I work as an analyst for local government, and on the whole I do really enjoy my job. However, now I’m approaching my mid 30s I’ve become more worried about what the long term future looks like. In some careers (law, engineering, surveying etc), your value seems to increase as you age and knowledge increases. When my Dad was made redundant age 56 (telecommunications engineer) he had a call the very next day from one of their contractors offering a well paying job.

Meanwhile in another public sector organisation we work with a lot, about half their analyst team was made redundant last year.. and their old team lead in his early 50s can’t find anything. No calls, no recruiters calling him up. In my last career in insurance, you’d learn something new from the people in their 50s all the time. I sat next to a woman in her late 50s, and often had to ask for advice on how to approach something complex. It just isn’t the case in analytics in my experience.

With the way AI is developing, and how data analytics involves constant learning due to how technologies always change, I feel the career path involves a lot more grinding to stay on top rather than accumulating knowledge over time.

Perhaps I’m just being pessimistic, but recently seeing these redundancies, hearing about the poor job market, and seeing how the people in their late 20s and 30s are probably the most technically skilled, does make me wonder how things will go in the future with my own career.

Context: I am the marketing analyst for a massive, global company. They have thousands of employees, endless layers of middle management, and a bottomless appetite for "data-driven insights" that nobody actually looks at.

I’m hitting a wall, and I need to vent/get some advice before I just start sitting on my hands until its asked for 3 times.

Here is the weekly cycle:

• I get a frantic ping at 4:00 PM on a Tuesday. "We need a deep dive on X. We needed this yesterday for a leadership sync."
• I drop everything, pivot, clean the messy data, build the visualizations, provide some insights on the results, and send it over.
• No "thanks," no follow-up questions, nothing.
• Two weeks later, the person who "needed it yesterday" is ASKING FOR THE SAME THING. It's demoralizing.

How do you guys handle this?

I’ve tried the "value-based" approach where I ask what decision this data will drive before I build it, but they just give me some corporate buzzwords about "visibility."

Back when I was an individual contributor, things were simple. I opened my laptop, drank my monster energy, and dove into a dashboard. My biggest challenge was figuring out why the campaign table had 14 different definitions of “spend.” Life was beautiful.

Now I spend most of my time managing “stakeholder expectations,” navigating the political landscape like I’m playing 4D chess with people who’ve never opened a dashboard but have strong opinions about color palettes and KPI definitions.

I used to optimize media mix models. Now I optimize the wording in Teams messages so I don’t step on toes. I used to A/B test landing pages. Now I A/B test how direct I can be in a meeting without someone getting concerned about my tone. I used to ask “What does the data say?” Now I ask, “how are we going to bs the talking points this week”

Sure, I make more money now. I have a nicer title, I’m in meetings with leadership, and my calendar is a Tetris board of strategy sessions, alignment check-ins, and recurring “quick syncs” that never end quickly. But I don’t touch data anymore. My brain doesn’t light up solving a tough query. It flickers nervously trying to remember which VP is quietly feuding with which other VP.

Somewhere along the way, the craft got buried under the politics. And yes, I chose this path. I wanted to grow. But I can’t help missing the days when I had zeal. When I opened up a Jupyter notebook and felt excitement, not existential dread.

Now I just forward emails and write one-pagers with sentences like “We’re working cross-functionally to ladder insights up to the business goals.”

God help me.

Anyone else feeling this?

Hey, fellow analytics/BI folks,

Just reaching out for support, I think. On Monday, the organization I work for (US) just did another round of mass layoffs, and this one swept me up.

It'll be my third layoff (all in this field), with my second layoff having occurred just last year, on my birthday no less.

My wife and I have a baby on the way in a couple months (we have other grade school kids, too).

Last year, during the last layoff, I was battling with pushing past depression and eventually(/luckily!) landed on my feet with a much better role than the one I'd been severed from.

I'm hoping I can bounce back like that again, but I'm feeling the dread building up.

At any rate, thanks for any words of advice or support, all.

