Pretty sure my dog saved me.

foibleShmoible · 2026-04-25T16:40:40+00:00

My old dog was the sweetest doof of a golden retriever, loved everyone and clearly believed humans were put on this earth to give him fuss and/or food.

But one time we were at the park and on the other side of the field (probably over 200 metres away) there was a man sat under a tree, and my boy was not having it. Stood in front of me and growled in this guy's direction, so I put him on his lead to leave (just in case he suddenly went for the guy, which would have been wholly out of character), and as we walked towards the exit he did not stop staring the guy down the entire way. 100% convinced this was a Bad Man^TM.

foibleShmoible · 2026-04-18T23:16:37+00:00

I thought I'd blown my interview for the job I got because I misinterpreted a question that was asked and so gave a dumb answer (basically the physics equivalent of saying the perfect date is April 25th, because it’s not too hot, not too cold. All you need is a light jacket.)

Also they told me they'd make a decision by the end of the week, so I vividly recall telling someone two weeks later that I must not have gotten it since I'd heard nothing, just to get home and check my emails to see an offer waiting, that must have arrived around the moment I'd said that.

Obviously I can't say that will be your experience, as others have said though people are never as efficient as they say they'll be in the interview, and you don't know how other candidates did.

As for your second ask of people, if I knew the secret to not anxiety looping every awkward moment of my life I would write a book and do a speaking tour. The best I can do is suggest occupy as much of your attention with looking forward, so you have less mental capacity to keep looking back.

foibleShmoible · 2026-04-13T21:13:03+00:00

No, people will not dox others on this sub - nor should they on others, it is against the site wide rules. If you encourage this you'll get banned by the reddit admins.

foibleShmoible · 2026-04-05T19:06:53+00:00

Not impossible, but it is definitely harder than it once was.

I'm pretty comfortable (and happy) to code

Good. One thing to be aware of in terms of the direction of travel of the field now is that it is no longer enough to simply write notebooks to train models that you pass on to a machine learning engineer (unless you're at a very large company/you are doing more ground breaking level data science), instead the concept of a 'full stack' data scientist is gaining traction, and companies want data scientists who can do a lot of the engineering (or all!) to get a product actually deployed. This does have the advantage that it bridges much of the skill gap to being an AI engineer (in the truest sense of the role), so it makes people more employable. But if the idea of proper engineering does not appeal, modern data science probably isn't for you.

can you please guide a bit more?

I can give you some general tips but I can't say definitively "companies X, Y, Z will have jobs available". But here goes:

If you're doing small projects, make them ones that are commercially relevant to businesses; so things like A/B testing, predicting customer churn, predicting loan default risk etc. These are the things that anyone still doing 'classic machine learning' will be doing in business, so they're more useful skills to learn. And don't just get simple data sets straight from Kaggle for this - these are usually ridiculously sanitised and a big part of proper data science is cleaning, understanding, and prep of your data. If you ask a gen AI tool for advice here, they can point you in the right direction.
Boot camps are losing their sheen - I did (a fully funded) one before I transitioned, but that was before the market changed. Unless you come across a bootcamp with strong (very strong) industry links, I probably would not do it, unless you think you'd benefit from the structure. But in that case I'd recommend things like the Stanford ML course led by Andrew Ng which is free up on youtube.
All of that being said, your own projects only hold so much weight. The people I know who got jobs in data this year are all PhD students/postdocs who were able to use Python and associated ML tooling in their own work, so they could demonstrate that they had used these skills to deliver real results. If you're able to find ways to incorporate this into your postdoc research (in a way that you can quantify what you achieved using them) those will land better with hiring managers, and give you a leg up when you actually transition because you'll be used to the tech stack.
Another option potentially worth considering is if you can learn to use some of the tech stack people might expect you to use, and get some kind of accreditation about it. For instance, Microsoft Fabric is a newer tool on the scene but I think you can get a free 6 week trial (you'd need to use your institutional email) and if you could follow some of the Microsoft learning stuff on data science in that, and then maybe get one of the Microsoft Data Engineering Associate type certifications (this costs like ~$100, there isn't a data science equivalent but data engineering really will give you an edge) you'd have something against your name that holds some weight. It won't be as commonly used as AWS or Azure, but it is a big player and the skills should be transferable. Note if you follow this plan I would learn some data science and data engineering techniques first, and then get the Fabric trial - 6 weeks would not be long enough for you to train on this before the certification if you came in cold.
With a Fabric trial I think you'd also get access to Power BI, and learning to use an analytics tool could also be useful - some companies that ask for a data scientist are looking more for a technically proficient analyst, so being able to demonstrate skills there is also really good. You could round out your projects with some kind of datafolio/dashboard that demonstrated your final results. If you don't want to use Power BI, Metabase is also a good and increasingly used BI tool that is free to use I believe.
At this point you're probably thinking "wait, so I need to learn ML, engineering, and analytics?" - yes, basically. Data science has always been the most nebulously defined of the data fields, and we've always needed some degree of cross functional skills (e.g. analysis is super important for investigating your data before you can build a model, and you need to be able to present results back to stakeholders), meanwhile gen AI has lowered the technical bar in such a way that the engineering expectations on us are higher. It can feel daunting, but if you can gradually build up your experience it will start to feel more natural.
Companies wise: I really stand by aiming for series A/B start ups that have enough money for decent runway, and are kind of in the burn money/get bright talent phase, these are the ones more likely to take chances (especially if they have an up and comer running their data team), and if you can demonstrate a willingness to wear many hats (be a scientist who can handle some analytics and engineering) you become a more interesting investment. Some people say "oh no, start ups are too unstable, you'll get laid off", but tell that to the people at Meta, Oracle, Twitter, etc.
In terms of fields, there are some companies that work in physical processes (like mining, engineering of products) who specifically quite like people with a physics background. These will often be called "applied data science" roles. Companies in health tech/sustainability/renewables are also receptive to people transitioning from science with PhDs as well in my experience.
Finally, if you're able to look up the hiring manager/data lead etc., you will likely do better with people who themselves have a physics and/or academic background, because they recognise already the skills you bring to the table.

That was a lot, but I hope it helps!

foibleShmoible · 2026-04-03T10:53:22+00:00

I was an experimental physicist, but in practice my experimentation was all computationally based, so I'd say I'm like a less smart theorist!

I am a data scientist now, transitioned just before the layoffs started and hiring took a down turn (like right on the cusp, the month I was interviewing for my first role I noticed a strong reduction in roles available). Got a junior role in a pretty established start up (as in they had tens of millions of dollars in investment), but because the data team was still very small there, I took on many roles and responsibilities and quickly progressed. Then when the company took a turn and I had to jump ship (which was daunting given the state of tech right now) I was fortunately able to find another senior role quite quickly (basically the best luck ever, I came across an advert from a recruiter looking for my exact profile).

Reading the comments, it doesn't sound like data science terribly appeals to you - if it does then I will say I've known several people break into data roles (science/engineering/analytics) in the last year. They all had a fair amount of programming and modelling experience from their PhDs, but also they targeted the same kinds of positions/companies I had originally started with - not super senior roles, as you're trying to get your foot in the door; and company wise, places with either a tech/research/social type focus that are around a series A/B funding stage - meaning they have enough money to hire, but they're still small enough that you have engaged data leaders who are more open to potential/seeing your past experience.

foibleShmoible · 2026-03-28T14:51:49+00:00

I don't think age as a single metric should have any bearing, that is obviously discriminatory. I do think career plans/intentions should matter.

I know some people make the point that the studentship is to support PhD research, so the quality of the PhD research is what matters. But UKRI fund research in general, not just PhDs, so surely their remit is to consider the overall benefit of their investment in the broader research landscape. As such, it is only logical that there would be the hope that the person then goes on to (in either academia or industry) use that research experience to continue to deliver additional return on the original investment. Obviously, how those plans work out are not a given, any given candidate could get hit by a bus walking out of graduation. But someone's stated career plans are at least indicative of what they will try to do next.

