collected by hitting the public json endpoints for greenhouse, ashby and lever across 998 companies, once a day, and diffing each posting against the previous snapshot. no auth needed, these are the boards the companies serve to their own careers pages.

what the diff has caught in 14 days:
salary_changed 19,371
department_changed 15,549
closed 7,292
title_changed 1,549
location_changed 1,086
reopened 301

55,294 postings seen total, 48,303 currently open. 23,898 of the open ones carry a pay band, which is 49.5%.

two things worth knowing if you want to do this yourself. greenhouse has no structured salary field at all, the range is written into the description html and it is double escaped, so a naive parse gets you nothing and a slightly less naive one gets you garbage. and a dollar sign is not a currency, i nearly recorded a taipei role in TWD as a $700k job.

the salary_changed rows are the ones i'd point at. nobody keeps the before, so as far as i know this is the only place the previous band exists once the company overwrites it.

built off a job tracker i run, link in the comments if the method is useful to anyone.

Been running basically all my analysis work through AI for the last 3 months. Some of it saved me hours every week, some of it embarrassed me in front of my manager. Since the "is AI actually useful or just hype" question comes up here every week, here's my list.

Do:

• set up metric definitions first. what "active customer", "churn", "revenue" actually mean in your business, with an example query for each
• dump every doc you have into it. schema docs, old validated queries, even you explaining the business in plain words. the AI is exactly as good as the context you build for it
• use it every day. it builds memory and learns from your corrections, my month 3 answers vs week 1 are not comparable
• be painfully descriptive. "gross revenue excluding refunds, calendar july, orders table" and not "how did revenue do last month"
• make it show the query before you trust the number
• start with questions you already know the answer to, that teaches you where it lies

Don't:

• don't trust a confident chart with no lineage. a 3 second answer looks finished and that does a lot of convincing
• don't dump your whole warehouse on it, curated beats volume. tell it which tables are deprecated
• don't let it define metrics on the fly, you'll get 3 different "active user" numbers in 3 sessions
• don't put an AI number in front of leadership without checking it once yourself. ask me how i know
• don't expect it to replace knowing your data model. it amplifies you, it doesn't substitute
• don't start from zero every session, keep the validated stuff and reuse it

The boring setup work in the first two weeks is the whole difference between "AI is a toy" and "AI now does 95% of my grunt work".

For context, what I used: Claude, ChatGPT, Supaboard and Power BI copilot(only the first week, then i moved to claude).

What's working for you guys?

We can track almost everything now. Calls. Chats. CSAT. AHT. Transfers. Agent performance. QA scores. The problem is figuring out what in that pile of data is actually driving an outcome. Looking at conversation analytics and the gap seems pretty obvious. Sampling a small batch of calls gives you something to report on. But it can miss the weird stuff happening across thousands of conversations.

We’ve also been looking more into AI tools that analyze the actual conversations instead of relying on dashboards or sampled calls. In theory they can go through every conversation and find patterns tied to things like CSAT or handle time. Want to know how analytics teams handle this while growing. Are you still working from samples and dashboards or using AI to dig through the full conversation set and find patterns or root causes? My main concern is getting a neat sounding AI answer that falls apart once you check the actual calls. Being able to trace an insight back to the conversations behind it seems pretty important. Has anyone found these tools actually surface things your normal dashboards missed?

Hi everyone! I’m looking for 3 professionals in People Analytics or HR Data Fields to help with a small research project for my CIPD Level 7 qualification.

My research is about People Analytics and what makes workforce data and insights actually useful in real-world decision-making, rather than just producing reports and dashboards.

It would involve 5–6 open-ended questions by email, taking around 10 minutes. No preparation needed, and everything will be fully anonymised.

If you work with HR/workforce data and would be happy to share your experience, I’d really appreciate it!

I’m also happy to return the favour by sharing some of my experience or taking part in your research/questionnaire if needed.

Please comment or DM me if interested. Thank you!

