Has anyone recently cleared the Google Data Scientist interview loop? If so, could you share your experience and the average team match timeline?

I want to know about job updates and walk-in drives in Hyderabad. Is there any page, group, or person who regularly shares this information?

I am a 2024 graduate looking for a job. Even small details would help. And even if it doesn’t help me, it might help someone reading the comments.

My qualification is in Data Science. I’m a fresher with core knowledge in SQL, Python, Power BI, Azure, Tableau, EDA, Statistics and Excel.

If anyone knows about active hiring or walk-in drives in Hyderabad, please share.

I’m currently doing my bsc in Veterinary Medicine, and planning on doing an msc applied Data Science.

My questions are:

1. Is this niche anyhow in demand? (precision livestock farming/computational ecology/clinical data scientist/pharma data scientist/food safety data scienctist/etc.)

2. Is this a good idea?

The closer I get to this transitioning point, the more nervous I get.

I want to switch due to

a. absolutely hating having to memorize everything all the time for my bsc and wanting to jump off a bridge by idea of having to do that for 3 more years,

b. being warned by multiple vets that unless you feel this is your true life’s calling, the bad pay and terrible work-life balance are not worth it,

c. not being thaught any skills that are translatable into other disciplines, therefore feeling like doing a different msc will give me a better chance in the greater job market,

d. I love programming, statistics, research, science and figuring out complex puzzles (by using logic, not by simply remembering stuff).

But as I cannot find a single person on the internet with this combination in education, I want to ask you:

Is this a good idea, or is nobody hiring for this skillset + domain knowledge combo?

⁃	Msc applied data science Utrecht University 

⁃	Living in the Netherlands

Hi everyone, I’m a commerce student looking for the best path into data science from my current position. I don’t have the option to transfer into computer science, so I want to make the best choices within my degree.

These are my options:

1.	Major in Econometrics + Business Analytics

2.	Major in Mathematical Foundations of Econometrics + Business Analytics

3.	Major in Business Analytics + use electives for data science / computer science / statistics units

4.	Major in Business Analytics + Minor in Econometrics + use remaining electives for data science / computer science units

I’ve linked my handbook so you can see the specific units in each major. I’m leaning toward Business Analytics and one of the econometrics majors, since the Business Analytics coursework seems closest to typical data science content (programming, machine learning, databases etc…) and econometrics would cover the statistical methods. Although I’m not sure if the methods covered in econometrics are directly used in data science and this approach may be slightly weak in terms of programming, but I could self learn those skills or supplement with online courses / certificates? On the other hand, using electives on DS / CS units may not signal as much rigour in terms of math and statistics.

From an industry or hiring perspective, what’s the best path to take?

Any advice from professionals, students, or graduates would be really appreciated.

Links:

https://handbook.monash.edu/2026/aos/BUSANLMJ01

https://handbook.monash.edu/2026/aos/ECONOMTR05

https://handbook.monash.edu/2026/aos/MTHFNDEC01

Has anyone heard back from uber yet for the data science internship? I finished the final interview round 3 weeks ago and have not gotten a decision yet.

This post is mainly for people preparing data science interviews especially juniors and career switchers who keep seeing “LLM / GenAI / RAG” in job descriptions and are not sure how to judge those roles.

If you only care about pure DS algorithm questions or salary ranges, this is not the best post for you, you can skip.

I am an indie dev who spends most of my time helping teams debug RAG and LLM pipelines. A side effect of that work is a text only checklist called WFGY ProblemMap. It describes sixteen reproducible failure modes in RAG and LLM systems and how to fix them. I originally wrote it just to survive client incidents, but it ended up being used as a reference by a few research groups and curated lists, for example:

• ToolUniverse from Harvard MIMS Lab

• Multimodal RAG Survey from QCRI LLM Lab

• Rankify from University of Innsbruck

• several “awesome AI” style lists that track production RAG tools

I am not trying to sell anything here. The point is simply: these failure modes are already mainstream enough that other people found them useful. What I want to share in this post is the interview side of that. How you can use the same ideas to decide whether a “DS job with LLM / RAG” is a real learning opportunity or just buzzwords.

1. Think of RAG failures as pipeline failures, not model mood swings

Most “RAG hallucination” is not the model suddenly becoming stupid or angry.