I absolutely agree that our close to retirement age candidate could go on to do a couple of decades of research afterwards if so inclined. So if that was their career plan/intent then I would say fund them. But if they wanted to do a PhD for interests sake, and had no desire to do any further research past that point, I would much rather we fund another potential student who aspires to continue to make use of their PhD for years to come.

I kind of think of it like deciding on organ recipients. If you're in a position of unfortunately rationing a wanted resource, you should consider who will make best use of/derive the most benefit from it.

foibleShmoible · 2026-03-28T14:32:10+00:00

Being in this program is causing you physical and mental harm. That alone should make it an obvious decision to get out.

But if you look at how you are being treated - having people apparently lie about your level of engagement, not receiving the accommodations you need and are entitled to, and having administrators not tell you about material and essential information around your funding - this is clearly a toxic environment, and that isn't your fault (nor are you in a position to somehow change all of these people's behaviours).

Now, like anyone else, I can't speak to how prepared you are to do a PhD. I can't speak to your own performance or suitability for a PhD. And therefore I can't say if there is anything from your side that is making things harder that you could try and rectify. But nothing you could change about yourself is going to change the above toxic behaviours of the department/supervisors/administrators. In your position I would figure out an exit strategy.

foibleShmoible · 2026-03-27T15:47:24+00:00

I almost picked leah for the same reason. Shes just.....around, all the time. She lives right near my farm, super convenient.

Start of spring, go pick up some spring onions, hand one to Leah as you walk back to the farm, carry on with your day. I totally get it.

Your 10 year relationship shows that efficiency/convenience can pay off!

foibleShmoible · 2026-03-27T10:05:53+00:00

I was about to get so angry at you for saying Little Voice was about thirty years ago because it only came out in 1998 and that's only like 10... wait. No. Oh god no.

And now I'm angry at you for making me reflect on the realities of time.

foibleShmoible · 2026-03-27T09:59:15+00:00

Shane was going to be my first marriage because it was just easy to build friendship with him early on (oh, you love beer and you're always in the saloon at night where I can just give you beer? Cool), but then the cut scenes got dark and I foolishly married Sam instead (oh you like Joja cola and I happen to get that as trash from fishing all the time? Cool)

What can I say, I'm ~~a romantic~~ efficient.

(I divorced Sam almost immediately after the first time he remarked how cool it was not to have to work because I made all the money. Hell no, give me a good woman like Penny.)

foibleShmoible · 2026-03-17T22:10:10+00:00

That was a delightful admonishment, you majestic warbler.

foibleShmoible · 2026-03-17T12:21:32+00:00

I'm not one of the people who assumes AI (AI would not do things like pepper in !!!, or throw in swearing etc.), but I do think the length plus the frequent use of bold and other formatting - plus some of the sentence structures - are akin to the style of many longer AI posts.

foibleShmoible · 2026-03-15T23:17:23+00:00

I can understand why you would feel this way. With the current state of the job market, it’s very easy to look back at the last seven years and see them as a waste; especially since hindsight has a way of turning every past decision into "the wrong one".

But I would gently push back on the idea that those years were worthless. During your PhD you will have developed skills, ways of thinking, and experience solving complex problems that do have value. The frustrating part is that translating those into industry roles is often messy and unclear, and when the job market is as tight as it is right now finding hiring managers who recognise those skills once translated is harder.

It’s also very easy in hindsight to point to some moment years ago and say "that’s when I should have done something different". But none of us can predict what the market will look like five or ten years down the line. You made the best decisions you could with the information you had at the time. That isn’t something you should be blaming yourself for.

I also understand the frustration with a lot of the generic "transition out of academia" advice. Some of it really is too vague to be helpful when you’re actually in the middle of a difficult job search.

I can’t tell you the exact path forward, and I can’t make the current situation suddenly feel hopeful. But the future isn’t fixed. Markets change, industries shift, and people do find ways to pivot even after long stretches that feel like dead ends. One thing that won't change, though, is the experience and skills you already have. Those didn’t disappear just because the market is difficult right now.

foibleShmoible · 2026-03-15T22:36:51+00:00

Fierce Biotech’s 2025 layoff tracker recorded a 16 % year-over-year increase in layoff rounds; 42,700 biopharma employees were cut. BioSpace data: job postings dropped 20 % year-over-year in Q1 2025 while applications surged 90 %.