I recently joined a company as an operations executive. While my initial goal was to work as a data analyst, securing this role was challenging due to my non-technical background. As the saying goes, "Beggars can't be choosers," so I accepted the opportunity.

Upon joining, I noticed that many tasks were being done manually, even though they could easily be automated using basic Excel formulas. For example, my colleagues were manually counting and transferring filtered data from one sheet to another. While I was impressed by their speed and efficiency with Excel shortcuts, the process still seemed time-consuming and prone to errors. With the help of ChatGPT, I created an Excel formula to automate this task, making it about 10 times faster and more accurate. However, my team leader didn’t seem pleased with my initiative. He has extensive experience with Excel and is usually the go-to person for troubleshooting, so I suspect he may have felt undermined.

It’s been 17 days since I joined, and my primary responsibility is to review daily data in an Excel file (around 50,000 rows x 11 columns) and compare it with a master file. The expectation is to complete this task within an hour, which feels unrealistic given the volume of data. So far, I’ve managed to do it in about 1.5 hours. To streamline this process, I spent my entire weekend writing a 1,600-line script with the help of AI, which automates most of the task by defining ranges and conditions.

While I’m proud of the effort I’ve put in, I can’t help but feel that the company doesn’t fully appreciate the value I’m bringing. The pay doesn’t seem commensurate with the level of work I’m doing, and the lack of holidays (like Holi) has been disappointing. I’m also concerned that if they find out about the script, they might simply assign me more tasks instead of acknowledging the efficiency I’ve created.

apologies if this is inappropriate - i don’t know who to share this with who understands the relief and ecstasy i’m feeling currently

i (21F) have been with my boyfriend (24M) for a little over 3 years. he graduated in management information systems and a ds minor as valedictorian of his major in 2023, and has been stuck in the job application rut for the last 3 years. after a year straight of self boredom via SQL dashboards & tableau projects, he applied for MS programs and began completing the georgia tech ms in analytics degree while applying, which he’ll be done with in december.

13,456 applications later, he got the call today. incoming analyst - data science at a major fintech in new york! so proud of him, as he knows, but please don’t lose hope if you’re also stuck in the endless and seemingly unfruitful phase of wrestling with this horrendous job market. there is light at the end of the tunnel, even if you had 0 internships, much experience, or went to an oversaturated undergrad.

I cannot continue to be poor. I cannot enter my 30s with no career making shit money living paycheck to fucking paycheck. Not after all the hard fucking work I've put in and all the suffering I've had to experience just to get my fucking education.

MA Mathematics, Certificate Computational Linguistics - A university

AS Data Science and Computer Science - A community college

Certificates in Java and SQL/Database Development - A community college

Data Analysis: Python, SQL, Excel, Snowflake, PowerBI, Tableau, Data Visualization, Natural Language Processing, Large Language Models

Why isn't this enough to get an entry level job? Even with relevant work experience? I get interviews, sometimes I get deep into the process. One job interviewed me SIX TIMES. NO OFFER. WHAT DO I DO. I cannot continue like this with no future and no job prospects.

I’ve been trying to switch jobs for almost 2 years now, and I’m genuinely starting to wonder what I’m missing.

I have 6+ YOE in data/financial services, working primarily with SQL, Python, Snowflake, data pipelines, data quality and automation.
I’ve interviewed with companies like Bloomberg, Citadel, Tesla and Intuit, made it through multiple rounds including technical/coding interviews, and there have been several where I walked away feeling like I did really well.
I also ask for feedback whenever possible. Multiple times I’ve heard some version of:
“You did well, the team liked you, but we decided to move forward with another candidate.”
I understand that happening once or twice. But after almost 2 years of getting close and hearing similar feedback, I’m starting to question what I need to change.

For people here who’ve been hiring managers/interviewers, what usually separates the candidate who did well from the candidate who actually gets the offer?
Is it technical depth? Communication? Domain experience? Am I targeting the wrong roles at 6+ YOE? Or is the market really just this competitive right now?