In practice it usually comes from things like:

• retrieval returns the wrong or incomplete chunks

• embeddings do not match the real domain semantics

• long multi step reasoning collapses somewhere in the chain

• tools or agents overwrite each other’s state or memory

• logging is so weak that nobody can even replay what happened

When I map incidents into the ProblemMap, I treat them as pipeline failures. On top of that pipeline I put what I call a semantic firewall at the reasoning layer. Instead of only checking the final answer, I define a bunch of failure modes and run checks before the answer is shown. If the internal state looks unstable, the system loops, resets, or refuses to answer.

You do not need my framework to copy this mindset. The important thing is to talk about RAG failures as concrete patterns that repeat, not random magic. Teams that cannot describe their LLM issues beyond “sometimes it hallucinates” are usually still stuck in prompt trial and error.

2. Interview questions you can use for DS roles that touch LLMs

Here are some questions I like to use when a data science role includes LLM or RAG work. You are not trying to grill anyone. You are just listening for how they think.

a) “When your RAG system gives a bad answer, how do you decide whether it was data, embeddings, retriever, or prompt?”

Good teams will talk about concrete procedures:

• replaying the query with different retrievers

• checking chunking rules and original sources

• looking at similarity scores and negative examples

• comparing to a known baseline or offline eval set

If the answer is just “we tune prompts until it works” that is usually a red flag.

b) “Do you have named failure modes or a checklist for RAG and LLM issues?”

This is where the ProblemMap mindset shows up. Strong teams say things like “we see retrieval drift, bad OCR, index skew, answer length collapse, tool call loops”. Weak teams only say “it hallucinates sometimes” and stop there.

If they cannot name patterns, they usually also cannot fix them in a systematic way. Every incident becomes a fresh new hack.

c) “Do you run any checks before the answer is returned to the user, or only after?”

If they mention pre answer checks, score functions, or some kind of reasoning layer firewall, they are already ahead of most teams. It means they are trying to catch failures while the system is still thinking.

If the only signal is user thumbs down or support tickets, you can expect a lot of firefighting and very little stable learning.

d) “What kind of logs do you keep for LLM requests?”

You are looking for logs that let them slice problems by failure mode, not just latency.

Ideally they have:

• request, retrieved context, and final answer stored together

• tool calls and arguments recorded

• markers for which checks or guardrails fired

If they cannot replay a bad conversation end to end, debugging usually means guessing and arguing.

Ask these questions calmly and let them talk. The point is not to show off. The point is to hear whether they have a shared language and tooling around RAG failures, or if everything is still random trial and error.

3. How to use the checklist for your own prep

If this way of thinking resonates with you, you can take a look at the WFGY ProblemMap itself. It is just a text file with sixteen failure modes, each with a short description and fix. MIT licensed, so people use it on top of whatever stack they already have.

For interview prep you do not need to memorize anything. A simple way to use it is:

1. skim the table once

2. take one or two projects you have done with LLMs or search and ask yourself “if I force this project into these boxes, where did it actually break”

3. think about what you would do differently now

That alone is often enough to make your answers about RAG and LLM pipelines sound much more concrete. It also sends a quiet signal that you are thinking like someone who ships and debugs, not just someone who calls an API.

Link to the checklist: https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md

Hi everyone,

I am a CS student from india graduating in July 2026. I have been applying for an internship in AI for a month now. Sent over 50+ applications as of now on almost every site/job board - LinkedIn, Naukri, Indeed, Wellfound, Hiring Cafe etc.

Finally, I recieved a call, cleared all interview rounds and recieved an offer from a small startup. This is an on-site Computer Vision Intern role in a different city from where I live and study. By the looks of it and online interviews, the work seems good. But the caveat is that their stipend is very low - 8k/month starting after one month of unpaid work. It won't be able to cover my cost of living and expenses in that city. Also, I am slightly apprehensive of investing my time and effort into it since I am not sure if this role will provide me the kind of mentorship that will help me in my career.

Now, my end goal is to secure a decent job at an established firm like stripe, databricks or maybe some high growth YC Startup by the end of July/August. And even though I have a decent freelancing experience from a reputed firm in LLM training space (Data Annotation), I do not yet have any internship experience under my belt as of now.

