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I'm trying to transition to CS from a social science background, and am having a lot of trouble figuring out what to put on my resume/what to present. A big part of the issue is that most of the programming I've done outside of coursework is in R/Python, and my experience is mostly social science research/statistical programming. I want to get an internship doing some kind of software engineering, and don't really have a strong preference for front v backend etc. I'm definitely going to work on a project I can add as an example of a significant project in C/C++ over the summer, or maybe JS. Do y'all have other critiques/advice? 1. How should I present the education section? I've been leading with it, in part because I'm still a student and most of my peers resumes I've seen do the same, but also because the department has a v good reputation and the BS/MS are probably the most clearly CS, not just data analytics thing on my resume. It sounds like that may not be the move? I'm also doing a weird thing, where I'm getting a dual BA and BS, as well as an MS, and all three are going to be conferred at the same time. I'd imagine that the BA in Gender Studies probably isn't going to be the #1 draw there, and I'm sure having all three there is probably confusing. Any thoughts on how I should organize those? 2. Should I cut some more of the less technical experience? The 2nd in the sociology department did involve a good bit of working with weird API's/databases in R & Python, but that wasn't the main focus. 3. I've listed the languages that I've either used a lot for work (R and Python), or taken at least 2 or 3 semesters in and would feel comfortable using in a workplace (i.e. C/C++, although I'd rather not use that much C...). Not sure if 'familiar with' represents that or not though. Should I add Node.js and D3 into the skills section as well as the bullet points? I thought they might just fall under JS but looking at other resumes that seems wrong. At the moment I'm getting way more responses from researcher/data analyst roles, which makes sense given my background, but I'd like to do dev stuff, and get my resume in a place where I can be more competitive for that as well.  e: Also not sure if it's just me, but for some reason the image looks a lot crisper in its own tab than on the forum - not sure what's up with that. e2: Also not really sold on my coursework section... I don't know if I'm actually getting across that much with it. foutre fucked around with this message at 01:11 on Apr 30, 2019 |
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Apr 30, 2019 00:58
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RIP ZEEZ 
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30.5 Days posted:If you don't want to do data science, you need to stop talking about data science so much. Remove selected coursework, remove the experienced/familiar dichotomy from skills, and move R to the end. Eliminate the first two skills bullets and replace them with popular infra/systems tech you've worked with, if any. When you interacted with databases, what databases were they, etc. Additionally, are you a MS student who's getting a side job, or are you a software engineer who goes to school on the side. If you want to be the latter, you need to present yourself that way. Don't call yourself a student, just tell people you're working on your master's. Move education to the bottom, etc. It's all a matter of focus. Very good points re: the focus, ty. I think I'll take it back to the drawing board some, and definitely add the infra/systems stuff. This is (clearly not well) adapted from my data science resume, which is most of what I've focused on in the past. ultrafilter posted:Have you thought about data science? Your skill set and experience are better aligned with that career path than plain vanilla software engineering. I have thought about data science -- it's definitely interesting. I actually initially went into the MS thinking I would mostly focus on it, but I've really liked some of the more general CS/HCI stuff I've done and want to see what regular software engineering is like as well, and try it out. I'm also somewhat concerned that not having a PhD/only having a medium amount of grad-level Statistics could be more of an issue down the line for progressing in data science vs. regular software engineering. I'm not sure to what extent that's actually the case though, it very much could just be that a lot of the people I know who do DS are doing PhDs/have much stronger stats backgrounds than myself and that's skewed my perspective.
