Hey! I have an interview coming up with BCG X. Anyone here been through the process with them? What about other consulting/mbb firms? submitted by /u/Feeling_Bad1309 [link] [comments]

YouTube released some interesting metrics for their 20 year celebration and their data environment is just insane. Processing infrastructure handling 20+ million daily video uploads Storage and retrieval systems managing 20+ billion total videos Analytics pipelines tracking 3.5+ billion daily likes and 100+ million daily comments Real-time processing of engagement metrics (creator-hearted comments reaching 10 million daily) Infrastructure supporting multimodal data types (video, audio, comments, metadata) From an analytics point of view, it would be extremely difficult to validate anything you build in this environment, especially if it's something that is very obscure. Supposed they calculate a "Content Stickiness Factor" (a metric which quantifies how much a video prevents users from leaving the platform), how would anyone validate that a factor of 0.3 is correct for creator X? That is just for 1 creator in one segment, there are different segments which all have different behaviors eg podcasts which might be longer vs shorts I would assume training ml models, or basic queries would be either slow or very expensive which punishes mistakes a lot. You either run 10 computer for 10 days or or 2000 computers for 1.5 hours, and if you forget that 2000 computer cluster running, for just a few minutes for lunch maybe, or worse over the weekend, you will come back to regret it. Any mistakes you do are amplified by the amount of data, you omitting a single "LIMIT 10" or use a "SELECT * " in the wrong place and you could easy cost the company millions of dollars. "Forgot a single cluster running, well you just lost us $10 million dollars buddy" And because of these challenges, l believe such an environment demands excellence, not to ensure that no one makes mistakes, but to prevent obvious ones and reduce the probability of catastrophic ones. l am very curious how such an environment is managed and would love to see it someday. YouTube article submitted by /u/takuonline [link] [comments]

I'm pursuing my doctoral deg in data science. My domain is ai in Healthcare. We collab with a hospital from where I get my data. In return im practically at their beck and call. They expect me analyze some of their data and automate a few tasks. Not a big deal when I have to build a model it's usually a simple classification model where I use ml models or do some transfer learning. The problem is communicating the feature selection/extraction process. I don't need that many features for the given number of data points. How do I explain to them that even if clinically those two features are the most important for the diagnosis I still have to scrape one of them. It's too correlated(>0.9) and is only adding noise. And I do ask them to give me more variable data and they can't. They insist I do dimensionality reduction but then I end up with lower accuracy. I don't understand why people think ai is intuitive or will know things that we humans don't. It can only perform based on the data given. submitted by /u/crazyplantladybird [link] [comments]

I entered DS straight after an undergrad in Computer Science. During my degree I did multiple DS internships and an ML research internship. I figured out I didn't like research so a PhD was out. I couldn't afford to stay on for a Masters so I went straight into work and found a DS role, where I'm performing very well and getting promoted quickly. I like my current org but it's a very narrow field of work so I might want to move on in 2-3 years. I see a lot of postings (both internally and externally) require a Masters, so I'm wondering if I'm putting myself at a disadvantage by not having one. My current employer has tuition reimbursement up to ~$6k a year so I was thinking of doing a part-time Masters (something like OMSCS, OMSA, or a statistics MS program offered by a local uni) - partially for the signalling of having a Masters, and partially because I just really love learning and I feel like the learning has stagnated in my current role... On the other hand I'm worried that doing a Masters alongside work will impact my ability to focus on my job & progression plans. I've already done two Masters courses part-time (free, credit-bearing but can't transfer them to a degree) and found it ok but any of the degrees I've been considering would be much more workload. Another option would be to take a year out between jobs and do a Masters, but with the job market the way it is that feels like a big risk. Thanks in advance for your opinions/discussion 🙂 submitted by /u/fightitdude [link] [comments]

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When churn prediction is done, we have predictions of who will churn and who will retain. I am wondering what the typical strategy is after this. Like target the people who are predicting as being retained (perhaps to upsell on them) or try to get people back who are predicted as churning? My guess is it is something that depends on the priority of the business. I'm also thinking, if we output a probability that is borderline, that could be an interesting target to attempt to persuade. submitted by /u/Adventurous-Put-8042 [link] [comments]

I'm currently doing my undergrad and have built up a decent foundation in machine learning and data science. I figured I was on track, until I actually started looking for internships. Now every ML/DS internship description looks like: "Must know full-stack development, backend, frontend, cloud engineering, DevOps, machine learning, deep learning, computer vision, and also invent a new programming language while you're at it." Bro I just wanted to do some modeling, not rebuild Twitter from scratch.. I know basic stuff like SDLC, Git, and cloud fundamentals, but I honestly have no clue about real frontend/backend development. Now I’m thinking I need to buckle down and properly learn SWE if I ever want to land an ML/DS internship. First, am I wrong for thinking this way? Is full-stack knowledge pretty much required now for ML/DS intern roles, or am I just applying to cracked job posts? Second, if I do need to learn SWE properly, where should I start? I don't want to sit through super basic "hello world" courses (no offense to IBM/Meta Coursera certs, but I need something a little more serious). I heard the Amazon Junior Developer program on Coursera might be good? Anyone tried it? Not trying to waste time spinning in circles. Just wanna know how people here approached it if you were in a similar spot. Appreciate any advice. submitted by /u/No-Brilliant6770 [link] [comments]