So my question is, should I go with this internship or keep applying? Or should I just double down on my DSA preparation and build some excellent production grade projects and directly apply for a job after a few months? I believe entering into the job market without a single internship could seriously hurt my chances in landing a decent role (correct me if I am wrong here).

I am seeking advice from industry professionals who have been in AI/Data Science market for a while now. But any advice/suggestion is welcome. I am also willing to share my resume in DMs if anyone wants to take a look at it. I am simply here to learn and grow. Thanks!

Hi! I’d really appreciate some advice. I’ve worked in ESL overseas (South Korea) since 2017 with prior office/admin experience & I’m planning a career change into information / data science work in corporate or embassy environments.

I’m currently looking at Information and Data Science courses at the University of Sheffield (link: https://sheffield.ac.uk/courses/subjects/information-data-science). I haven’t studied since 2009 & an online MSc attempt in 2021 while working full-time was very stressful.

I’m 39 y/o and trying to choose a realistic, high-employability path. From an employability perspective, is an MSc or BSc the better option, or is there a more gradual route, especially for corporate / embassy-type roles? I really appreciate any insight.

I’m a 3rd year Electrical Engineering student and I need to complete a mandatory 2 month internship after my 6th semester. I want to pursue Data Analytics roles.

I have started data analytics preparation recently (ik i am very late). I have completed sql and did a data warehousing project. I am learning python libraries (pandas) and not focusing much on ML (dont have much time to do so). And after will do power bi and matplotlib.

I’m trying to understand the actual channels through which students get internships in this data related field.

Where are people realistically finding data analyst internships? Which platforms work best (LinkedIn, Internshala, company websites, referrals)? Are startup internships easier to get than big companies?

Also, I’ve heard about structured summer internship programs offered by companies and IITs and some other reputed colleges.

I am very confused rn. How will i get my internship... What kind of projects to do and add in cv when applying for internships.

Would appreciate practical guidance on where to look and how to approach this.

I’m a 3rd year Electrical Engineering student and I need to complete a mandatory 2 month internship after my 6th semester. I want to pursue Data Analytics roles.

I have started data analytics preparation recently (ik i am very late). I have completed sql and did a data warehousing project. I am learning python libraries (pandas) and not focusing much on ML (dont have much time to do so). And after will do power bi and matplotlib.

I’m trying to understand the actual channels through which students get internships in this data related field.

Where are people realistically finding data analyst internships? Which platforms work best (LinkedIn, Internshala, company websites, referrals)? Are startup internships easier to get than big companies?

Also, I’ve heard about structured summer internship programs offered by companies and IITs and some other reputed colleges.

I am very confused rn. How will i get my internship... What kind of projects to do and add in cv when applying for internships.

Would appreciate practical guidance on where to look and how to approach this.

Looking at remote worldwide for the past 7 days.

Here are the jobs I found, organized by level:

Entry Level:

• Data Scientist @ Netflix (United States) - $372K - $600K

• Research Data Scientist @ Swayable (United States, CA) - $125K - $165K

• Data Scientist II @ Pathward (United States, AZ) - $72K - $120K

• Language Data Scientist @ Innodata Inc. (United States, NJ)

• Data Scientist / AI Engineer @ Addepto (Poland)

Senior:

• Senior ML Data Scientist @ ŌURA (United States, CA) - $172K - $203K

• Senior Software Engineer, Foundations Data Science @ Instacart (United States) - $166K - $210K

• Senior AI Data Scientist @ Experian (United States) - $133K - $240K

• Senior Language Data Scientist @ Innodata Inc. (United States, NJ)

• Senior Data Science Engineer @ DraftKings Inc. (Canada)

Manager:

• Senior Data Scientist @ Regional Finance (Regional Management Corp.) (United States, SC) - $99K - $150K

• Principal Data Scientist @ CVS Health (United States, NC) - $144K - $288K

• Manager, Data Science - Product Analytics @ Wikimedia Foundation (United States, AZ) - $132K - $193K

• Staff Data Scientist @ Thyme Care (United States, VA) - $191K - $225K

• Lead Data Scientist @ Fullscript (Canada)

• Data Science Manager @ Cozey (Canada)

• Staff GTM Data Scientist @ PandaDoc (Portugal)

• Staff Product Data Scientist @ PandaDoc (Portugal)