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Apr 30, 2019 01:41
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Mecca-Benghazi posted:So to the newbies, agreed with the above, play the game and milk whatever differences you have that could be construed positively for all they’re worth IMO On that note, I'm going through the application process for undergrad internships at the moment. Although I'm doing a CS degree at the moment, I've also done a degree in Gender Studies, and have experience (that doesn't make it onto my resume) doing other stuff related to that, i.e. being a counselor, doing research, volunteering etc. For behavioral interviews I've been mostly sticking with examples from CS/data science projects/internships, but there's a good number of questions where other experiences would be pretty relevant, and would highlight the stuff that I'm trying to get across in my other answers (i.e., problem-solving skills, learning techniques, collaborating effectively). I think they'd probably stand out more - like today there was a question about 'helping others', and I answered with something about a project I'd worked on, but I could also talk about like, working with homeless youth at a queer outreach center. All else equal, is there value to using these alternative answers, or is it generally better to stick with stuff that's more obviously relevant to stuff that might come up in whatever job I'm interviewing for? How would y'all suggest making that decision? I want to make sure that I get across that I have technical skills, but a lot of those other experiences heavily informed how I make decisions, balance competing demands, work in teams, etc. as well.
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Feb 5, 2021 00:16
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What's the best way to go about declining/backing out of an internship offer you've accepted? I accepted the initial offer at the latest date they could do, but then got an eleventh hour offer from a place I like a lot more, that's the type of position I want in the industry I want to work in (ie, one was full-stack at a big finance place I'm not too excited about, the other one is a data science position at a company I'm way more into). They're both intended to lead to a full time position post graduation, and the new offer would basically put me exactly where I wanted to be starting out. I realize this is bad form no matter what, but I would like to back out of the original internship in whatever the most graceful/professional way is. Should I be more specific at all, or just sort of say something more general about my plans changing, uncertainty with covid, etc? I'm not concerned about trying to get hired at the original place or anything, I just want to be polite to the hiring team and my would-be colleagues who were great throughout the process. Would appreciate any help, it's hard to find a script/guidelines for this kind of thing.
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Mar 15, 2021 21:10
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Great, thanks y'all. Left it mostly vague, and added an additional thank you/a bit more explanation to the person who'd been my main point of contact/interviewer. Validating to hear that generally saying less is better in these kinds of situations, it does seem like that's usually a good heuristic.
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Mar 15, 2021 23:47
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Lockback posted:I hire people with and without degrees, but right now junior/intermediate roles are getting more candidates than I've ever seen before. On that topic, I know people who've been getting offers for entry level positions for the fall of 2022, with like, one or two month decision deadlines, usually from places they're going to intern at but haven't yet. Is this actually a common practice in the industry, or maybe a function of how weird the job market is at the moment?
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Mar 22, 2021 20:20
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E: ^^ 100% agreed On the "scary and impossible" note, does anyone have tips for interviewing for 'High Performance Computing' positions, or honestly just resources explaining what that actually means? I've gone through two screeners prior to the actual interview, but they work on a wide range of projects and so far the interviewers have been general recruiters who haven't actually known what specific project I'm being considered for. It sounds like it's a group that's a mix of working on supercomputer/quantum computing architectures (I guess?) and creating deep learning models to run on them. My background in the latter, so I've gotta imagine those projects are the ones I'm being considered for, but I figure it couldn't hurt to have a better idea of what the rest of it even means. Honestly I'm still kind of confused re why I'm being considered at all, but I figure I should give it my best shot. At the very least it's more experience interviewing and definitely validates just applying for jobs even if you don't really meet the requirements.