Also shooting my shot — if anyone’s team is hiring for Senior Data Analyst / Data Management / Data Engineering-type roles, especially around NYC/NJ or in
fintech/financial services, I’d really appreciate a DM.

Happy to share my resume.
Would genuinely appreciate any advice, criticism, leads, or perspective.

I’m so curious about this post. I saw someone screenshot it and by the time I got here to check it out, it was removed.

Why was it removed?

What were the details? What type of AI was being used and what types of details were being fabricated?

Hi,
i’m a rising college junior and as i’m writing this post right now it’s currently 7 am and i can’t sleep because im having so much anxiety.

I start my junior year next week and I feel like i’ve done nothing my entire time in college. I’m going to be a TA for my SQL class i took last semester, i was a TA for my OOP class i took freshman year, i had a policy internship where i was basically doing consulting/internet research, and i have very basic projects on my resume claude could code in 5 minutes.

I don’t know what to do. I’m messaging professionals on linkedin for either a coffee chat or just some advice for applying but they either don’t respond or they tell me to apply on their website.

Reading all the posts on here i’m concerned if I should even stay in this field because i’m clearly not trying hard enough and i’m scared

sorry for the rant

I’ve been thinking about this question a lot while looking for my first opportunity in Data Science / Data Analytics.

Is the ideal candidate someone with a perfect degree?

Someone with 3+ years of experience?

Someone who knows every tool listed in a job description?

Or is it someone who can identify a real business problem, build a solution, and explain how that solution can create business value?

I’m genuinely curious to hear what recruiters and hiring managers think.

Because this is what I’ve been trying to do.

Instead of building another basic ML project, I built a Customer Retention Intelligence System focused on a real business problem: customer churn and lost revenue.

The system can:

• Predict customers who are at risk of churning

• Segment customers based on their risk/value

• Estimate potential revenue at risk

• Identify customers worth prioritizing

• Generate personalized retention strategies/offers

My goal wasn't simply to say:

I built a machine-learning model

I wanted to answer:

A customer is likely to leave. Now what should the business actually do about it?

That mindset has pushed me to work beyond just Python, SQL and machine learning — into business thinking, customer analytics, experimentation, visualization, and decision-making.

I’ve also been consistently practicing Python and SQL, learning how to communicate analytical insights, and building projects around problems that companies actually face.

But despite putting in this work, I’m still looking for my opportunity to prove myself professionally.

So I want to ask the recruiters, hiring managers, founders, and experienced professionals here:

What makes someone an ideal entry-level candidate in your company?

• Is it technical skills?

• Problem-solving ability?

• Business understanding?

• Communication?

• Projects?

•Curiosity and willingness to learn?

•Or something else?

And if you were evaluating my profile, what would you want me to improve or demonstrate before considering me for a Data Scientist / Data Analyst opportunity?

I’m not looking for sympathy.

I’m looking for honest feedback, opportunities, and a chance to prove what I can do.

If you’re a recruiter or hiring manager who works with Data Science / Data Analytics / ML roles, I’d genuinely appreciate your perspective.

And if my profile sounds relevant to something you're hiring for, I’d love to connect.

I haven’t gotten any interviews after three weeks of applications. I only seem to get rejections.
Drive link: https://drive.google.com/file/d/1P_OpbRZ1fc3pIJj0MzIxHP_LhSUdbZV3/view?usp=drivesdk
Updated Resume Link: https://drive.google.com/file/d/1zb8xzLAkpHrc810mpmt_uL1UbfgGpjMw/view?usp=drivesdk

If you're a Data analyst or someone experienced in that field help me how to bulid a dashboard first should I look for questions that can be answer if so people online like in linkedin are posting dashboard with multiple pages so I am confused how a real data analyst project looks like.

And give out some pointers like what are the things you guys are dealing with being a data analyst, what's important to learn for a person looking to land a job in that field.

Thank you in advance.

Today was my second day working as a data analyst with no prior data experience except for a SQL course I took back in college a long time ago. I am feeling so overwhelmed.

