u/According-Salad-6448

This is a big rant so pls hold on 🙏🏽

So after months of interviewing I finally have an offer in hand and I wanted to share, partly to celebrate and partly because I’ve got a real question at the end.

Quick background. I’m 26, about 3 years of experience, mostly in analytics and operations. My work has been SQL, dashboards, cleaning up reporting, figuring out why a business number moved, that kind of thing. Economics background, not computer science.

The process for this one was long. It went case study round, then a couple of interviews on the role and my experience, then a 48 hour take home assignment (a business case plus a technical test), then a data and consulting style discussion, and finally the offer call. Multiple rounds over several weeks. There were points where I genuinely thought I’d fumbled it, especially the take home. So getting the yes felt huge.

It’s also a big jump for me. The role is a move from just analyzing problems to actually building the automated fix for them, roughly half data analysis and half building automation workflows, with AI increasingly used in the decision making, in a large customer operations setup. On top of that it’s an international move and a serious jump in pay from where I am now, close to a 5x on paper. So on the surface it feels like a clear win but like I wouldn’t say I’m happy, it feels like I’m done with a task, nothing that would make me fulfilled but money is important and i can finally give back to my parents, I don’t know after what point I’ll be happy about where i am but it’s never enough and I don’t think it ever will be

Coming back though I’d love honest input from people who’ve actually lived it.

I’m getting into this niche of analytics plus automation plus applied AI, basically encoding business decisions into workflows and using AI to make them smarter. My worry is whether this is actually a transferable and scalable skillset, or whether I’m getting good at something that’s tied to one company’s tools and doesn’t travel well. I know it’s not core data science or ML research.

So if you work in this space, is it a strong long term bet? Does it travel across companies and industries? Where does it cap out? And is there anything you wish you’d known before going deep into it?

Hi all,

Looking for honest insight from anyone working at the intersection of analytics + automation + Al applied to real business operations, not ML research, more the "encode business decisions into automated workflows and use Al to make them smarter" side of things.

My background (my archetype):
I'm almost 26 now, 3 YOE. My experience so far is analytics and operations, SQL, building dashboards and reporting pipelines, diagnosing why a business metric moved, cohort/segmentation work, and automating manual reporting. Basically the "find the problem in the data and make it visible" type.
Economics background, not a CS degree.

Where I'm heading:
I'm about to start a role that's roughly 50% data analysis, 20% building automated decision workflows (low-code + some JavaScript), and increasingly using Al to automate parts of the decision making in a large customer-operations context. So I'm shifting from analyzing problems to building the automated fix for them, at scale.

What I'm trying to figure out:

1. For those in this niche (ops automation / decision automation / applied Al in workflows) how transferable has it been for you across companies and industries? Does the skillset travel well, or does it get tied to specific tools/platforms?
2. Coming from an analytics not engineering background, what actually mattered to learn to be good at the building side? Where do people like me usually struggle?
3. What should I deliberately build/collect over the next 1-2 years to make this a strong, portable profile? (Projects, quantified outcomes, specific skills?)
4. Honestly like where does this path cap out or fall short? I know it's not core data science/ML.
   Anything you wish you'd known before going deep into it?
5. Is applied Al in this space (using LLMs/Al to drive workflow decisions) actually a durable skill

Any opinions, war stories or suggestions - anything is welcome

Hi all,

Looking for honest insight from anyone working at the intersection of analytics + automation + AI applied to real business operations, not ML research, more the "encode business decisions into automated workflows and use AI to make them smarter" side of things.

My background (my archetype):
I'm almost 26 now, 3 YOE. My experience so far is analytics and operations, SQL, building dashboards and reporting pipelines, diagnosing why a business metric moved, cohort/segmentation work, and automating manual reporting. Basically the "find the problem in the data and make it visible" type. Economics background, not a CS degree.

