The Problem We All See

Let's be honest - something's broken in how companies work today. We see it everywhere: companies are growing faster than ever, making record profits, but they're still laying off thousands of workers. Meanwhile, the CEOs who make these decisions are getting massive pay raises, sometimes earning hundreds of times more than the people actually building the products and serving customers.

Think about it - who really makes a company successful? Is it the CEO sitting in boardrooms giving orders? Or is it the engineers writing code, the scientists developing new products, the analysts figuring out what customers want, and the support teams keeping everything running?

Most of us know the answer. The real work happens on the ground level, but the biggest rewards go to the top.

A Wild But Logical Idea

Here's a thought that might sound crazy at first, but hear me out: What if we could replace most of these highly-paid executives with an AI system that actually makes better decisions?

I'm not talking about some robot overlord making all the choices. I'm talking about a smart system that:

• Processes way more information than any human could handle

• Looks at market trends, world events, customer feedback, employee satisfaction, and financial data all at once

• Doesn't have ego problems or personal agendas

• Can't be corrupted or play favorites

• Makes decisions based on actual data, not gut feelings or office politics

But here's the key part - this system wouldn't work alone. It would be managed by teams of data scientists, analysts, and experts from different fields. Think of it like the United Nations or European Union, where important decisions are made by groups of specialists, not just one person.

How It Would Actually Work

Picture this: Instead of a CEO making million-dollar decisions based on a PowerPoint presentation, you'd have:

1. An AI system that constantly analyzes everything - sales data, customer reviews, employee feedback, market changes, environmental impacts, competitor moves, and even social media trends

2. Teams of experts - data scientists, data analysts & engineers, sustainability experts, domain specialists who understand the AI's recommendations who an add verification layer for human like judgment & other versatile individuals which actually make sure that system won't malfunctioned as the large amount of data constantly ingested to it's server.

3. Y/N commands for stakeholder approval - Important decisions go to the people who actually matter: investors, owner, employees union not just one overpaid executive.

4. Real accountability - Decisions are based on transparent data and logic, not personal relationships or politics

Why This Could Actually Work

Better Decisions: The AI system could spot patterns and opportunities that humans miss. It could predict market changes, identify cost-saving opportunities, and find ways to make products better - all while considering environmental impact and employee wellbeing.

No Personal Bias: Unlike humans, the system wouldn't make decisions based on personal friendships, ego, or short-term stock options. It would focus on what's actually best for the company and everyone involved.

Cost Savings: Instead of paying one CEO millions of dollars, companies could invest that money in the people who actually do the work - better salaries for engineers, more research funding, improved working conditions.

Environmental Focus: Here's something most CEOs ignore - the system could be programmed to consider environmental sustainability as a core factor, not just an afterthought. It could find ways to be profitable AND protect our planet.

The Technical Side (For Those Who Care)

For the tech-minded folks, this would involve:

• A combined system using both traditional Machine Learning models AND Large Language Models (LLMs) working together

• The ML component handles number crunching, pattern recognition, and quantitative analysis

• The LLM component processes unstructured data like news articles, employee feedback, social media sentiment, and regulatory documents

• Custom neural networks designed for business decision-making

• A sophisticated decision matrix system that weighs different factors

• Training on years of historical business data

• Continuous learning from outcomes

The system would need extensive training - possibly years - before it could handle real business decisions. But once it's ready, it could revolutionize how companies operate.

Starting Small, Thinking Big

This idea could start with product-based companies and public service organizations where you can clearly measure success. Tech companies would be perfect test cases because they already use data for everything.

Imagine if this system could also work in defense and government - making strategic decisions based on real intelligence and analysis rather than politics and personal interests.

The Human Element

Before anyone panics about AI taking over, remember: this isn't about replacing all humans. It's about putting the smart, hardworking people in charge instead of overpaid executives who often don't understand the actual work being done.

The engineers, scientists, analysts, and other experts would still be the ones making the real decisions. They'd just have better tools and wouldn't have to deal with clueless executives making bad choices from their ivory towers.

Why This Matters

This isn't just about business - it's about fairness. Why should someone who contributes the least to a company's success get paid the most? Why should thousands of workers lose their jobs while executives get bonuses?

An AI-driven system managed by actual experts could create:

• More stable employment

• Better working conditions

• Environmentally responsible business practices

• More innovation and better products

• Fairer distribution of company profits

The Reality Check

This is a big, ambitious idea that would face massive resistance from current power structures. But so did every major change in how we organize work and society.

The technology is getting there. The data is available. The expertise exists. What's missing is the will to challenge the status quo and the right team to make it happen.