During my job interview, I specifically mentioned that it had been a long time since I had worked with SQL queries or SQL in general, but that I was willing and eager to learn and that I loved solving problems, which is true. I also majored in computer science. They ended up offering me the job, and I accepted it, but I had no idea what I was getting myself into.

It turns out I am replacing someone who used to do some heavy data-related stuff that I am not going to get into, but that person was responsible for that portion of the data within our team, and my role was not supposed to get into that, but now that that person is gone, I am being bombarded with not only learning Excel, SQL, Power BI, and other data-analytic tools that I won't be mentioning, but I will also be learning stuff that goes beyond my job description.

And today, they introduced me to some of those concepts for the first time. I had only gotten like 4 hours of sleep last night because of my anxiety due to having this job, and needless to say, my brain retained very little information throughout my shift. I am also feeling imposter syndrome because I am surrounded by amazingly intelligent people, and sometimes I feel like I am not smart enough to keep going. This was my second day, and I was in tears by the time I got home.

If I quit this job, it will severely impact my future employment opportunities with this agency, which I don't want to do. I know they chose me for a reason, but I am severely doubting myself at this rate. I feel dumb.

Please tell me it gets better. Any advice will do. Any experience that you have will be greatly appreciated. Or should I just give up?

Thank you for reading this. I kind of needed to vent, too.

Looking for guidance from experienced folks who have navigated the transition from traditional ML to the current GenAI/Agentic AI landscape.

I have around 9 years of experience in Data Science. Most of my career has been in traditional ML—classification, regression, recommendation systems, propensity models, etc. Over the last couple of years, I've been involved in a few GenAI initiatives, but mostly at the POC stage.

My current work is largely around calling LLM APIs for tasks like summarization, content generation, and similar use cases. While it's GenAI-related, I don't feel I'm building the kind of production-grade systems that many companies seem to be looking for.

I'm now planning a job switch and have been reviewing a lot of job descriptions. Almost every role seems to mention some combination of:

LLMs

RAG

Agents

MCP

AI System Design

LLMOps / MLOps

LangGraph

Evaluation & Monitoring

To be honest, I'm feeling a bit overwhelmed.

When I started my career, stepwise regression was still a thing. Then the industry moved toward ensembles, gradient boosting, deep learning, and now it feels like the expectation is that every Data Scientist should be able to design and deploy agentic AI systems.

For someone with my background:

What would you focus on first?

MLOps or Agentic AI?

System Design or hands-on frameworks?

Which resources actually helped you (courses, YouTube channels, books, projects)?

If you had to create a 3-6 month roadmap today, what would it look like?

I'm specifically looking for advice from people who were experienced Data Scientists and successfully made this transition, rather than generic beginner roadmaps.

Would appreciate hearing what worked for you and what you would do differently if starting again today.

We have finally got IT to agree that we need Fabric, previously being locked behind SSRS reports created by ERP admin on uncurated data with no defined metrics.

My N+2 invited me to the meeting "AI, Fabric and the way forward", though there won't be any discussion on AI, that's far out of our reach. While Fabric wouldn't be my first pick, I'll take anything that IT agrees to so we move forward.

My main concern is if I will be sufficiently heard on the requirement to also get GitHub, as I've been informed that IT isn't super positive since git is "open source". Needless to say our IT department is small and mainly focused on supporting a small technical organization, modern data tools and software development practices is not something they have a lot of experience with.

It took me 5 months after date of hire to get view access to the SQL test server, and only from a second desktop computer where they turned off outgoing telemetry so I could set up a proof of concept for using dbt.

Ownership of data, defined metrics and versioning / approval process is also something we don't have in place currently.

Only the ERP admin can create SSRS reports, which drives all our data needs, when the requestor is happy with "Geoffreys Material Report R05" development stops, and the report is not revisited unless someone complain about the output. R04 and R05 would typically live side by side with no documentation outside of the SQL code, safely tucked away somewhere on someones harddrive. I requested some .sql on email to understand what I was looking at, and I believe I was the first to ever do so.