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Jul 24, 2021 23:05
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Would y'all be up for giving some feedback on my resume? This version is specifically for new grad data science jobs, but I also use a version with some skills/projects swapped around for data engineer positions too. There's a couple things in particular I'm not sure on: 1) I started my current internship a week ago, so I have no accomplishments, but it will use skills from coursework that don't come up elsewhere (esp on the data engineering/infrastructure side of things), and has a lot more name recognition than other places I've worked. Basically I want enough there that I can chat with an interviewer re: what I'm doing without misrepresenting my experience. Tbh I'm still kind of baffled they hired me for the role at all. 2) The most recent completed internship (second in list) specifically wasn't focused on business outcomes, but instead on making design decisions - which I loved - but that doesn't translate as well to clear numbers/impact. Are these bullets alright, or should I try and cut/quantify more? 3) At the bottom, I just added a section with two research projects I completed for classes, with links to the Github and paper. They're also on my portfolio site, but the basic idea is to get across the idea that I've completed substantial ML projects (which aren't under NDA or otherwise disallowed from being put on Github) and can do that kind of work despite not having a grad degree. Not sure if the section is getting that across as-is though - maybe I should be more specific re: the techniques/tools used, or just cut it and leave them on the portfolio site only? The 'impacts' are also strictly 'here is some small contribution to the field', and idk if that really comes across as substantial 3.5 - I left out my other experience during college, since some of it was social science research, and the rest was product management at a start-up and neither felt as relevant. It does mean I have a gap for 2019/2020 though, not sure if that matters  More broadly, does this seem like a reasonably competitive resume for like, FAANG/big tech companies? I haven't applied to those kinds of roles in the past since I was more oriented towards social science research, but would like to this time around. I have gotten a decent number of recruitment messages from those kinds of places, but I'm not sure if it's just because some search for major + school + gpa threw me out as a result, or because I'd actually be a solid candidate. I'll apply either way ofc, and will go over it again at my uni's career center once I finally get an appt, but just kind of wanted to set my expectations. On a related note, if anyone has good resources they'd point to slash tips for negotiating offers I'd appreciate it (reading through the one from the OP). The place I was at over the summer should be sending me a full-time offer in the next few weeks, and is pretty great in terms of work-life balance, has a uniquely interesting career progression, and is at a v specific intersection of a lot of my interests - I loved working there. The one issue is, from looking on Glassdoor/h1b sites, salaries there average ~80k, whereas it looks like equivalent roles in the Bay Area are more like 110-130k (the place I'm furthest along with in the interview process apparently has like 140k total comp for the Cloud SWE III role I'm for some reason being considered for). I honestly didn't realize that new grad salaries were that high here, but that's a pretty tough salary difference to overlook, and I'm worried about anchoring salary expectations for my career going forward. It does feel kind of absurd when compared to the non-profit stuff I'd done before going back to school, but hey. e: Ok, I found a typo/some inconsistent capitalization looking back at it, will fix that foutre fucked around with this message at 03:08 on Sep 30, 2021 |
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Sep 30, 2021 02:57
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| # ¿ May 15, 2024 03:16 |
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Deptfordx posted:So I was thinking of trying one of the Digital courses. Wow there's a lot of options. I can very much see how that'd be overwhelming. As a disclaimer the majority of roles I've been in have been data science/ analytics, not SWE, so I don't have as much to add on the latter. On that note... Data Analytics could be good (for whatever it's worth you'd probably be using SQL). The most important skill is translating analysis to 'business impact', and I'd imagine your experience running a business would translate and be a big plus. Data Science may be pretty similar, in terms of course content. Imo choose between the two tracks, and individual boot camps, based on course content - ie a focus on stats/working with databases/modeling is better than Excel skills or learning a specific dashboarding tool. Imo it'seasier to learn to use a tool once you have the general background, but ymmv. I guess I would say that for Data science specifically it can be harder to progress without a tertiary degree of some sort, at least relative to regular software engineering. Doesn't mean that the boot camp wouldn't be helpful! In general, data analytics (v much depends on the role) tends to care less about any specific background. Obviously make sure you like statistics, working with data, etc before committing to either of these. I think the coding, or coding with Python would be good general introductions. Just as a general rule (feel free to correct me if other folks know better) I would probably avoid the ones that are targeting one technology - ie, AWS cloud engineer. Although AWS probably isn't going anywhere for a while, I think it'll probably be better to get foundational, general skills rather than tool-specific ones. You can always learn how to use a platform pretty quickly if you've got the basics. I also think you should think about/ share what kind of work you enjoy! Could help narrow down some with that.
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Oct 25, 2023 09:49
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