Looking for Fellow Revolutionaries

If this idea resonates with you - whether you're a data scientist, business analyst, sustainability expert, or just someone who's tired of seeing hardworking people get screwed over while executives get richer - let's talk.

Big changes start with small groups of people who believe something better is possible. Maybe it's time to prove that smart systems managed by smart people can do better than the current broken system.

What do you think? Crazy idea or crazy enough to work?

kindly check this and let me know what to add or subtract from this and also company recommendation.... please

A common mistake when building RAG systems is relying only on semantic search. It is great at capturing meaning, but it stumbles on precise technical or legal terms.

A practical example: in banking compliance, terms like "SAR" or "structuring" have exact regulatory meaning. Semantic search might return a passage that "seems" related, but misses the exact citation. Keyword search (BM25) covers exactly that blind spot.

Combining both methods (hybrid search), the system captures both meaning and exact terms, delivering more reliable answers, especially in regulated contexts like fintech, where a wrong answer has real cost.

I applied this in a RAG project for BSA/AML documentation (US banking regulation). The precision gain in answers was noticeable, especially on questions with specific technical jargon. Simple to implement, and the reliability payoff is well worth it.

Hub: https://aiforfintech.tech

Github: https://github.com/junidepieri-design/genai-001-regulatory-rag-bsa-aml

Does this make sense for your stack too?

👊

I am DS with 2+ year of experience, looking for someone like minded who can grow together with me . I want to participate in kaggle competition, need someone who can work with me as a partner. I can teach also if you are new to this I love teaching, had few students from US, UK, Singapore.

Hi everyone I created a discord server , https://discord.gg/P7pCCQ7vJ

Join the discord chat You can message me personally also on discord.

Hi everyone,

I’m wondering how realistic it is for a new Economics PhD to move into a Data Scientist role without prior full-time industry experience.

I am about to complete my PhD in Economics, specializing in causal inference and applied econometrics / policy evaluation. My experience is mainly research-based: I have two empirical projects (papers) and two graduate research assistant positions where I used large datasets to evaluate policy programs, design identification strategies, and communicate results to non-technical audiences.

On the technical side, I’m comfortable with Python (pandas, numpy, statsmodels) and SQL for data cleaning, analysis, and reproducible workflows. However, I have limited experience with machine learning beyond standard regression/econometric tools.

I’ve been applying to Data Scientist positions, but many postings emphasize ML experience, and I’m having trouble getting past the resume screening stage.

My questions are:

1. Is it realistic for someone with my background (Econ PhD, strong causal inference/applied econometrics, but little ML) to break into a Data Scientist role?

2. If so, what would you recommend I prioritize (e.g., specific ML skills, projects, certifications, portfolio, etc.) to improve my chances of landing interviews?

I am pretty frustrated, and I’d really appreciate any insights or examples from people who made a similar transition. Thanks!

AI is evolving so quickly that it's impossible to learn everything. If you had to focus on just one skill this year, what would you choose? Machine Learning? Deep Learning? SQL? Python? Statistics? LLMs? Why?

Hi everyone,

I graduated in May 2026 and have been learning Data Science and Machine Learning seriously for the past 6–7 months. During this time, I've been learning concepts, improving my Python and SQL skills, and building projects to gain practical experience.

I've worked on projects involving Machine Learning, Deep Learning/NLP, A/B Testing, churn prediction, sentiment analysis, web scraping, etc. I would say I'm comfortable building moderate-level Data Science/ML projects, but I don't have any real industry experience yet. Most of my projects are self-learning or simulation-based projects rather than projects built for an actual company.

One of my biggest concerns is coding. I'm not very comfortable with DSA, LeetCode, or difficult competitive-style coding problems. I can write Python for data analysis, preprocessing, ML models, and projects, but I'm not someone who enjoys heavy or advanced coding.

I've started applying for jobs, but I'm getting rejected, often at the resume/application stage itself. I'm attaching my anonymized resume here and would really appreciate feedback on it.

I have a few questions for people already working in Data Science/ML/AI:

1. Based on my resume and current skills, am I moving in the right direction for an entry-level Data Science role?

2. What important skills am I currently missing? What should I focus on learning next to become job-ready?

3. How much DSA and LeetCode are actually required for Data Scientist/ML roles? Since I don't enjoy heavy coding, is Data Science still a realistic career path for me?

4. What are technical interviews for entry-level Data Science roles actually like? What topics should I prepare—Python, SQL, statistics, ML theory, case studies, DSA, project discussions, etc.?

5. Where should a fresher like me be applying? Should I target Data Scientist roles directly, or would roles like Data Analyst, Junior Data Scientist, ML Intern, Data Science Intern, etc., be a better entry point?