Curious what people here think about this article: Responsible Tech Certificates: A Worthwhile Expense? Personally I find these to be mostly a waste of money, but as someone who's interested in getting into ethical AI, was wondering if anyone has had a similar experience and if it helped them get their foot in the door. submitted by /u/Moonlit_Sailor [link] [comments]

I’m a sophomore double majoring in Data Analytics and Data Engineering with a minor in Computer Science. (It sounds like a lot, but I came in with an associate’s degree from high school, so it’s honestly not a ton) My end goal is to become a Data Scientist, ideally specializing in time-series forecasting or recommendation systems. I plan to go straight into a Master’s in Data Science after undergrad. Today, I just got an offer for a Business Analyst Internship. The role focuses heavily on SQL and Power BI, but doesn’t involve any Python, machine learning, or advanced statistics. It’s a great opportunity and I’d be working with a Business Analytics team at a credit union, but I’m a bit torn. Will having “Business Analyst Intern” on my resume make me look less competitive for future data science internships or full-time roles—especially compared to students who land internships with “Data Scientist” or “Data Science Intern” in the title? I know I’m only a sophomore, and I don’t want to overthink it, but I also don’t want to unintentionally steer myself toward an analyst-only path. Any advice or insight would be appreciated! submitted by /u/LilParkButt [link] [comments]

Hey all, I posted a little bit about my current job situation in a previous post: https://www.reddit.com/r/datascience/comments/1javfus/do_you_deal_with_unrealistic_expectations_from/ Ever since the year started, I've just been looped into tasks where I have no context what it's supposed to do, don't have the requirements clear, frequently have my boss try to get something out without clear requirements and then us fixing it after the fact with another co-worker constantly expressing dissapointment and frustration for things not churning out sooner. For the past month, I've been working several 12-14 hour shifts. On days when I don't have quick turnaround times, I've noticed myself losing focus, losing interest in the work overall. I signed up for a bunch of Udemy classes in the beginning of the year and feel like my headspace isn't there to upskill even though I had a lot of enthusiasm before. Has anybody gone through this situation and have advice? I want to change my job eventually in a few months, but I want to spend time preparing rather than just jump ship at the moment, esp in this market. submitted by /u/thro0away12 [link] [comments]

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Our DS group was moved under a traditional IT org that is totally focused on delivery. We saw signs that they didn’t understand prework required to do the science side of the job, get the data clean, figure out the right features and models, etc. We have been briefing leadership on projects, goals, timelines. Seemed like they got it. Now they admit to my boss they really don’t understand what our group does at all. Very frustrating. Anyone else have this situation submitted by /u/DeepNarwhalNetwork [link] [comments]

My parents live in the Chicagoland area and I'm considering moving back home. I've been a data scientist at my current company for about 1.5 years now, primarily doing either ML builds (but not deployment, that's another role at my company) or more classical statistical analyses to aid in decision making. I have a location requirement where I work currently, and while I've been given feedback that I'm a strong performer, I don't anticipate being granted permission to work remotely. I've been looking into the companies in the area and Walgreens is one of the ones I'm considering, but in addition to the current acquisition they're undergoing, I'm hearing some odd things about their data science group - however it looks like there's ML roles open in the area. I'm wondering if there's anyone who works there that would be open to just a quick conversation about how those roles look there so I can better understand if it's a viable option for me. submitted by /u/SkipGram [link] [comments]

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Hi, I was curious to know if you are an interviewer, lest say at faang or similar big tech, what makes you feel yes this is good candidate and we can hire, what are the deal breakers or something that impress you or think that a red flag? Like you want them to think about out of box metrics, or complex metrics or even basic engagement metrics like DAUs, conversions rates, view rates, etc are good enough? Also, i often see people mention a/b test whenever the questions asked so do you want them to go on deep in it? Or anything you look them to answer? Also, how long do you want the conversation to happen? Edit- also anything you think that makes them stands out or topics they mention make them stands out? submitted by /u/Starktony11 [link] [comments]

I'm currently working on a credit scoring model. I have tried various feature engineering approaches using my domain knowledge, and my manager has also shared some suggestions. Additionally, I’ve explored several feature selection techniques. However, the model's performance still isn't meeting my manager’s expectations. At this point, I’ve even tried manually adding and removing features step by step to observe any changes in performance. I understand that modeling is all about domain knowledge, but I can't help wishing there were a magical tool that could suggest the best feature ideas. submitted by /u/guna1o0 [link] [comments]

I see a lot of job posting saying “leverage AI to add value”. What does this actually mean? Using AI to complete DS work or is AI is an extension of DS work? I’ve seen a lot of cool is cases outside of DS like content generation or agents but not as much in DS itself. Mostly just code assist of document creation/summary which is a tool to help DS but not DS itself. submitted by /u/Trick-Interaction396 [link] [comments]

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My firm was contacted by a marketing measurement company called Incrmntal. Their product is an MMM that uses interrupted time series (i.e. synthetic control) with a reinforcement learning step. Their documentation is very light. There are no simulation studies and just a handful of comparisons with A/B tests. It's not clear what the reinforcement learning process is, if it's there at all, and the time series model is similarly opaque. The whole thing seems pretty scammy. The marketing materials are fairly aggressive and make repeatedly inaccurate claims. Has anyone used them? Any insights into what they're doing? How well did it work for you? submitted by /u/Lanky-Question2636 [link] [comments]