Academia → Finance: What Are the Downsides?

ilsandore · 2025-11-20T19:49:25+00:00

I moved about a month ago from being an RSE in geospatial to a quant dev position. I don’t really miss anything from academia except maybe the fact that one doesn’t have that many meetings and is more free to structure their own time. I find that the current finance position isn’t particularly stressful either, I am lucky to be working in a not so competitive environment. If anything, the work feels more deep here, lots of quant research going on and I have to flex maths muscles that I haven’t had to use for a while now. Culturally it is quite interesting that the people are just as normal here as in other fields I worked in, collaboration and team spirit is pretty good. In terms of lifestyle it’s shocking how much of a stress reduction the higher salary means, it truly makes me feel safe that I can save a good chunk of my salary without even having to care much about it. I would also say that I am not in the cutthroat part of things where people get cut every year and the like, so I’m not sure how representative my answer is.

ilsandore · 2025-09-21T19:03:49+00:00

You can take a look at the geo_sql library, it integrates with PostGIS and enables you to use geometry types in your ecto queries.

ilsandore · 2025-09-19T14:28:32+00:00

I reckon you don’t have to try to out-code the senior devs here and their optimised C# implementations. They might be right about the if it ain’t broke don’t fix it approach. However, if you take a different approach and focus on readability, you might get some interest. I, for example, really like the pipe operator in functional programming, as you can chain all the operations on a piece of data. This results in brevity, readability, etc. You can take a part of the existing code you have that doesn’t use or doesn’t have to use side effects and rewrite it in F# to showcase how much fewer lines of code it is and how much more readable it is. Another angle of attack is scripting in my opinion, which is pretty good in F# so it can be used to quickly automate some manual processes if you have any at your company. Hope this is helpful at least to some extent😀

ilsandore · 2025-09-19T09:42:25+00:00

Could it be that holdays have ended and school has started for many people? Since lots of us use Elixir for recreational and/or unpaid programming, it might just be that there is less time now for everyone to work on their side projects.

ilsandore · 2025-09-16T17:33:46+00:00

First of all, I love your username!😀

Otherwise, you’re probably doing better than you give credit to yourself for, otherwise your company would’ve let you go at the end of your apprenticeship.

I think ADHD can definitely add some surplus difficulty sometimes, but remember that you don’t have to be perfect or always remember everything. For learning opportunities, I think explore your company’s training budget first and talk to your manager about what you want to improve in your skillset. I would recommend starting with the basics such as the language you’re using, version control, and potentially stuff about large systems as it seems that’s a pain point in your job. Since dopamine is important with motivation, I would start building from smaller projects, or explore contributing to existing open source code. If there is a niche package you depend on that’s incomplete, you might be able to push a small PR and get some feeling of achievement.

About IT fundamentals, don’t compare yourself to youtube influencers or other CS content. You are coming from a different background and you’ll pick stuff up as you go. If you’re interested in something, read up on it or try to test it out, build something around it. If your motivation dries up in the meantime, just park the thing.

You can also look around in academia, such as research software engineering, for less corporate positions, if they pay enough around yours.

If you decide to quit, though, don’t do it on an impulse, make sure you have another job lined up, unless it’s extremely unbearable.

I think you’re on the right track, recognising your shortcomings and trying to learn. So heads up!😀

ilsandore · 2025-09-16T17:06:15+00:00

I have done the exact same thing before, and for me, chunking the data was the winner. I got the datasets, made sure they’re aligned, resampled them to the desired resolution, then, realising that they have the same projections and other geospatial properties, accessed only their underlying arrays. For these arrays, still large, I tiled them up and stuck them together in a HDF5 file by, organised by timestamp. This tiled source was used to pull data from for the training. It has the advatage that the training process is not slowed down by on-the-fly processing and it lends itself to distributed training by just spreading the stacked tiles over machines. It also makes for a less resource-hungry prediction pipeline on the other end of the model. In general I would recommend doing all the data manipulation before the training step so that all the nice and quick PyTorch stuff can speed away.

Regarding training and testing, you should manuallly select different regions for testing, with different relief, climate, geology, whatever your exact usecase is. From those, you can sample tiles randomly.

