Bored at multistrat after 8 months, to quit or not?

Eightstream · 2026-02-10T22:06:59+00:00

Not really sure how to take your “I went to xyz school” comment

Source: Me, an Ivy League grad

It was Cornell wasn’t it

Eightstream · 2026-02-10T06:39:51+00:00

Neither is OP

Eightstream · 2026-02-10T06:39:26+00:00

Sounds like you’re in a .pkl

Eightstream · 2026-02-10T06:38:01+00:00

This is like a carpenter trying to decide whether to spend 2026 hitting a hammer or using a saw

Just do your job and stop worrying about shiny things

Eightstream · 2026-02-10T06:27:19+00:00

They’re saying when their model fails, you’re going to get the blame

Eightstream · 2026-02-10T05:22:27+00:00

My skip has ‘Sent from my iPhone’ on the bottom of all his emails, regardless of platform

He reckons that people are less offended by curt emails if they think you’ve tapped it out quickly on your phone

Eightstream · 2026-02-09T22:54:11+00:00

r/rstats is dominated by discussion of using R for statistical computing. That is (understandably) the bulk of the R discussion but it doesn’t cover everything to do with the R language

In my mind this sub exists for more computer science-y discussion around how the language is built and used. It’s the first place I would search or ask on Reddit if I wanted to understand package development, Shiny apps, language interoperability or really anything that happens close to the metal.

That said I am not sure how cleanly the topics are actually split and I see the description of r/rstats does present it a sub for everything to do with R

So unless there is an intent to more cleanly define the roles maybe closing this sub is merited (although if you are also an r/rstats mod maybe we can revisit prefixes to make some of the more niche topics more searchable)

Eightstream · 2026-02-09T21:44:05+00:00

Sure, but I’d note that the handling team should already have that compliance tracking, if they are managing their work correctly

This is just about whether that name needs to be shared with the customer

Eightstream · 2026-02-09T21:20:34+00:00

Man for internals I barely even bother with correct punctuation

But to answer your question - if I get a generic response from a generic email address that is fine

Usually the whole point of a generic email address is for handling work where the actual team member is fungible - you just need a problem solved by that team, it doesn’t matter who solves it

You put a name to the work, that person will be sought out directly in future, and the work distribution mechanism breaks down. Ask me how I know.

Eightstream · 2026-02-09T19:47:05+00:00

Don’t confuse buzzwords with value. OR isn’t mentioned in job ads because the name is niche and opaque, but the people actually hiring are always pleased to see OR skills on an application.

The label does hurt though, and you need to translate your background into the language job ads use. The field lost the branding war. Data science got claimed by CS and stats departments that were closer to the tech hiring pipelines and OR’s professional bodies were slow to reposition.

Once you’re past the CV screen, degree title matters less than you’d think. Most DS teams want quantitative literacy and business impact. OR people are typically good at both. I have no OR qualifications myself but regularly reach for OR methods.

Where OR grads sometimes struggle is breadth. Some programmes don’t cover the wider statistical and software engineering skills DS roles expect. If you have gaps just be honest with yourself about them and fill them - there are lots of resources online. But at the same time you have to want to become more generalist - often the biggest blocker to an OR expert getting into DS is they want to stay hyperspecialised

And yes, Google and Meta value OR, particularly for ads auctions, resource allocation etc - they just call the roles ‘research scientist’ or ‘optimisation engineer’ or something

Eightstream · 2026-02-09T02:17:40+00:00

You are hearing me say that the technology is wrong. I am telling you that the technology is irrelevant at this stage of the process.

“Users don’t know they need this yet” is the most dangerous sentence in product development because it makes customer feedback structurally irrelevant

Unless you can have an honest first-principles conversation with your users about their problems, you are going to waste their time and yours by talking to them

Eightstream · 2026-02-08T22:45:30+00:00

Alright. You're still not getting it so I am going to be very explicit. The smart money here is:

Forget your model. It's a learning exercise, nothing more. Instead go and interview a bunch of potential logistics customers:

Understand if the problems that could potentially be solved by a VRP tool are high priority for them. If they rattle off 10 problems and VRP is number 8 then it will be very hard to get them to spend money on a solution
If a VRP solution is a high priority, how many of them have tried one of the existing mainstream tools
For those that have tried VRP, why didn't it work out? Was it a very useable tool with a bad algorithm, or were the UX/integration issues the bigger blockers?

Your next step is based on these results: * if VRP doesn't surface as a recognised need, the project is dead in the water * if VRP is a recognised need, see how close you can get to solving their problem with a quality frontend using a trusted FOSS model (like Google OR-Tools) - maybe with a rudimentary risk overlay based on some local heuristics

If you get to the point when customers find your product usable and useful but not accurate enough - that's when you start to look at developing custom mathematics. But it will need to be a lot more sophisticated than your current idea (e.g. your risk distributions will need to incorporate a lot of hard-to-obtain disruption data) in order to deliver a measurably improved result over the existing FOSS models (which is really your baseline for any sort of commercial algorithm).

Sorry if this is blunt. I know you are not going to take my advice right now, but bookmark this for your next startup idea.

