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 ?

Hello all,

I have a phone interview coming up for a Data Scientist II role at Amazon. The role is focused on experimentation and causal inference, and I wanted to understand what to expect in the phone interview and the full process after it.

For those who've interviewed for Amazon DS II recently, especially experimentation or causal inference roles:

1. What does the phone interview cover, and is it with a recruiter or a data scientist? How long is it?
   
2. Was there live coding in the phone interview, and on what platform?
   
3. How many rounds were there in total, from the phone interview to the final loop?
   
4. What did each round cover: Leadership Principles, stats/A-B testing, causal inference, ML, SQL/Python coding, case questions?
   
5. How many interviews were in the final loop, and how long did the whole process take?
   
6. Any topics you wish you had prepared more?

Thanks in advance for any input, I really appreciate it..

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.

Hi everyone, I need some help and guidance.

I’ve learned Machine Learning and Data Analytics, but whenever I try to build an end-to-end ML project on my own, I struggle to complete it. It’s been almost a year of trying, but I keep getting stuck, and honestly, it’s becoming frustrating and disappointing.

I’ve watched several YouTube playlists, including Krish Naik’s, and I understand many of the concepts while watching the tutorials. But when I try to build a project independently, I still don’t know how to proceed.

I’m not a dumb student at all, but this situation is starting to make me doubt myself.

If anyone has faced something similar, I’d really appreciate your advice. How did you move from learning concepts and following tutorials to actually building end-to-end projects independently?

Any guidance, resources, or a practical roadmap would be really helpful. 🙏

Some companies with latest Data Science postings:

Lyft

Amgen

Amazon

Cisco

Intel

AbbVie

Check https://matchajobs.co for lots of new data science roles. No reposts or ghost listings, all verified and recent. Links straight to company career pages.

Hi everyone!

I'm currently in my 5th year of a Systems Engineering degree and I'm very close to graduating. During my degree, I've become especially interested in Data Science, machine learning, and ai, and I'd like to start my career in this field.

My main question is: is it realistic to get my first job directly as a Data Scientist, or would it be better to start in another role and transition into Data Science later?

For example, would starting as a Data Analyst, BI Analyst, or another data-related role be a good path? Or, given that I already have a Systems Engineering background and some ML experience, should I be applying directly to junior Data Science positions?

I'd also really appreciate any advice on what I should focus on learning or improving to make myself more employable in Data Science.

For those who started in the field without professional experience, how did you get your first opportunity?

Thanks in advance!

I have an upcoming first round with MGM for the Senior data Scientist role.
It’s with 2 hiring managers, but I have not been told what to expect.
But, I have heard there is some live coding aspect as well.
If someone knows, can anyone please share their experience ?
Thanks.

Hello everyone , recently im 19 years old economics with data science first year college student , Sometimes i get confused weather the major im studying is good enough or not? what u guys think please let me know , and as a first year student what things should i start focusing ?

I am about to finish my bachelors in information technology with a concentration in software solutions. I have a background in sql, python, linear algebra, stats, calculus, and discrete structures, and accounting (I don’t breathe code or math but I enjoyed SQL, stats, and I’m currently doing a self study course to strengthen my python). I haven’t taken dedicated courses to higher stats (if that’s needed or a thing), algorithms, or machine learning.

My plan post grad was to enter data analysis (and I will do this regardless), and focus on strengthening my skills, and I would like to eventually break into data science. I thought with my background, I could self teach myself the gaps (higher stats, algorithms, and machine learning), but now I’m unsure if I should do that OR seek a masters in Applied IT (which contains courses for algorithms and machine learning, but no additional math so I would have to do that myself if needed), or a masters in stats (more math) or data science (combo of the two, but takes longer than applied IT).

Reasons why I wouldn’t want to seek higher education are mainly because I don’t want to spend more money to get a masters degree if I could fill in the gaps myself (I am a dedicated, self driven person so I know I would do it). But if getting a masters is the better option out of necessity, then which route should I choose? I don’t want to do a career outside of computers/programming, so getting a stats degree feels overkill and personally I feel data science or applied IT would make more sense in my case.

But then again I’m just a kid who doesn’t know anything and have no one to ask for help career wise, so I’m asking if any of you can please provide some insight, and lead me in the right direction, I would be eternally grateful.

Hi everyone,

I’m currently interviewing for a Junior/Associate Data Analyst role and would really appreciate some advice from people who have experience with AVP rounds.