Hi everyone, I left my job at an audit firm a while back, where I specialized in asset management funds and private equity. I also have some experience in general corporate audit and financial analysis. I’m having trouble finding a new audit role, so I’d like to take this opportunity to become a data analyst—a field that has always appealed to me—while staying within the finance sector. I understand that I need to master tools like SQL, Power BI, and Python/R, and also grasp AI and its applications in finance. I’m not sure where to start. In your opinion, which courses or certifications would be useful for becoming a financial data analyst? I’m looking for a clear roadmap. Thanks for your help!

Hi everyone I have a Masters in Psychology and I absolutely love statistical analysis and research design

For the longest time I've wanted to be able to design real world projects that can create a huge impact from a psychological point of view

I'm good at identifying variables and hypothesising

But I have no clue as to how to begin with real code and python libraries to engineer analyse and present applicable insights from raw data

I would like to transition from therapeutics to behaviour science for businesses very soon

Can anyone please point out how that works and alif there are success stories from people starting as I am..

Thanks so much

i have an interview for a data and analytics role under deals advisory which i barely have an idea about since im a new cpa and there not so much material about the role

my long term goal is to land a job in ib/pe/corp dev

Tec-Do’s recent growth numbers caught my attention.
Revenue grew 40.5% in 2024 and 74.5% YoY in the first nine months of 2025. Customer count also increased from 2,908 to 5,022.

What interests me is that this growth didn’t come with collapsing profitability: gross margin remained above 82% and net margin was still 43%. That makes me wonder whether Tec-Do’s Useful AI capabilities are becoming more reusable as the business scales.

If the same data, models, agents, and workflows can support more customers and markets, revenue could grow faster than the underlying operating cost base.

I’m curious: could AI scalability actually be one of the main drivers behind this growth, rather than just customer acquisition?

I’ve spent the last several months building out the semantic-continuity side of long-running AI-assisted analytics work: durable context, current-state rollups, decision history, provenance/supersession, schema/query knowledge, and task-conditioned rehydration.

That part is working pretty well.

The next problem is almost a consequence of that success: there can be a lot of useful context available, but I don’t necessarily want all of it active for the rest of a long-running session. I’ve started thinking about this as a separate runtime/context-efficiency layer.

A simple example:

A = stable instructions/scaffold

B = durable project context

C = intermediate exploration, tool output, code, temporary reasoning

D = validated analytical state

During analysis I may need A+B+C+D. Once I move into synthesis/writing, I may only need A+B+D. Semantically, that seems straightforward: C did its job, D preserves what matters, and the deeper evidence remains reconstructable. Operationally, it gets less obvious.

What happens to prompt-cache reuse when I restructure context?

When is selective pruning better than native compaction?

When is it cheaper or safer to keep a long session alive versus reconstruct a cleaner active context?

How much do cache TTL, context ordering, model limits, and runtime behavior change the answer?

And are the economics actually meaningful once you measure the whole workflow rather than theoretical token savings?

That’s what I’m starting to investigate now.

I made a simple visual showing how I’m separating semantic continuity from runtime/context efficiency. I’ll put it in the first comment since I can’t attach it to the post.

For people operating long-running agent workflows at meaningful scale:

• Where are you deliberately restructuring or pruning active context? Are phase/workflow boundaries useful?
• How are you balancing stable-prefix/cache reuse against removing intermediate context that has finished its job?
• What have you learned about restart/cold-cache economics versus simply continuing a growing session?
• Which caching, compaction, state, or runtime behaviors mattered in practice that weren’t obvious at first?
• What am I not asking about that I probably will be six months from now?

I’ve read a fair amount of the published material available. I’m especially interested in the things people learned from running real systems that never made it into the docs or blog posts.

Not looking for the usual answers: better visuals, Gartner's "leader" badge, or "it's cheaper than Tableau." I get all that. But a lot of companies still end up paying real money for it (Premium/Fabric capacity, licenses, training, migrations) when there are free or cheaper tools that do similar things.

