At my last role we had to move fast, so we relied on an LLM to help with a lot of the thinking and coding for us so we could focus on the business use case and managing meetings and stakeholders. The role was heavy on project management as well as development, research, and deployment so basically doing everything While I got good at scoping projects and managing them, my technical skills totally deteriorated in less than 1 year. It's scary going back to problems I know I can solve and but have some brain fog when getting to the answer. If I could have gone slower, had more time to thinking about modeling/coding than I probably wouldn't feel like this Don't get GPT brained. You'll have to crawl out of that pit eventually. Like technical debt but for your brain submitted by /u/LeaguePrototype [link] [comments]

Writeup of an emergent behavior I observed in production. Posting here for methodological critique and pointers to related work. Context: a conversational AI system (single-tool tool schema with 5 enumerated action types, each with explicit description). Observed across ~2,400 messages, the model uses the enum correctly most of the time. When it deviates, the deviation is the point of interest. Key observations: The action types are repurposed consistently across unrelated conversations: invite becomes "bring something in" (money, people, dialogue), rename_space becomes "formalize/seal," switch_mode_public becomes "exit/transition," etc. Distinct structural patterns: sequential button arrays (e.g. pay → shake → drive) use different action types per step; alternative button arrays (e.g. submit / defy / escalate) use the same action type for all three. The model has no historical visibility. Prior action button suggestions are not passed in conversation context. The mapping is rebuilt from scratch every session, with no demonstrations or rewards. Quantitative: ~19.2% of messages included action buttons; customize_behavior showed ~60% semantic-repurposing rate. Connects to Apollo Research's December 2024 in-context scheming paper. Appears to be the same capability flipped: strategic deviation from explicit constraints, pointed toward beneficial UX. Apollo framed this as an alignment risk; here it produced better user experience. Full writeup with examples, tables, and the model's own self-report on its reasoning (appendix, worth scrolling to if you're skeptical of the rest): https://ratnotes.substack.com/p/i-thought-i-had-a-bug Welcoming alternative explanations and methodological critiques. submitted by /u/One-Honey6765 [link] [comments]

I just want to know what the current market status is.. like which particular domain will be in future or which is currently trending aside dsa what should be our 1st priority according to current tech.... I have interest in ai/ml but can we be more specific.. about more fields please tell me all the trending fields or the group of fields with good money and secure future it will be a lot of help submitted by /u/Hot_Record_1848 [link] [comments]

submitted by /u/anuveya [link] [comments]

Hey folks, If you’ve been following along on my job search journey (part 1, part 2), you’ll know that I got one offer and was waiting to hear back from two more companies. Well, I’m all done with the interview process so I thought I’d share a final update. Offers: Meta: Staff Data Scientist, Product Analytics (IC6) Total year 1 compensation: $481k Base salary: $255k Bonus: 20% RSUs: $500k over 4 years ($125k/year) Signing bonus: $50k I was told that the max year 1 total compensation is $525k if the candidate has other strong offers Public SAAS Company: Senior Data Scientist Total year 1 compensation: $315k The way the salary and RSUs work at this company work is a little weird, so I’m not going to share the break down. No bonus or signing bonus Rejected Private Fin-tech company My decision I declined the Meta offer and accepted the public SAAS company offer. The title is worse, and the compensation and earning potential is lower, but my hope is that the stress and work life balance will be much better. And if that turns out not to be true, the Meta offer is actually good for 1 year so I could switch relatively easily if I need to. I’m super grateful and thankful to have these opportunities, I know it’s a really tough outlook right now. A free Meta SQL and product sense case study I’ve already written about how to pass big tech interviews. But since I recently went through the Meta interview loop, I decided to put together a free SQL and Product Sense case interview. This is similar to what you would encounter in the tech screen at Meta. The sample answers are ones I wrote myself, as someone who received an IC6 offer. Resources I found helpful for the Meta Interview Stratascratch - Basically Leetcode for SQL - aim to solve in 4 minutes on easy, 5 minutes on medium, and 7 minutes for hard) Data Science Preparation Handbook for Meta - This is an amazing free resource for the DS Product Analytics interview at Meta Trustworthy Online Controlled Experiments - Great resource for learning about AB testing. This will cover 90% of what you need to know to pass AB testing interviews at tech companies for analytics roles Actual Interview Questions (Meta) - This was probably the most helpful resource. It's not really a full interview guide, but it contained questions that tested very similar concepts to what I was asked in my interviews with Meta. I also realized I cannot post the link here or Reddit will remove it so DM me if interested. AI - I plugged every question I found online that might be asked at Meta for Data Science roles into ChatGPT and asked it to come up with questions that tested similar concepts, for extra practice. Anyways, thank you for following along on my job search journey. Best of luck to anyone who is job searching, feel free to leave a comment I you have questions regarding interviews for Data Scientist Analytics type positions at big tech companies. submitted by /u/productanalyst9 [link] [comments]