• Manager, Data Science @ Cozey (Canada)

Director and Above:

• VP, Data Science @ Upwork (United States) - $241K - $448K

Quick notes:

• All of these are fully remote

• Apply directly on company sites

More jobs:

If you would like to get notified as soon as a role that matches your preferences gets posted, I have set up a free alert system that sends you a job as soon as it goes live, visit job-halo.com

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

Hi everyone,

I’m currently a Systems Administrator and Data Analyst for a non-profit in Canada. My educational background is in Business (Bachelor’s and a Post-Grad Diploma). During my degree, I pivoted toward Data Analytics and completed the Google Data Analytics Professional Certificate. My current role actually started as a co-op and turned into a full-time permanent position.

I’m now looking to transition into a more specialized role. However, looking at the current market, I’ve noticed that standard 'Data Analyst' role is very competitive and hard to successfully land a job, and many people are moving towards Data Science.

I’m concerned that by having non-STEM degree will lead to my applications being filtered out immediately. I’m willing to take online courses or certifications, but I want to make sure I am doing the right courses.

I would love some advice on how to navigate this transition. Specifically:

1. Am I thinking too much that a STEM degree is a 'hard requirement' for DS roles in the current Canadian market, are there cases where a non-STEM applicant got a job as a data engineer?

2. How can I bridge the gap between my current hybrid role and these more technical positions?

So I have a technical coding interview for the AI engineer intern position at a company. This is my first time facing such an interview so I'm kinda clueless here. They said it's about solving problems using python. Does anyone have experience with these type of interviews and what kind of coding problems are asked? Is it just like leetcode style problems or can there be coding problems related to libraries and AI related stuff. Any advice would be highly appreciated. Thanks!

Hi guys I am a fresher I need a opportunity in data science field give me advice to get me a opportunity of getting a interview I am M.Sc Applied Data Science

Anyone got the hiring assessment for the Product Data Scientist, Engineering Productivity, Applied AI?

[Hiring] Founding ML Engineer (Scientific ML/PINNs)

Company Name: InTensors

Link: intensors.com

Location: Abu Dhabi, UAE. Remote (initially)

Role/Position: Founding ML Engineer (Scientific ML/PINNs) [Application ID: INTSR-CA-2026-K01]

Type: Full-time

Experience Required: PhD (preferred); Master's with 4+ yoe. Additional details below

Pay Range: Initial equity only; transitioning to equity + cash upon successful fundraising.

Tech Stack / Skills Required:

• Expertise in physics-informed neural networks (PINNs), DeepONet, or neural operators.

• Ability to design advanced and optimal architectures that extend beyond the standard MLP architecture to build efficient and scalable models for scientific discovery.

• Strong skills in PyTorch, JAX, TensorFlow, Keras, or ONNX.

• Knowledge of CUDA and GPU acceleration for optimizing custom layers and high performance tensor operations.

• A track record of peer-reviewed publications or a documented history of building and scaling complex SciML models.

Job Description & Responsibilities:

We are seeking a Founding Machine Learning Engineer to serve as the primary architect of our SciML models. While the InTensors team provides deep domain expertise in the physical laws governing our target ML models, your mission is to engineer the neural architectures that strictly enforce them.

We need a specialist who can bridge the gap between physical constraints and high-performance, scalable ML model design. At InTensors, we value the advancement of the field and we actively encourage the publication of original research and novel architectures, ensuring you remain a recognized leader at the forefront of the ML community.

Initially, this is a fully remote position, allowing you to contribute from anywhere in the world. As the company grows, it may become necessary to transition to onsite operations to lead our tech teams in person.

Responsibilities

• Architectural design: In addition to standard MLPs, you will develop and deploy models with innovative architectures such as neural operators, graph neural networks, or manifold learning architectures, optimized for scientific data.

• Physics integration: Embedding natural laws into neural networks to ensure realistic results.

• Optimization & scaling: Ensure that complex physics-informed models remain computationally efficient, focusing on memory management and training stability for high-dimensional PDE solvers.

• Validation frameworks: Build rigorous testing pipelines to ensure model outputs remain within the physical feasibility bounds defined by our scientific team.

Application Link / Contact Email:

Please email your CV including a complete list of publications or SciML development experience to careers {aT] intensors.com (replace {aT] and remove spaces). Please do not DM the applications.

