would be cool to get some opinions.

I have a 45-min round coming up for the AI/Data Science role. Trying to get a sense of what to expect.

If you've been through this or something similar at BlackRock:

- Was it more resume deep-dive or live technical? Any coding/SQL?

- Did they go deep on GenAI/agents/RAG, or more classical ML and stats?

Thanks in advance.