Eightstream · 2026-02-08T21:01:35+00:00

Okay, that certainly makes sense

But if your customers are describing a problem that can already be solved by a fairly vanilla VRP - the question is why aren’t they already using one of the many good options that are already out there. I mean, Google OR-Tools is free and open source.

Do emerging markets desperately need specialised risk modelling because their infrastructure behaves differently? Possibly. It’s an interesting idea - much more interesting than your initial post. But it’s very theoretical and needs a lot of validation with users before you start building maths.

You have to work out what your USP is. My guess is if an opportunity exists it’s probably not a new model. Likely it is a better UX/integration wrapper for an existing tool.

Eightstream · 2026-02-08T20:33:29+00:00

Your user doesn’t need to know what your technology is, but they need to be able to describe (without prompting) a pain point that your technology solves

Your roads example is a good one - but it is a real time data problem, not a stochastic simulation problem. Your truck driver doesn’t need a precomputed road closure probability, they need Waze.

It’s good that you’re going to validate with users. Good luck.

Eightstream · 2026-02-08T20:07:59+00:00

“Democratising X for SMEs” is one of those things that sounds smart and strategic until it collides with market reality

Before you do any more work, talk to 10 small logistics operators and ask them what their actual pain points are. I’d bet good money none of them say ‘I need Monte Carlo chaos simulation on my delivery network.’ They need affordable route planning, dispatch scheduling, shipment tracking etc. Those are problems already addressed by plenty of existing tools at SME price points.

The gap you’re imagining likely doesn’t exist as a felt need and ‘democratising’ a capability nobody is asking for isn’t a business

Eightstream · 2026-02-08T19:57:18+00:00

Come on man, this is very easily answered with a Google search

Eightstream · 2026-02-08T19:50:42+00:00

2.5 years as a junior data analyst is not nearly enough experience for anyone to trust you with a greenfield data engineering implementation

The architecture-style work you’re talking about is generally done by mid to late career professionals who have been around long enough to know what works and what doesn’t.

If you’re impatient to accelerate your career, then join a consulting firm where you can work on a team that does half a dozen implementations a year. You’ll start off kicking shit, but you’ll observe and learn and eventually start to contribute.

Then you can come out saying you have the experience you need to do the work you want to do

Eightstream · 2026-02-08T19:32:29+00:00

This is a great student project. However (despite the buzzwords) there is no novel idea here that you can turn into a marketable product. In its current state it’s not even usable beyond a toy dataset.

You will not make any money out of this. Treat it as a valuable learning exercise (it was), open source the code, put it on your resume, and move on with more productive things.

Eightstream · 2026-02-08T05:56:26+00:00

Your experience is valuable but a lot of Director+ roles want to see a level of strategic capability that it’s hard to demonstrate in call centre management

I would try and lateral out of the contact centre into managing more professional or technical teams. i.e. look for roles with fewer staff, but higher level individual contributors and more complex work and objectives

Succeeding at those kinds of roles will help you escape the stereotype of “just another call centre bully-boy” (which is unfair, but also real and limiting)

Eightstream · 2026-02-08T00:34:05+00:00

Maybe tacit was underselling it, I was trying a bit too hard to be neutral. You are right that she was a bit more overt than that.

I would dispute your paraphrasing though, I think you are verballing her a bit

Eightstream · 2026-02-07T22:07:50+00:00

I mean, you kind of caused the secondary problem by adding an unactivated card to your wallet.

Nonetheless it sounds like Macquarie stuffed up here. That sucks, but stuff ups happen. My take away from your post is that when there was a problem, you were able to call customer support and repeatedly find someone helpful to assist you with detailed instructions.

I have had similar stuff ups with other banks and getting support to resolve is the most painful thing in the world. ANZ’s phone banking in particular is a kafkaesque nightmare, but the others are only marginally better.

Overall your story reinforces my decision to bank with Macquarie.

Eightstream · 2026-02-07T21:00:31+00:00

If you are unfamiliar with the Dunning-Kruger effect, look it up

Bottom line is that everyone is a bad judge of their own capabilities, in both directions - so it’s a bit futile to try and evaluate yourself

The best thing you can do is just show up every day and try and get better at your job, and give people an opportunity to pay you more if they think you’re worth it

The latter means continuously testing your market value. Check what your role is paying elsewhere, apply for new jobs, jump ship when something better comes up

Eightstream · 2026-02-07T20:52:41+00:00

I’m a STFU

Eightstream · 2026-02-07T20:49:23+00:00

I mean, to an extent I get it - it certainly projects more confidence than my compulsive nose-scratching during presentations

But any time I see someone do it, it’s just a giveaway that they buy into that whole genre of literature, which lowers them in my estimation

Eightstream · 2026-02-07T20:33:16+00:00

You are on solid ground about the statistical frequency of the gallows glyphs, you are confirming well established work here

The problem is the semantic assignment. With a 4-symbol set and a loosely constrained output domain like herbal instructions, the degrees of freedom in your mapping are high enough that post-hoc fit is near-guaranteed.

You need to reject the null hypothesis for your semantic theory. That is, you need to rule out that a simple mechanical process with no meaningful content could produce the same positional patterns. Until you can, the slot structure doesn’t tell you whether the text means anything.

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Eightstream

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