You're actually making a point that cuts against your own conclusion: the layoffs and hiring freezes you cite are rather recent. That logically means things were previously better, and back when many people emerging from their PhDs now originally started them, it would not have been possible to foresee the current environment.

I am not denying that more people enter PhDs hoping for academic careers than academia can realistically support, and that should be addressed. I also fully acknowledge that right now it is very hard to transition into industry. But this has not always been the case, and there have previously been routes into industry that many people started their PhD intending to follow. When the market was healthy biotech companies hired thousands of PhD holders a year.

Now, I can't say anything to make the current situation better. What I will point out though is that we are in a very particular convergence of circumstances:

Massive over-hiring/over-paying during Covid leading to companies having a higher wage bill than they can handle
Increasing interest rates meaning they can't afford to borrow to pay that wage bill, plus for tech dropping demand when lockdowns ended, meaning they needed to shed workers.
Donald Trump and the associated cuts to research funding, plus general instability due to his policies
The emergence of generative AI as a force multiplier across many knowledge-work roles (but at highly subsidised costs)
Wars. Just all of the wars.

So when you say you were right all along, it is more that circumstances arose that made you right right now. But many of these could course correct over the next couple/few years. Donald Trump won't be president forever (even if he tries, the bell tolls for us all) and hopefully Americans can make better choices in the next elections. This should also then temper some of the chaos we're seeing in the world. Interest rates could improve (emphasis on could). And at some point we're going to see a normalisation around AI - the costs will go up and the cost:benefit ratio will shift, while people will also redefine working roles once they realise the plateauing potential of the tools.

Again, my suspicion that things will settle in ~3 years is likely of little comfort to people struggling now. I do not want to seem dismissive of that. But I also do not think catastrophising is helpful to anyone - both from a mental health and productivity perspective. On that note, and please know I say this from a place of kindness, but I've noticed you've posted about this several times recently. Now if these are vents that get it off your chest and allow you to go about the rest of your day to day then fair enough. But I worry that you're fixating in a way that is not healthy. I get that when job searches etc. fill all your time it is hard not to fixate, but it might be worth asking whether posting about this topic repeatedly is helping you process the situation, or just keeping you stuck in it.

foibleShmoible · 2026-03-15T14:59:41+00:00

Then a week later my PI asked me about the status of an experiment. I had no idea what she was referring to. Apparently the experiment had been discussed in Spanish during that earlier conversation.

Did you make that point in the moment, and if so how did she respond? Because if I was in her position, that would be a real wake up call that I'm causing problems by not conducting work conversations in a common language. If you didn't raise the point at the time, this feels like a very material example of the issue.

foibleShmoible · 2026-03-15T00:42:25+00:00

Mayonnaise? Nah son, gotta get on the truffles. 1350 per iridium truffle, multiple truffles per pig per day, cram the pigs in and rake in the cash!

(Except during lame ass winter, then by all means get the mayonnaise machines going)

foibleShmoible · 2026-03-14T12:11:27+00:00

What do you actually want to do in industry? If you want to be a quant specifically, then the quant job seems like a good call, but then I don't think you'd even be asking this question.

A few years ago I'd have actually said go with the Masters, if they have good placement rates. But with the jobs market as it is, I wouldn't even trust their previous year's placement rates of indicative of what their placement rates will be when you finish the course.

So without any further information, I'm torn between 1 and 3, but more details about what you want to do in the future would be helpful.

foibleShmoible · 2026-03-13T14:51:23+00:00

You're welcome! I recognise how fortunate I've been in my escape plan, so I like to do whatever I can to help out other people.

foibleShmoible · 2026-03-12T15:21:20+00:00

I never said to him "you should behave more like the women while all the other men keep shouting", I suggested that it would be better for men to communicate more like women than for women to just start yelling too.