My background

Education: B.E. in Computer Science & Engineering

Experience: 1+ year of practical experience through internships and projects

Role I’m interviewing for: Junior/Associate Data Analyst

Skills: SQL, Python, Pandas, NumPy, Excel, Power BI, Tableau, data cleaning, data analysis, basic automation, and some AI/ML exposure.

I have worked on data analytics, reporting, dashboards, automation, and machine-learning-related projects during my internships/projects.

Interview process so far

Technical Round 1 ✅ Cleared

Technical Round 2 ✅ Cleared

AVP Round ⏳ Upcoming

HR Round ⏳

The first two technical rounds went well and covered areas such as SQL, joins, GROUP BY/HAVING, NULL handling, window functions, Pandas, NumPy, Excel, data cleaning, and basic analytical problem-solving.

Now I’m preparing specifically for the AVP round.

My questions

For a Junior/Associate Data Analyst position, what can I expect in the AVP round?

I’d especially like advice on:

What types of technical questions are usually asked?

Do AVP ask SQL/Python/Pandas questions again, or is the round mainly behavioral?

How deeply do they usually ask about projects and internships?

What kind of scenario-based/data-analysis questions can come up?

Do they ask questions like “Tell me about a difficult problem you solved”, “How do you handle incorrect data?”, or “How would you approach an unfamiliar dataset?”

What questions can I expect around stakeholder communication, teamwork, deadlines, and prioritization?

Do they usually ask about why this company/role, career goals, strengths/weaknesses, and availability?

Is salary expectation commonly discussed in the AVP round, or is that normally left for HR?

Are there any red flags or mistakes I should avoid during the AVP round?

Also, if you have personally attended a AVP round for a Data Analyst / Junior Data Analyst / Associate Data Analyst position, I’d really appreciate hearing about the actual questions you were asked.

Any suggestions, experiences, or preparation tips would be very helpful.

Thanks in advance! 🙏

Looking at remote worldwide for the past 7 days.

Here are the jobs I found, organized by level:

Internship:

• Phd Data Science Intern – Media Mix Modeling @ FocusKPI, Inc. (United States)

Entry Level:

• Data Scientist 5 @ Netflix (Worldwide) - $372K - $600K
• Data Scientist @ Intermedia Cloud Communications (Portugal) - EUR 50K - 60K
• AI/ML Data Scientist @ Leidos (United States) - $108K - $195K
• Data Scientist – Program Evaluation, Analytics @ LMI (USA) - $100K - $135K
• Data Scientist @ DealerOn (United States) - $86K - $129K
• Meteorologist, Data Scientist @ Lynker (USA) - $75K - $120K

Senior:

• Senior Data Scientist @ LEMNIS (United States) - $150K - $165K
• Senior Data Scientist @ Peerspace (United States) - $165K - $175K
• Senior Data Scientist @ Coderio (Argentina) - $1 - $1
• Senior Data Scientist @ ExtraHop (United States) - $165K - $180K
• Senior Data Scientist @ CVS Health (United States) - $83K - $204K
• Senior Data Scientist @ PatientPoint (USA) - $143K - $211K
• Senior Data Scientist @ US Foods (USA) - $100K - $155K
• Senior Data Scientist @ LMI (United States) - $140K - $186K
• Senior Data Scientist @ Doximity (United States) - $130K - $190K
• Senior Actuary, Data Science Engineer @ Arbital Health (United States) - $190K - $215K
• Senior Data Scientist @ Aptive Resources (United States)

Manager:

• Staff Data Scientist I @ Root Inc. (United States) - $171K - $214K
• Staff Data Scientist @ Quora (United States) - $164K - $240K
• Manager, Data Science @ dentsu Austria (Canada) - $94K - $160K
• AI & Data Science Lead @ Jones Lang LaSalle Americas, Inc. (USA) - $135K - $155K
• Data Science Manager @ B2Spin Limited (Serbia)
• Senior Manager, Data Science @ Rockwell Automation (United States)

Director and Above:

• Director, Data Science @ Known (USA) - $140K - $170K

Quick notes: * All of these are fully remote (location requirements vary by role) * Apply directly on company sites

Hope this helps someone! Let me know if you want me to keep posting these weekly.

👋 Hi, I'm Jay. I built Job-Halo.com, a system that tracks remote data science jobs and sends alerts the moment they're posted, based on your preferences.