While I at some point would want to use dbt with Fabric, I don't want to sidetrack the meeting with topics that will take longer to discuss. As I understand, Fabric itself don't have versioning so my hope is to get everyone to understand that. I will also suggest that code I write is reviewed by the ERP admin, and vice versa.

Git would also be used to document our metrics, sql styleguide and anything else that improves the way we work, which won't be too hard.

Where I need help, is if anyone have experience on how difficult it would be to build something proper in Fabric without GitHub. Also any thoughts on how we can set up a simple system where IT would feel to be in control, while still giving me sufficient access to do my job as an analyst / analytical engineer.

I haven't used Fabric outside of Power BI as a super user, but my initial thought was two workspaces, one where IT ingest raw source data where I have view access, then an analytics workspace where I'm a member to create staging, intermediate and marts.

In terms of data in the SQL on-prem database we have around 130 gb reserved, but only 50-100 tables will be required and really only the last 4 of the 12 years available on the fact tables. I'm the only analyst, no data engineers and maybe 40 users of the reports we create, of those 4-5 who use them every day as part of their job.

I've been building an analytics studio app to visualize digital marketing data from key data sources (like GA4, GSC, Google Ads, SEMrush, Nozzle, Profound, adobe etc.). It's a drag & drop dashboard builder; built on Next.js, React 19, Gridstack and eCharts. Supabase is the database, BigQuery is the data warehouse.

Currently the app is hosted on my local computer, no github repository, no hosting, etc.

I want to host the app on Vercel because it integrates/ships relatively seamlessly. But our agency's analytics team would like to host the app on Lovable, because that's what they use for dashboard creation. I have explained that Lovable does not support Next.js and it would require substantial re-writes (possibly taking weeks or months).

Hoping some analytics pros in here could provide me with some talking points on why Vercel is the better option (beyond having to rewrite the app in lovable) - things like error logging, app/user management, data accuracy, scalability, etc. If you have any good use cases, I would love to hear them! Thanks in advance!

So I am currently employed as an administrative assistant at a community college. I have a BA in Psychology and recently graduated with my MS in Data Science from the University of West Florida (degree conferred May 2024). I have been applying indefinitely to multiple job openings to no avail and this be concerned about the probability of me ever landing a job in this field especially with the abundance of AI taking over many traditional human aspects of the job. I know it sounds kind of pathetic to just quit but I am 30 years old and may need to reconsider my career pathway because I don't believe I can continue to work for near minimum wage for the rest of my life. I also think that my undergraduate degree is hurting me more since it's in psychology and I am competing with CS and math grads despite having a Masters in Data Science.

I have been scrolling this sub for over a year now. I had a cs degree a bunch of projects I thought no one cared about, but it all paid off at the end.

Just 6 months working at a shitty job in a certain domain ( marketing). I landed a data analysis job in marketing. Domain knowledge was the missing piece of the puzzle.

Anyone that’s feeling lost out there make sure you actually:

- learn the job

- practice with projects

- and most important in my opinion get some domain knowledge and get in a professional environment for a couple of months

I’ve literally had 2 interviews but I think I am also in a rough spot as I am fairly senior (10+ years) and my compensation is extremely high for an IC in my area.

I also am a more “traditional” analyst and I do not have experience with LLMs. Luckily I still have job but it’s hell on earth with all the PIPs, layoffs and productivity tracking.

I even started to have wild thoughts that maybe I’m blacklisted or something lol. I use AI to tailor my resume but no hits. I tried to apply to gov jobs where I would take a 100k pay cut and no interview. The two interviews I did get were with reputable companies for very senior positions with high comp that I would say are high competition so maybe I’m doing something right but it feels hopeless. Anyone relate?

Hi everyone,

I have 1.7 years of experience as a Business/Data Analyst. After leaving my job, I had a 2.5-year career gap due to preparation for competitive government exams. Now I've decided to return to data analytics, but I'm struggling to get interview calls.