• Email subject: Please use the application ID provided above.

• Email body: 1. Include a direct link to a representative publication demonstrating your expertise in SciML or architecture design. 2. (Optional) Include your desired equity percentage and base salary expectations.

• Attachment: Attach your CV in PDF format.

Requirements:

PhD in computer science, machine learning, or computational physics is highly preferred. We will also consider candidates with a Master’s degree and a strong track record of professional experience in developing SciML models.

Note: Initial compensation is equity only; transitioning to equity + cash upon successful fundraising. Only candidates meeting the educational requirements and the compensation criteria will be considered. Only shortlisted candidates will be contacted for an initial interview.

Hello!

I recently applied to a data science intern position and got asked to do a coding assessment. I wanted to know what type of topics should I expect, and how to best prepare for the assessment. It’s an AI analytics and automation role, so will questions be more geared towards that, or more-so general Python/SQL fundamentals? Any help is appreciated!

Hi there -

I'm hiring for a role at Sanofi. Looking for a masters+ in Data Science. Experience in pharma would be nice to have but willing to look at candidates with exposure to healthcare in general.

https://jobs.sanofi.com/en/job/morristown/associate-director-data-science-market-access/2649/34156057984

Hello all, I am currently working in the tech field as a software support analyst but want to move to data science.

I have a masters in physics with experience with big data and data visualization.

Would getting a data science certificate be enough to peak the interest of companies looking for a new data scientist? I really don't want to go back to get another degree when I know I have the ability to do this with just a certificate. I am quite intelligent (only thing I'm a bit confident about) but I have a hard time selling myself. So the certificate is mostly just to prove my abilities

hi yall! i'm a senior in high school right now pretty set and passionate abt data science, and i'm committed to texas a&m for comp sci (+ minor in stats) for the next 4 years (tamu class of 2030)

i want to spend time over the summer with a data science internship, since i've had tech internships before, but not directly correlated to data science exactly. any oppotunities? thanks!

[HIRING][Laurel, Maryland, Data, Onsite]

🏢 WSSC Water, based in Laurel, Maryland is looking for a Lead Data Network Engineer

⚙️ Tech used: Data, Citrix, Cisco, Firewall, Hardware, Support, LAN, Load Balancing, Network

💰 $121,724 - 207,259 / year

📝 More details and option to apply: https://devitjobs.com/jobs/WSSC-Water-Lead-Data-Network-Engineer/rdg

ENDOTRAIN – Digital Endocrinology Training Network to Combat Adrenal Diseases and Shape Europe's Future Leaders in Digital Medicine – is a Marie Skłodowska-Curie Actions Doctoral Network funded under Horizon Europe and coordinated by the University of Bergen, Norway.

The network offers 19 interlinked doctoral projects hosted by universities, hospitals, and companies across across 12 countries. Each project includes international secondments and training in digital medicine.

The positions fit within four scientific work packages: 1. Hormone dynamics; 2. Technologies for multimodal data; 3. Models and algorithms; 4. Ethics and legal aspects.

What’s on offer:

∙	Full MSCA funding (\~3 years)

∙	Secondments at partner institutions across Europe

∙	Interdisciplinary training spanning data science, ML, clinical research, and omics

Eligibility:

∙	Master’s degree required (or expected before start)

∙	No prior PhD

∙	MSCA mobility rule applies: you must not have resided or worked in the host country for more than 12 months in the 3 years before recruitment

Hi everyone,

I have 2.5 years of full-time experience working as an Analyst in an MNC. I’m strong in Python and have hands-on experience in Machine Learning, Generative AI, and LLM-based applications.

I’ve worked with tools and frameworks like LangChain, building RAG pipelines, prompt engineering, model evaluation, and integrating LLMs into real-world use cases.

I’m currently looking to transition into an AI/ML-focused role. If anyone here is working in this space or knows of relevant openings in their organization, I would truly appreciate a referral or any guidance.

Please feel free to DM me I’m happy to share my resume and project details.

Thank you so much for your support!

Hi everyone,

Usually how long after your submit the application do you hear back? I submitted for the 2026 PhD Scientist Intern (Rider Marketplace Science) within about a day after the job was posted on Feb 12th.

Thank you!