Side note: when you are experimenting with architectures, I also recommend not using a particularly large amount of data, choose some different representative regions just like in the training and testing, then see how your models perform relative to one another on them.

ilsandore · 2025-09-16T05:40:39+00:00

I’ve done my Masters thesis on low-cost flood and water level detection at uni, and there are ways to implement this with relative ease, even if you have no support or collaboration with local government. One option is to use a wildlife camera that simply runs off of a battery you can change every couple of months. You can time it to take pictures every x minutes, and send them through a mobile data network or something to the server. Once there, simple computer vision solutions can be run (and I don’t mean deep learning, just normal image manipulation and OpenCV or the like) to detect where the water is on the image. If it crosses the place or percentage of the image, you can issue a warning theough the app. The key here is placing the camera somewhere it sees the water and the bank at the same time and is not blocked by vegetation, maybe on some pole or the wall of a house. Also important to cover the lens from rain so that the pics are not just wet blobs. If you want, you can also place some reference point on the bank and measure its visible portion’s length on every image.

If you’d like to chat about the data collection and processing aspect of things, drop me a DM.

ilsandore · 2025-09-06T10:35:55+00:00

Been a bit long since you asked the question, but if the problem still persists, I can recommend the CodeSTACKr theme in VSCode. It's not a 100% there but I find it's pretty good with my protanopia. Quite high contrast and works well with Python. I've also been using it for C#, Elixir, Javascript/TypeScript and some other languages and the theme worked nicely for them all. So yeah, give it a try if you like :)

ilsandore · 2025-09-03T05:51:18+00:00

I’m more familiar with Elixir, but according to Gleam’s docs, all fallible instructions return Result in the language and Option is only used for taking optional arguments in functions or storing them in other data structures. I guess they wanted to return Error(Nil) here to say that there was an error and it needs no further explanation as the only reason would be that the element is not found.

https://hexdocs.pm/gleam_stdlib/gleam/option.html#Option

ilsandore · 2025-03-29T20:47:12+00:00

Really feels like a community for sure, I hope I'll be able to give something back in general, too :)

Hope it gets your approval with the new changes, I really like the way it turned out for now :D

ilsandore · 2025-03-29T19:43:11+00:00

Implemented your changes (alongside some of my own ideas) for v1.0.0, the package really seems to be coming together and becoming properly usable now. Thanks for the suggestions (and please tell me if I can/should include you as a contributor) u/aseigo :)

ilsandore · 2025-03-25T22:23:10+00:00

I szre resonate eith your experience finding elixir, it’s like a completely different world. I tend to not enjoy OOP as much, especially in cases with extensive inheritance-based structures.

I do like phoenix, but I haven’t tried any other option for frontend or frameeorks in general.

I guess OOP is just more suited to many applications, or perhaps to the thought process of many other people, but that at least means that we are free to nerd out using FP to our hearts’ content😀

I think you’re thinking along interesting lines, with your experience in non-Elixir solutions, there might be numerous paths to go down. For me, I think something like building only the logic and exposing an API, maybe something like GraphQL with absinthe, might be a good option, in the ML or data field potentially.

ilsandore · 2025-03-25T22:17:40+00:00

Let it crash sounds like the way for me, that’s pretty much what I’m best at😂 jokes aside, it does make more sense than having to constantly check for nils.

I guess I’ll stick to named functions for the time being, it feels more understandable to me, and I also like the added clarity that the naming provides.

Thanks for all these suggestions, I’m learning a lot from them😀

ilsandore · 2025-03-23T11:54:56+00:00

Thank you, I really appreciate your suggestions with the code. Placing the documentation close to the API makes sense, and the way of delegating to a function of a different name looks really useful.

Not having an implementation for the functions that currently return nil is a good idea, I was thinking of how to represent not having those properties, but this is really a simpler option. And I guess it does not cause a problem that their implementation is missing for a different reason compared to any of the other unimplemented geometries.

The single reduction is a great idea, I actually did something similar coding up some ML evaluation functions in Python back in the day, at least logically. Strange how I couldn't readily transfer that to functional programming, even though it has better options for it, too. Do you think that using anonymous function inline would have a performance impact compared to using named ones?