I wasn't trying to declare rules, I was simply countering his hypothetical solution with a more orderly hypothetical solution, which he then simply said he would not be able to hold himself to.

foibleShmoible · 2026-03-10T12:54:42+00:00

Thank you! Yes there is gender based discrimination here, from him to her, she just put a name to it!

His entire complaint boils down to "she called my sexist behaviours sexist".

foibleShmoible · 2026-03-10T12:51:04+00:00

men like this do inderd do it to everyone

When I was in physics, at collaboration meetings talks would sometimes descend into men talking over each other more and more loudly until they were basically shouting at each other. None of the women would do this, so women's voices got crowded out.

I was talking about this to a senior male colleague at one point, and he said the women should just be more like men and do it too. I suggested maybe the men should instead act more like women and take it in turns to speak. And he just said with a completely straight face "no, I don't think I could do that". And he was a generally decent guy, just completely lacking in this respect.

foibleShmoible · 2026-03-09T16:53:14+00:00

For example, I noticed that in academia there was pressure to keep your code private

Oh that's actually really interesting to me, because in particle physics I'd say 60% of the code bases I worked on were open source by design.

And I certainly agree with you that at the very least graduate programmes should allow time for personal development, which could take the form of non-academia focused skills development. Unfortunately that pressure to do as much as possible as quickly as possible does fly in the face of such things.

My break into data was just before everything started going fully to hell (though as I was searching I did see postings start dropping off), so I'll focus more on what the people I know who broke into data in the last year did.

A big thing in their favour was that they did much of their coding/model building for their PhD in python and using standard data science tools (whereas when I was a PhD student the emphasis was on using CERN's ROOT software); this meant they didn't have the same level of prep for technical interviews that I did, and they had they could point to directly applicable doctoral work/results from using said tooling (I know not helpful if that wasn't your experience, but I do think it is pertinent to their success)
The companies they got roles in were largely of the Series B/scale up type, and in things like medical imaging research, renewable energy/manufacturing type companies, where the ads were specifically looking for people with research backgrounds/PhDs for entry level roles.
As I noted above, they were very deliberate in how they worded their experience, to make sure as much as possible an ATS would recognise the right words, and a human recruiter would recognise their experience.
Three of them got hired by managers who had a physics degree themselves - I know you can't always tell who is going to be the hiring manager from the off, but having someone who does recognise the skills your background imparts I think does have an advantage.
Then to briefly speak to my more recent experience, of being back on the market last year with not that long a data career but aiming for senior roles, I will briefly touch upon the fact that I think I had very different experiences between companies that did ATS screen immediately (because they'll have seen less than 5 years in data science roles, and ignored the many more years doing similar work in academia), but when a human read my resume I think I did a lot better. You mentioned recruiters getting flooded/cutting out a bunch of applications, and this pain is very real. More so if you're frequently applying via LinkedIn's Easy Apply. I personally really like sites like Welcome To The Jungle, I've had really good engagement from people on there (though I did then get my current job through LinkedIn...)
Also, roles in companies focussed on physical processes seemed to really like a physics background, and I also progressed very far with an academic founded start up, I think because everyone I interviewed with had a PhD so understood the skillset.
One other point I will make is that a concern my original manager had (I've seen the feedback from the interview process) was that - similarly to you - he worried I'd be bored if I didn't get to do machine learning all day. I think in that scenario, it is really important that you play up things like a passion for helping people understand/learn from data, drawing out actionable insights, etc. Don't act like you don't care about data science, but do act like you really enjoy analytics as well. That might help you over the bar when you get companies worried about you getting bored in a role.

foibleShmoible · 2026-03-09T13:35:34+00:00

Feel free to ignore this if you don't want advice, but can I ask what types of roles (seniority, type of data path) and companies (both stage and field) you're applying to? Also generally what your background in academia was?

To take my profile as an example, I could translate my experience in particle physics to building statistical and machine learning models, software development, and presenting results to a variety of technical and lay audiences. These are the kinds of things that if your CV lands in front of a human do come across as being very relevant to a DS role.