Where I'm heading:
I'm about to start a role that's roughly 50% data analysis, 20% building automated decision workflows (low-code + some JavaScript), and increasingly using AI to automate parts of the decision making in a large customer-operations context. So I'm shifting from analyzing problems to building the automated fix for them, at scale.

What I'm trying to figure out:

1. For those in this niche (ops automation / decision automation / applied AI in workflows) how transferable has it been for you across companies and industries? Does the skillset travel well, or does it get tied to specific tools/platforms?
2. Coming from an analytics not engineering background, what actually mattered to learn to be good at the building side? Where do people like me usually struggle?
3. What should I deliberately build/collect over the next 1–2 years to make this a strong, portable profile? (Projects, quantified outcomes, specific skills?)
4. Honestly like where does this path cap out or fall short? I know it's not core data science/ML. Anything you wish you'd known before going deep into it?
5. Is applied AI in this space (using LLMs/AI to drive workflow decisions) actually a durable skill, or is it hype that'll commoditize fast?

Any opinions, war stories or suggestions - anything is welcome

Hi all,

Looking for honest insight from anyone working at the intersection of analytics + automation + AI applied to real business operations not ML research, more like "encode business decisions into automated workflows and use AI to make them smarter" side of things.

My background:
I have 3 YOE. My experience so far is analytics, reporting and operations SQL, building dashboards and reporting pipelines, diagnosing why a business metric moved, cohort/segmentation work, and automating manual reporting. Basically the "find the problem in the data and make it visible" type. Economics background, not a CS degree.

Where I'm heading:
I'm about to start a role that's roughly 50% data analysis, 20% building automated decision workflows (low-code + some JavaScript), and increasingly using AI to automate parts of the decision-making in a large customer-operations context. So I'm shifting from analyzing problems to building the automated fix for them, at scale.

What I'm trying to figure out:

1. For those in this niche (ops automation / decision automation / applied AI in workflows) how transferable has it been for you across companies and industries? Does the skillset travel well, or does it get tied to specific tools/platforms?
2. Coming from an analytics and not engineering background, what actually mattered to learn to be good at the building side? Where do people like me usually struggle?
3. What should I deliberately build/collect over the next 1–2 years to make this a strong, portable profile? (Projects, quantified outcomes, specific skills?)
4. Honestly where does this path cap out or fall short? I know it's not core data science/ML. Anything you wish you'd known before going deep into it?
5. Is applied AI in this space (using LLMs/AI to drive workflow decisions) actually a durable skill, or is it hype that'll commoditize fast?

Any war stories and honest takes would genuinely help, thankssss

I’m a 26-year-old from Bangalore, India, about to relocate to Bangkok for a role. Base is 120,000 THB/month plus performance based bonus of 19%. Really excited but also a bit nervous since this is my first time moving abroad and living alone in a new country, so I’d genuinely appreciate honest input from people who’ve done it.

A few things I’m trying to figure out:

1. Saving and cost of living. My main goal is to send around 60,000 THB/month back home to family. That leaves me roughly 40–45k to actually live on after tax. Is a comfortable life on 50k THB/month realistic in Bangkok, or am I being optimistic? Not looking for luxury just a decent 1BR, eating well, getting around, occasional going out.

2. Where to live. I’ve heard Asok, Sathorn, Ekkamai, Onnut, and Rama 9 come up a lot, especially for people working in the city. Which areas give the best balance of reasonable rent + good commute + expat-friendly? Anywhere you’d avoid?

3. Rent reality. What does a decent 1BR actually cost in those areas right now? Trying to separate real numbers from listing prices.

4. Culture and settling in. How was the adjustment as a foreigner, especially coming from India? Anything about day-to-day life, making friends, or workplace culture you wish you’d known earlier?

5. Learning Thai. How far can I get with basics, and how long did it take you to reach a level where daily life got easier? Is it necessary, or do you get by fine in English in Bangkok?

Any other advice for someone about to make this move would mean a lot. Thanks in advance 🙏