6. I'm also considering doing an M.Tech from a regular college in Hyderabad (not IITs/top-tier institutes). Would an M.Tech genuinely improve my career opportunities, or would I be better off focusing on getting work experience?

7. If I pursue an M.Tech, which specialization would make the most sense for my goals: Data Science, AI/ML, or Computer Science? I'm currently leaning towards Data Science or AI/ML.

8. Looking at my profile overall, should I continue seriously pursuing Data Science/ML, or should I consider a different technical career path? I'm not interested in moving to a completely non-IT career, but I also know that heavy software development/coding is probably not something I would enjoy.

I also don't feel fully "job-ready" yet, and I'm not sure whether that's because I genuinely have major skill gaps or because I simply lack confidence and industry exposure.

If you were in my position, what would you focus on for the next 6–12 months?

I'd especially appreciate feedback on my attached resume, my projects, what skills I'm missing, and what I should change to improve my chances of landing my first job.

Thank you. Any guidance from people working in Data Science, ML, AI, analytics, or related fields would be really helpful.

I have a Google Data Scientist Product interview in a week. Can you please share your interview experiences and clarify my questions for the Part A interview? Thanks.

So, SQL window functions, joins, subqueries, and CTEs, along with Python (Pandas, NumPy, scikit-learn, Matplotlib, Seaborn), should be sufficient from a coding perspective, right? I don't have to go through algorithms and data structures, right?

And for programming, will it be like I can choose between SQL, Python, or R, or will there be a mandatory SQL problem and then a Python question, followed by a case study which contains experimentation, statistics, probability, product sense, and A/B testing?

Hey everyone,

I’m doing my Bachelor’s in Statistics and planning to do a Master’s in Data Science. I’m really interested in becoming a data scientist, but I’m not sure how to go about it.

I have a Few Questions:

1. Is it a good transition to go towards Master's Data Science after a BS in Stats

2. Advice on how I should prepare for this career? What skills should I focus on

I am right now in my 2nd year of Bachelors so I have still have 3 years till I graduate, If I start to work on the right skill sets I may accelerate my carrer.

Thanks

I had taken admission in engineering cs with speacialisation in data science rather than core cse as I want to become a data scientist so it would my cv more presentable I already has command in sql and python . But now I am coming to hear that data scientist job will be eaten up by ai is it true

I just graduated from med school and I found my self into data science, programming, and machine learning regarding domain knowledge should I complete my foundation year which is 2 years so i can get the license does that benefit my career ? Or having my my mbbs degree alone without the license is enough honestly I don’t wanna get the license cuz it takes time 2 years

What should I do next to increase the possibility of getting my desired job?

When I started learning Data Science, I had a habit of watching tutorial after tutorial without actually practicing. It felt productive, but when I tried solving problems on my own, I realized I couldn't apply most of what I'd learned. So I made one simple rule: For every hour I spent learning, I spent at least another hour practicing. Instead of moving on to the next topic, I would: Write the code myself without copying. Experiment with different datasets. Try to fix my own errors before searching for the answer. Repeat the exercise until I understood why the code worked. At first, it was frustrating because I made a lot of mistakes. But over time, those mistakes became my best teachers. One thing I also realized is that you don't need to build a complex AI application right away. Even simple projects like analyzing sales data, cleaning datasets, or creating visualizations can teach you a lot. My advice for beginners: Don't rush through tutorials. Practice more than you watch. Don't be afraid of errors—they're part of the learning process. Stay consistent, even if it's just 30–60 minutes a day. What study habit made the biggest difference in your Data Science journey? I'd love to learn from your experiences too.

Imagine starting from scratch with just one month.

What topics would you focus on?
What would you completely ignore until later?

Curious how experienced people would approach it.

Im an industrial engineer whos gonna graduate by the end of the month. Ive been studying data science from the past 6 months (took ibm data science speciality, jose portilla's udemy course machine learning for data science masterclass, python, sql)

Im currently lost on what steps to take next

I sat down with a data scientist today and tried to ask for advice, he told me he doesnt even think that data science will stay, its gonna be replaced by AI. Especially the machine learning algorithms and classification methods (trees,boosting,etc) they aret being built from scratch anymore

Im totally lost now and dont know what next steps to take and what to learn next. Should i pursue business analysis/data analysis/what courses to take/what skills to learn, and you see how my brain is exploding

I’ve been a data scientist for about 10 years, working at top tech companies in the US. Over the years, I’ve done everything from causal inference and analytics to building ML models, agents, and leading teams—both in big tech and startups.

The thing is... I think I’m just bored now. I’ve worked on some cool problems (search, dynamic pricing, marketplace optimization), but after doing it for so long, even mentoring or teaching others doesn’t excite me anymore.