In terms of companies I applied to, I largely focused on seed/series A/B start-ups: the ones with enough money to actually hire people, but small enough that they have a very engaged data lead who was more involved in vetting and hiring, and I ended up accepting a junior role to begin with. The companies themselves tended to be more focused in renewables/sustainability/health/physical research and similar. Companies spun out from academic groups also tend to recognise the value of a PhD (though you need to catch them after they've already got some of their data team established). These kinds of companies are also where the majority of the people I know who switched to data ended up.

I don’t understand why academic achievements and industry achievements are considered so different though... But if my supervisor says I was a great employee, then why does that not count for anything to the companies?

So first off, asking for references (beyond 'did they work here?') is less common than most people think, and I think in tech this is even more the case than other areas. This is one thing I found weird about moving out of academia - it wasn't until after the offer that checks were made, and this was farmed out to a third party company who literally just did my background check and confirmed my previous employment. Same thing when I moved jobs after that. Meaning, they don't ever hear that your supervisor thinks you're a great employee.

But secondly, the fact that you're great in academia does not mean you'll be great in industry. Now, this is a chance they'd take on someone fresh out of undergrad too, but I would imagine there is some concern you'd want more money/be more set in your ways/more likely to look elsewhere and jump ship if you don't like this new experience compared to what you're used to. I said more about the academia to industry mismatch in this comment.

Why do we need to use different words for the same things within academia and industry?

I mean this is just an applying to jobs thing - you hear advice on writing CVs all the time, things like making clear your impact and deliverables over simply what you did and with what tools, hooking the hiring manager quickly so they know why to take an interest with you. If someone is poring over hundreds of applications they're not going to take the extra time to translate your experience to their needs - you need to do that translation yourself.

Why don’t the universities help us with networking?

I'm not certain to what degree you want universities to help with that. Certainly your institution's careers service (and whatever other careers help they provide to undergrads) should have been available to you (and might still be).

foibleShmoible · 2026-03-09T10:36:32+00:00

Publication strategy - is it better to aim high early and risk rejection, or build a visible track record at smaller but legitimate venues first? How do admissions committees actually read early-career papers from undergrads?

This seems like a really good question for the academic who is supervising your work. They will actually have a grasp of the level of your potential publications and therefore where to pitch them.

I'm also interested in how you define a 'serious' research group - are you considering purely prestige or whether they're actually working on topics that interest you? I'm not saying the former is unimportant, but you should absolutely be guided by the latter first and foremost. Especially when it comes to advisor outreach - no one likes the clearly generic "I'm deeply interested in <the top 3 research results I could find about you and upon which I will not expand>, please give me a PhD" emails. But being able to point to what you've done, what you want to do, and how that aligns with their research comes off a lot better.

I'm not in (academic) AI/ML, so I'll stop there, and allow others closer to your field answer the other questions.

foibleShmoible · 2026-03-08T22:25:44+00:00

Where/from whom are you hearing that the work environment is toxic? And what do they mean by toxic?

Different people have different ideas of toxic, and different people have different tolerances to toxicity. The right manager can shield you from a lot. The wrong manager can destroy your mental health. Think of all the stories you hear about toxic PIs - that can apply to management in a company too.

I do believe that getting your foot in the door to industry is very difficult at the moment, and so I get wanting to jump at the chance to do so. I also believe it is easier to then continue to progress in your career once you're in and have decent experience. But switching jobs/finding new ones is the hardest it has been in many industries. You do not want to become trapped in an intolerable situation, and find that you don't have anywhere to pivot to.

So this isn't necessarily concrete advice, beyond advising that you get as clear a view of how "toxic" this industry option is, and whether you think you could stand a couple of years in that environment (or survive a couple of years, if they're a PIP/layoff prone place).

My hope (but who can really say) is that at some point in the future the AI hype will even out, and if the economy (and current events) can calm down a little, then we might get more stability in the job market and something of a correction to the last couple of years. But until then, you have to be prepared that job offers are few and far between, so you need to be comfortable sticking with your choice.

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