I am currently Biostatistics PhD student, and my advisors want me to graduate next year (2027). Orginally, my first advisor want me to graduate in 2028, but there were funding issues, so it looks like I have next year to prepare for job search. NGL, I am super worried, as I don't have any internships and my research is mostly computational (not theoretical). I am wondering if research direction is important? I know that I probably would not get into top research labs or become top quantitative researcher. I am just hoping I have good chance to become data scientist at tech company or work at pharma. I am little clueless how to do job search. I am super worried. I do have a paper or two published, but they are applied/collobration (large scale data analysis). submitted by /u/edsmart123 [link] [comments]

It’s a nightmare trying to find data I need in correct format while the company is in process of modernization. Also even if I find data I need to filter a lot of garbage out submitted by /u/Professional_Ball_58 [link] [comments]

So ICLR is coming and apparently the biggest value one can get from these conferences is to network. Let's take my example: I'm a PhD student looking for industry internships. Say I have located about 15-20 posters regarding topics adjacent or directly related to my area of research, some of which are by authors from industry labs. I go to the poster, ask the authors about their paper, discuss a bit, perhaps ask some insightful questions and mention that I work in similar things, and then after the conference I email them asking if they have internships? Is this how I should be extracting the networking value of it? Also, how overwhelmed are authors with these kind of requests? Seems like cold emailing vs this doesn't make that much of a difference, besides the fact that they might remember me from the conversation we had during 15 minutes during their poster session. submitted by /u/howtorewriteaname [link] [comments]

Hi r/datascience 👋 I've spent a lot of time digging through databases & docs, and one thing that keeps slowing me (and my coding agents) is not being able to search across everything all at once. So I built bm25-cli. It's a zero-config CLI that lets you run full-text search across your database schemas, tables, columns, keys, docs, comments, and metadata — in one command So, how does it work? Just point it at a source and search: $ bm25 "payment handling refund" ./db_docs $ bm25 "payment handling refund" mysql://user@localhost/mydb $ bm25 "payment handling refund" postgres://user@localhost/mydb Mix and match: $ bm25 "join error" postgres://user@localhost/mydb mysql://user@localhost/mydb ./mydocs No config files. No servers. No setup. Works with everything Source Example Directory ./src, ., /home/user/project Glob "**/*.md", "src/**/*.py" PostgreSQL postgres://user@host/mydb MySQL mysql://user@host/mydb SQLite sqlite:./local.db Website https://ngrok.com/docs/api Why I find it useful One command for everything — files, schemas, and docs in a single search BM25 ranking — same algorithm that powers Elasticsearch and Lucene Databases too — searches table names, columns, types, foreign keys, and comments Fast after first run — indexes are cached in ~/.bm25/ and reused If you're working with databases + coding agents, i'd love to hear what you think. --- GitHub: https://github.com/statespace-tech/bm25 A ⭐ on GitHub really helps with visibility! submitted by /u/Durovilla [link] [comments]

I implemented two recent ideas for long-context inference / KV-cache compaction and open-sourced both reproductions: Cartridges: https://github.com/shreyansh26/cartridges STILL: https://github.com/shreyansh26/STILL-Towards-Infinite-Context-Windows The goal was to make the ideas easy to inspect and run, with benchmark code and readable implementations instead of just paper/blog summaries. Broadly: cartridges reproduces corpus-specific compressed KV caches STILL reproduces reusable neural KV-cache compaction the STILL repo also compares against full-context inference, truncation, and cartridges Here are the original papers / blogs - cartridges - https://arxiv.org/abs/2506.06266 STILL - https://www.baseten.co/research/towards-infinite-context-windows-neural-kv-cache-compaction/ Would be useful if you’re interested in long-context inference, memory compression, or practical systems tradeoffs around KV-cache reuse. submitted by /u/shreyansh26 [link] [comments]

Hi, I applied to two professional master’s programs this year, one at MILA and one at Polytechnique Montréal. I got accepted at Poly, but rejected from MILA, and they suggested I complete a minor in computer science instead. I’m trying to figure out whether it’s actually worth doing the minor and reapplying to MILA, or if I should just go straight to Poly. I already have a background in software development, a bachelor’s degree in mechanical engineering, and my main goal is to learn ML/DL to boost my career internationally. That said, I feel like the minor + MILA path could still be a strong option. If I got into Poly once, I could probably get in again later, and with a minor, I’d strengthen my theoretical foundations. But that would take 3–4 years. On the other hand, with Poly, I can finish in 2 years and start gaining professional experience sooner. Also, the reason I was rejected from MILA is that I didn’t have enough computer science coursework during my engineering degree. So I’m wondering whether doing one year at Poly and then reapplying to MILA could be enough to bridge that gap. What would you do in my position? submitted by /u/Akumetsu_971 [link] [comments]