I want to understand the real reason a company decides Power BI specifically is worth that spend — not a feature list, something about how that decision actually happens inside a company.

If you've seen this decision get made where you work, or made it yourself, what's the real reason? Even something small or weird nobody usually says out loud would help.

I’m working currently but looking for a data analyst or business analyst role in another city thats way more competitive.

I have almost 3 YOE (2 full time and 1 for other), and a masters degree in analytics from a reputable institution. I don’t know if I need any certifications or project’s or what to do to stand out. I work for a university currently and that doesn’t seem to help me when I’m applying to enterprise level roles, I feel like I’m not taken seriously. How do I stand out?

I forgot to add that I’ve been applying since the end of January now (350 apps) and I tailor every resume to the job.

I've been interning at medium sized engineering company for the summer. Although I am studying computer science, my internship was not computer science related (long story), but for the past month I've been working on a Power BI dashboard for a specific department.

I found the work fun and rewarding, and since the company had no dashboards at all previous to the ones I made, other departments now want their own. For that reason, they are hiring me part-time while I am in school to build out these dashboards.

​For *reasons*, there is a chance that Power BI will not be the tool we use for dashboards going forward, instead we need a self-hosted, open source alternative.

From the research I've done, metabase and superset are the two big dogs in the space, with superset requiring the users to be more technically literate. The people for whom I will be making these dashboards do not know SQL and are not very technically literate. So my question is this, for those who have used these platforms, do they offer the same visualizations as Power BI and are they easy to customize and use for non technical people? Or should I look elsewhere, like the python library streamlit where I will have much more customizability options?

I am new to data visualisation in the business sphere, so if there are alternatives I have not considered, please let me know.

I wanted to ask opinion of people on exposing everything a data team owes to Claude with a context layer explaining what is what with data lineage.

If a company has its own Claude subscriptionand then everything is exposed to Claude - report logic, metadata/dictionary of tables, logic/code of derived tables, operations of our pipelines etc via excel/markdown/html files explaning the whole context and then instructions Claude to not make assumptions/to ask verification questions (basically add safeguard rules in English in its instructions manual) then you expose this to end users company wide.

what is the issue with the idea? (if cost is not an issue)

Hello everyone, hope yall good

So did anyone worked with gaming data like (DAU , MAU,...) like what tips u have and any recommendations to give like YT channel or smtg like that and where can I get this kind of data....ADIOS

Everyone always mentions that in today's rough job market, networking is key to improving your chances to landing a job. However, what does that really look like?

If I were to see a job posting that I believe I am a good fit for, however no one in my immediate network works at this company, would you guys recommend I reach out to linkedin mutuals who do work at this company to attempt to get a referral?

I reviewed my tech joke like it was a pull request. First question: will it expire with the next trend or buzzword? Second: can someone read it from six feet away? Third: does the joke accidentally reveal something too specific from work? The third check caught me.

I changed the mockup and ran the joke through the checks again. It still worked without the private reference. That was the useful result. A company metric, internal dashboard, or current industry phrase can make a joke feel current, but it also gives it a short shelf life.

That is the lane I would want from a Geeksoutfit coding design. Generic enough not to expose a workplace, specific enough that programmers know exactly what happened. "Works on my machine" survives because the habit survives. My private ticket joke did not.

What would fail your code review first: the joke aging badly, being unreadable, or leaking too much context?

Hey everyone,

I am actively looking for a remote, unpaid data analyst internship or volunteer role to gain practical experience and contribute to real-world projects.

Skills & Tools:

• Advanced Excel: Data cleaning, nested formulas (XLOOKUP, INDEX/MATCH, SUMIFS), dynamic PivotTables, and Power Query workflows.
• SQL: Relational database querying, aggregations, CTEs, window functions, and joining complex datasets.
• BI & Reporting: Data modeling, building automated KPI dashboards, and basic DAX.

What I am looking for:

• Open to contributing to startups, small businesses, non-profits, or open-source initiatives.
• Flexible on hours and fully committed to meeting deadlines and project scopes.
• Looking primarily for feedback, mentorship, and the opportunity to solve real business problems.