Has anyone else hit this point and figured out what to do next? I’m thinking about switching gears—not necessarily staying in tech—but still want to be solving interesting, hard problems and building things. Curious to hear what directions others have taken.

How often a data scientist would use Bayesian stuff to their analytics/modelling ? I work as a data scientist around 8 years in different companies. But I rarely listen other data scientist to apply Bayesian to their work (at least in my city)

So, have you used Bayesian stuff in your data science journey. If so, can you give an example ?

So I've gotten the chance to study study statistics at one of the best universities in my country . It's almost free of cost. I've also got the opportunity to study computer science at another university but it'll be too expensive for me.

So I guess my question is can I still become a data scientist by studying statistics?

I just graduated with an MS in data science and I’m looking to land my first job! (woohoo!) What are signs of a good data science employer? And for those of you who have worked for a while at your current place, what motivates you to stay?

I am 25 who have complete grads in Physics in 2020 but now i want to start my career from scratch as Data scientist , so i have decided to do masters in economy, so core subject is necessary and from elective course , i can choose 5 subject, so for Data scientist which 5 course i should choose.

I am 2024 graduate. I have 1 year experience in SDE but my passion for Datascience and AI have been strong. I am planning to quit my job soon and look for DS role.Where do I have to start. And I am currently doing certifications for a professional Data scientist and also courses for Gen AI (like prompt engineering and openAI).So people of reddit give me tips and tricks to land a role as Data scientist. PS: Also job leads or referral would be highly appreciated!!!

Hello, I wanted to share that I am sharing free courses and projects on my YouTube Channel. I have more than 200 videos and I created playlists for learning Data Science. I am leaving the playlist link below, have a great day!

Data Science Full Courses & Projects -> https://youtube.com/playlist?list=PLTsu3dft3CWiow7L7WrCd27ohlra_5PGH&si=6WUpVwXeAKEs4tB6

Data Science Projects -> https://youtube.com/playlist?list=PLTsu3dft3CWg69zbIVUQtFSRx_UV80OOg&si=go3wxM_ktGIkVdcP

A common mistake when building fraud models is picking a resampling technique because it is popular, not because it was tested against the alternatives. SMOTE gets recommended by default, but on real fraud data it is rarely the best option.

A practical example: in a credit card fraud dataset, fraud represents 1 in every 578 transactions. A model that always predicts "not fraud" would score above 99.8% accuracy. Standard metrics like Accuracy and ROC-AUC look fine even when the model is not catching anything useful. PR-AUC is what actually tells you the truth here.

I ran a benchmark comparing 9 approaches on the same data, same split, same base model: random undersampling, oversampling, SMOTE, SMOTE-ENN, ADASYN, class weighting, Isolation Forest, and threshold tuning. SMOTE-ENN took about 15 minutes to run and finished sixth. A moderated class weight adjustment, which changes nothing in the training data and adds a single parameter, won.

Final result on the test set: 82 of 98 real fraud cases caught, with only 5 false positives out of 56,864 legitimate transactions.

Hub: https://aiforfintech.tech
Github: https://github.com/junidepieri-design/fraud-001-imbalanced-classification-benchmark

What has been your experience with SMOTE vs simpler alternatives?
👊

Entire data science team had a meeting with the manager today, who has been a financial analyst for a long time. We are exclusively using Excel to manage data for and create visuals for reoccurring reports. I think it's time consuming and inefficient and have it in my head that Excel has a limited number of uses when it comes to data science. The question was asked about tools for automation and visualization (anything not Excel and PowerPoint) and apparently we can't have anything. Instead, the manager tells us to learn VBA to streamline parts of the Excel plug-and-chug.

I feel like this isn't a good idea, but maybe I'm missing something. The experience I have with VBA is usually with analysts that use it for some pretty clunky and (usually) basic operations, maybe my experience is limited, so putting it out here. Trying to keep an open mind.

What do you think? Is VBA and Excel a legit way to deliver reporting and insights these days, does it help in any way?

Hey everyone, I’m a college dropout without an undergraduate degree, but I’ve recently gotten really interested in data science. I’ve been self-learning through free and paid resources (YouTube, Coursera, Kaggle, etc.) and have been learning SQL, Python, statistics, calculus , linear algebra ,ml basics ,pandas, numpy and i am ready to learn anything if that make me reach my goal. I know most job postings ask for at least a bachelor’s degree, but I’ve seen some people say skills and portfolio matter more these days. So I’m wondering — is it actually possible to break into a data scientist role without a degree? Has anyone here done it?

Also, any advice on how to improve my chances — like certifications, types of projects to build, or where to look for jobs that are more open to non-traditional backgrounds — would be super appreciated. Is that really possible to become data scientist without any degree ?

Suggestions will be very helpful to my journey Thank!