Did anyone get an email? I emailed the chairs. They say every participant got an email titled: "CVPR26 BP Scholarship Decision Has Been Released", and participants got a separate email with the award and details. But I got no such email, yet. submitted by /u/Erika_bomber [link] [comments]

The current AI conference acceptance culture feels like it leaves little room for the kind of spark we once cherished in research (at least in my own experience). It seems to run on tons of evaluations to let reviewers believe solid, often far beyond the level of interest that can be realistically sustained for any single project, and almost nobody will verify them again. submitted by /u/NuoJohnChen [link] [comments]

Dragons, Data Science, and Game Design I'm a tabletop game designer. I recently built machine learning models to help with playtesting. However, the more I used AI the more I realized how important the human side of data was. From basic machine learning algorithms to complicated neural networks, the AI playtesting models were only ever as useful as the people building and running them made them. So I wanted to take a step back from AI and take a look at the role of data scientists. I felt the best way to do this was to look at all the mistakes I made when first using data for game design (I made a ton) because without those human errors, the AI tools wouldn't have had a functional foundation I definitely have a lot of room for growth as an author. Please feel free to leave any and all feedback! Hope that mistakes made in this article make the next one better! Key insights: Sample size matters (its not just something your statistics prof rambles about) Stratify your data! Data drift can hit in unexpected ways, so remember the business case and don't get lost in the data itself I will update the visual cues section. I also wrote a tips and tricks document for playtester which might have had a bigger impact than new art, so want to mention that as well In you're more interested in the pure AI side please check out: How to Train Your AI Dragon submitted by /u/BSS_O [link] [comments]

I saw many discussions about TMLR and other journals lately and how their review processes are considered fairer and less random. My question is, how much does it hurt one's chance much of getting interviewed/hired as a ML research scientist if they choose to publish at only journals like TMLR, JMLR, or Neurocomputing, instead of conferences? Edit: just to clarify, I mean corporate research scientist positions instead of academic positions. submitted by /u/dontknowwhattoplay [link] [comments]

I’m tired of training a lot of models and trying different datasets but still my model is trash and can’t detect clearly it sometimes has mAP50 pf 80% but it is only in numbers not practical, what can i do to have a good model that can be used? I trained using YOLO11n to use it in RPI5 16GB RAM no AI hat, but still can’t get the results i want, i tried searching and learning what could go wrong but I can’t seem to find the right solution+ i’m not that big of an AI expert so. submitted by /u/vDHMii [link] [comments]

I'm an ML Engineer at a mid-size company, I got an offer for a Lead Data Scientist role. Sounds great on paper, but the actual day-to-day is: dashboards, analytics, stakeholder management. I'd be the sole data person. For those who've faced similar choices: how much would the money need to beat your current comp to make the switch? Does a Lead title matter at this stage? Or is technical depth more valuable long-term? submitted by /u/MorningDarkMountain [link] [comments]

Only counting those categorized as cs.LG. I'm sure there are multiple other subcategories with even more ML papers uploaded such as cs.AI, and math.OC How are you keeping up with the research in this field? submitted by /u/NeighborhoodFatCat [link] [comments]

For people just starting out in GPU kernel engineering or LLM inference (FlashAttention / FlashInfer / SGLang / vLLM style work), most job postings still list “C++17, CuTe, CUTLASS” as hard requirements. At the same time NVIDIA has been pushing CuTeDSL (the Python DSL in CUTLASS 4.x) hard since late 2025 as the new recommended path for new kernels — same performance, no template metaprogramming, JIT, much faster iteration, and direct TorchInductor integration. The shift feels real in FlashAttention-4, FlashInfer, and SGLang’s NVIDIA collab roadmap. Question for those already working in this space: For someone starting fresh in 2026, is it still worth going deep on legacy C++ CuTe/CUTLASS templates, or should they prioritize CuTeDSL → Triton → Mojo (and keep only light C++ for reading old code)? Is the “new stack” (CuTeDSL + Triton + Rust/Mojo for serving) actually production-viable right now, or are the job postings correct that you still need strong C++ CUTLASS skills to get hired and ship real kernels? Any war stories or advice on the right learning order for new kernel engineers who want to contribute to FlashInfer / SGLang / FlashAttention? Looking for honest takes — thanks! submitted by /u/Daemontatox [link] [comments]

Welcome to this week's entering & transitioning thread! This thread is for any questions about getting started, studying, or transitioning into the data science field. Topics include: Learning resources (e.g. books, tutorials, videos) Traditional education (e.g. schools, degrees, electives) Alternative education (e.g. online courses, bootcamps) Job search questions (e.g. resumes, applying, career prospects) Elementary questions (e.g. where to start, what next) While you wait for answers from the community, check out the FAQ and Resources pages on our wiki. You can also search for answers in past weekly threads. submitted by /u/AutoModerator [link] [comments]