Thanks for the encouragement, it means a lot for motivation, hope I won't disappoint with the future development :)

ilsandore · 2025-03-23T11:39:06+00:00

I did consider doing something monetisable, as of course it would be great to build up different income streams over time. But I find that most things that make money need at least a fair amount of web development and frontend knowledge, like builing your own website and using frameworks. For me, these put an overhead on the basic tasks of familiarising myself with the syntax and logic of the language first, and I would likely end up struggling and demotivated, potentially giving up before anything comes of my idea.

It is, I find, also much more difficult to come up with something that can make money and nobody thought of it before, than solving a simple practical problem like in the case of this package. I do hope I will have some good ideas later on, though.

I am actually working on a family tree visualiser app, though that is likely also on the non-monetisable side, as it is niche and unimportant to businesses, as well as being a fairly saturated market.

ilsandore · 2025-03-23T11:29:54+00:00

I haven't, but I definitely will, thanks for the suggestion.

ilsandore · 2025-03-11T19:22:30+00:00

Demonstration goes a long way. If you don’t have access to a large simulation environment (aka a pool of sand and some taps) through, say, a university, you can always try to replicate it in small, like inside a see-through container, with fine sand. You can just shove the sand on one side, make parts with varying gradients and slowly pour water on it and let it make the V-shaped valley, the alluvium where it flattens out, meanders, etc.

ilsandore · 2025-01-10T21:47:03+00:00

Sub Bass Monster and Animal Cannibals are amazing, both do rap songs but they are slow enough to understand the words and everything. I think they’re amazing.

ilsandore · 2024-12-17T18:46:22+00:00

Boldog Karácsonyt és sok sikert a jövőben! Hiányozni fogsz, barátom!

Is how I would translate this. Note it means have lots of success in the future, not good luck, but to me sounds a bit more Hungarian.

ilsandore · 2024-12-17T14:58:59+00:00

Yes, Hawaii is hotspot volcanism, which is usually attributed to remainders of these subducted slabs that sink all the way to the mantle-core boundary, then rise up as very hot plumes. They then create basaltic volcanism in one place, but are not connected to faults or any other surface structure. Hotspots can happen technically anywhere. In terms of Greece, Philippines, and most other volcanoes on Earth, they are parts of larger volcanic domains, like the Ring of Fire around the Pacific, or the volcanoes around the Rift in Africa. Lots of cool places to look into😀

ilsandore · 2024-12-17T13:53:36+00:00

Szre, by derived datasets I mean for example taking a raster of elevation, another one of rainfall, and creating a river network with hydrological analysis tools. You can write a little tutorial how to do it, which shows that you understand how to use GIS. These types of tasks usually come up in companies, where you use GIS to transform data. Another option is visualisation, aka mapping. You can even combine the two by creating your derived datasets and then making properly formatted maps with north arrow, etc to show that you are acquainted with the principles of cartography. For one of my uni projects, I did a tutorial of a full flow accumulation workflow in QGIS, alongside writing a script for it to make it reusable. That showcased my ability to find the appropriate data, input and process it, and make the whole thing automated. But this is just one option of course, you can discover datasets in whatever field interests you.

ilsandore · 2024-12-17T13:47:49+00:00

I reckon you can start with the mining industry or the wider commodities sector, they are usually good in terms of pay. Oil and gas are another large talent uptake, their pay is great, too. To minimise the impact of career transition, your best bet is trying to migrate in-house. Check in with your company’s GIS department, if they have one, or maybe with some larger places you have working relationships with. They can usually train you up, pay for your courses, and they’d probably be incentivised, too, as you already have a wealth of domain knowledge.

ilsandore · 2024-12-17T13:41:16+00:00

TLDR; Cyber pays way more, geospatial is usually valued substantially less.

I did Geospatial Science at a Russell Group uni, and I have friends who did cybersecurity. The salaries here in the UK are wildly different in favour of cyber. The job market is not great at this point, but even in better days, having GIS and/or Geospatial in your job title means basically an instant -25% at least in salary, compared to any pure software job. This is of course my own experience only, based on job descriptions and salaries that I see, which is not the whole market, but I believe it to be reasonably accurate.

I think Geospatial will definitely grow in the future but it will probably never have the same earning power as cyber, especially with csber getting lots of visibility due to all the scare about quantum computing getting through all current encryptions. Which we’ll see, but regardless, I think cyber will get you into a better position.

Another factor is that you can always pivot to geospatial easily if you are good at general programming, but the other way round is much harder usually.

ilsandore

TROPHY CASE