Over the last few months, I've been working on upskilling and building my portfolio. I've completed:

• FP20 Power BI Challenge
• Quantium Data Analytics Virtual Experience (Forage)
• Tata Data Visualisation (Forage)
• End-to-end retail analytics projects using SQL, Power BI, and Python

I've updated my resume, LinkedIn, and GitHub, and I'm applying for jobs daily, but I'm still not getting many interview calls. I feel the career gap is the biggest reason.

I'm also planning to prepare for the Microsoft DP-600 (Fabric Analytics Engineer) certification.

My questions are:

1. Is DP-600 a good choice, or should I focus on something else first?
2. What else can I do to improve my chances of getting interview calls despite the career gap?
3. Has anyone here successfully returned to data analytics after a long gap? I'd really appreciate hearing your experience.

Any advice would be greatly appreciated. Thanks in advance!

So, two months ago our regional director came on a branch visit, and i was the one taking him through our entire analytics, after 3 hours he told me that I make mundane dashboards.

That really hurt, I have poured weekends on building our data suite, countless DAX queries and hours of unpaid overtime, to make sure that I don't miss a single point for this visit.

So I decided to make this "fun" dashboard instead, I put every metric out there and with the help of claude and supaboard made this Mario themed dashboard. I also made it realtime with actual data and the site could be embedded within our systems.

So last week when he came back for another visit, I showed him what I have built, he went through the same, a full 3 hour drill and this he left without any feedback.

The next day I get called into the manger's office and the word around the office was I am getting fired.

But, to my surprise my manager was actually really happy, my dashboard got shared with the executives and everybody appreciated my effort and creativity. I even got a certificate for excellence.

15 years in this trade, and I guess there is a lot to learn yet.

Here's a copy of what I made. Tell me what you think!

I've started my career in nonprofits and only worked in nonprofits until now. 3 times now, I have ended up in roles where I am the ONLY analyst on the team. Everyone I work with is either data adjacent, or not an analyst at all. I'm the only person ever working on analytics work, and I have no real life gauge/context on how to do things better in a real world context. I google things all the time, I take courses, but the advice is too generalized and doesn't go deep enough. I need people I can bounce off of. My biggest hope starting as an early career data analyst was that I'd be able to learn from other analyst and fill the gaps in my education with knowledge from mentors.

Instead, I have people looking to me to be an expert in analytics just because I'm the only one available(as if I'm not a junior). Very few opportunities to learn from actual analysts and get experience from them instead of the generalized advice from Youtube or online courses. I feel like I'm being stunted, but its incredibly hard for me to find roles that are placed in analytics teams, or where I'll be working under a senior analyst (and not just a VP or project manager). Have I screwed myself? Why is it seemingly harder to find analytic roles that work with other analysts?

My_Qualifications: B.Tech Mechanical Engineering (May 2026)

I knew before graduating that I wanted out of mech. It wasn't just the low pay, I genuinely had no interest in manufacturing/core jobs anymore. I also put my master's on hold cause I wanted work ex first. I don't wanna spend another year just learning something and end up with a huge gap on my resume.

At first I thought about support/sys admin/QA kind of IT roles since I'm not into hardcore coding or SDE stuff. Later I switched to analytics cause it looked like it'd have more options. I've learned Excel, learning SQL rn and planning to do Power BI next, but no portfolio or analytics internship yet.

The more I research, the more confused I get. First people say learn tools, then Python, then AI, then AI agents, then domain knowledge. As a fresher, idk how I'm supposed to get domain knowledge without getting my first job.

I'm applying on and off campus but barely getting any calls. So should I keep going with analytics or switch to something else? Should I target IT support/testing roles instead? Or just prepare for CAT/GRE and move on?

I'm not looking for "the market is bad" replies. Ik that already. I just wanna hear from people who were in this phase and actually made it out. What would u focus on if u were starting from scratch today?

There is no amount of words of going in circles asking for VSCode and being told “yeah but can’t you use just Visual Studio”

I get that approving new applications take time but… it’s already Microsoft and it’s already free. Is it really that terrible?

But no instead they gave me a paid license of visual studio so I’m making command line apps and I have no Jupyter notebooks.

However, I have a good manager. He did try to push for it… it’s just ass backwards here.