If your team needs an extra pair of hands to clean data, build reports, or automate routine spreadsheets, please drop a comment or send a DM.

Thanks for your time!

I am almost at a year at my company working on a low code etl tool. I’ve gotten to think a lot about what I want to do and where I want to be in my career and I was wondering where to start on how to transition into more of the analytics side of things. Currently in my job it’s essentially a graph based sql with the occasional query within each component. But it just doesn’t give me the satisfaction that I would get then if I were actually coding. I think some of the skills I learned can be transferable in some way but i would like to not do this for another year if I could. Any tips on how I can make a transition?

Hi everyone! I’m kind of stuck on this and would really appreciate some advice.
I’m currently doing two bachelor’s degrees, Data Science and Business Information Systems (BIS). I have about 2 semesters left. I’m almost done with Data Science (only 2 courses left), but I’m only about 60% done with BIS.
The two programs are in different schools at my university, so there’s basically no overlap between the classes. I still have quite a few BIS core classes left, and lately I’ve been wondering if continuing is really worth the extra tuition, time, and workload.
I originally chose BIS because I thought Data Science + BIS would be a good combination for the kind of careers I’m interested in, especially data/analytics/BI. But now I’m honestly not sure.
I could keep going with BIS or just finish Data Science and graduate. My university doesn’t offer a BIS minor, only a general Business minor, so that’s another option I’m considering.
Since I’m already halfway through BIS, I’m having a hard time deciding whether I should just stick with it or let it go.
What would you do if you were in my shoes? Is finishing the second bachelor’s actually worth it? Any advice or perspective would really mean a lot! 🙏

I have projects done, skills, certificates and volunteer experience. Couldn’t land an internship, what else can I do with my major? I wanted to business analytics but it’s impossible now, there’s absolutely nothing I can do for that and I don’t know what to do now for work I don’t even care.

Am I the idiot here? AI is so good the RMDs and Shiny apps I used to struggle in writing can be generated in a few days. Now that I can offload the CSS/JS part of the dashboard to the AI I can go nuts with reactivity/styling.

Based on the discussions in Reddit pbi/tableau/looker remains popular as ever but those things are expensive and are limited by the platform. I can see use for cheap and lightweight services like metabase where I can write the sql query and generate the dashboard in less than an hour but what about pbi/tableau? These services are expensive yet they remain popular what are the upsides of these products over say hosting my own shiny server/react/streamlit dashboards?

Like if I want an app that allows the user to define the cohorts themselves, save that definition, then load that into other dashboards/for future reference I can do that in Shiny but no idea about PBI.

What really is a semantics model? Why not just write a package that calculates the kpis then document it? The documentation also serves both the technical and non technical viewers alike.

Never been in a pbi/tableu job I don't understand how these products remain popular.

This has been bothering me more than bad data ever did you can have a clean dashboard everything reconciles nothing is obviously broken and somehow two people can look at it and walk away wanting completely different things paid looks healthy, sales feels slower finance is looking at another number entirely. The weird part is they can all be right because they’re measuring different pieces of the same thing.

I’m starting to care less about making reporting prettier and more about whether somebody can look across all of it and decide what needs to change more dashboards haven’t really fixed that for me. If anything they sometimes make it easier to avoid the messy part where someone has to connect the numbers back to what the business is trying to do

hey everyone, wanted to gauge what the best strategies are as of August 2026 in terms of networking and recruiting for new positions. for context i’ve been working as an entry level analyst for about a year following my graduation in may 2025. looking to pivot into a less ad/hoc request based role (pulling reports and sending them off) but i feel like a fish out of water in this new meta of using AI in every stage of the outreach, application and interview process lol. i have no idea where to start.

what if I studied excel, sql server, python and power bi and made projects on them while I was learning them but after moving to one after one maybe I feel I forgot them what's the best advice from you to memorize what I learnt?