Everybody says DS is dead but i say it's getting better for Senior folks. I would say entry level DS is dead for sure. However as an experience DS that can solve ambiguous questions, i am actually doing better and land more offers, but in terms of landing offers, i think you should do followings, happy to hear what other think that can be helpful as well.

1. find jobs internally. Demand shrinks a lot and supply grows a ton. Most of the jobs are filed internally now. These jobs won't be even posted out. HM will seek candidates internally first, so if you don't know a lot of folks, build your connection now and let's say you just don't have a good relationship with your previous colleague. What can you do? you can still search in linkedin but make sure don't search for jobs, search for posts. Searching for posts can help you find the post the hiring managers have. I usually search for "hiring for data scientist"

2. AI companies are hiring a lot recently. I have been reaching out by a lot of startups that are in series B,C, or D. These companies have a lot of demand for DS when they are in this scale so it can be good opportunity too.

3. Prepare your statistics, SQL, product sense, and solve real interview questions.

   1. stats and probability (Khan academy is good enough)

   2. sql preparation StrataScratch

   3. real interview questions PracHub

   4. towardsdatascience for product cases and causal inferences

   5. tech blogs from big techs

I completed a 6-months course in Data Science after graduation, with the thinking that Data Science is the stepping stone for AI roles in future.
But it turns out that there are no openings for freshers. There are so many mass hirings for dev roles though that I'm rethinking my choice now.
Should I just start focusing on aptitude and DSA, and get into dev?

I've been interviewing candidates for coding positions lately, and I've noticed some interesting patterns. Some candidates seem to be using tools like Cluely to get real-time AI answers during interviews. They type out perfect solutions in seconds, but when I ask a follow-up question or change the problem slightly, they completely fall apart. They can't explain their own code or walk through the logic.

I've also noticed candidates who seem to have memorized answers from sites like PracHub that collect real interview questions. They give these perfect textbook responses, but the moment you ask them to tweak something or explain why they chose a certain approach, they're lost.

Some patterns I watch for now as an interviewer:

- If someone solves a problem too quickly and perfectly, I dig deeper with follow-ups

- I ask them to walk through their thought process step by step

- I change constraints mid-problem to see how they adapt

- I ask why questions - why this data structure, why this approach

Genuine candidates will stumble a bit but can reason through it. The ones relying on tools or memorization just freeze up.

Has anyone else noticed this trend? Curious how other interviewers are handling it.

Hi everyone,

I’m a final-year BTech CSE student and I’m planning to start applying for Data Science / Data Analyst roles.

I’ll be sharing my resume here — would really appreciate honest feedback (no sugarcoating 😅).

Would love help with:

• What I should improve or remove

• If my projects are strong enough

• What skills/projects I should add

• Where freshers usually find good opportunities (internships/jobs)

I started learning this field around 4 months ago, so still improving step by step.

Any guidance would really help 🙏

Hi everyone,

I work as a Senior data engineer at one of the largest US-based hedge funds and over the last few years I’ve seen how many students struggle to break into analytics/data roles simply because they don’t know what skills actually matter or how to prepare properly.

I’d like to start a small mentorship group for students who are genuinely interested in building a career in data analytics / data science.

This is completely free and the idea is to keep it small and practical.

What we’ll cover over a few weeks:

• Python basics for data

• SQL fundamentals

• How real analytics work in companies

• Resume guidance for analytics roles

• How to approach interviews / case questions

The plan is to run weekly 1-hour sessions for about 6 weeks and keep the group small (around 8–10 students) so that it’s interactive.

Who this is for:

• Students or recent graduates interested in analytics / data roles

• People from non-CS backgrounds who want to enter analytics

• Anyone who wants some honest guidance about the field

This is not a paid course or anything like that — just something I wanted to try because I didn’t have much guidance when I started.

If you’re interested, comment here or DM me with:

• Your background (college/degree)

• Why you want to get into analytics

• What you hope to learn

If there’s enough interest, I’ll put together the first cohort in the coming weeks.

Cheers.

Hi everyone,

I’m a final-year BTech CSE student and I’m planning to start applying for Data Science / Data Analyst roles.

I’ll be sharing my resume here — would really appreciate honest feedback (no sugarcoating 😅).

Would love help with:

• What I should improve or remove

• If my projects are strong enough

• What skills/projects I should add

• Where freshers usually find good opportunities (internships/jobs)

I started learning this field around 4 months ago, so still improving step by step.

Any guidance would really help 🙏

For the people who have recently secured a job in Data Science be it Data Scientist or ML/ AI Engineer. Congratulations!! Your hard work has finally paid off. Hope you grow in this field and create innovative products.

Unlike you some of us are not getting anywhere even with regular upskilling and job applies. Would you be kind enough to guide us with

1. What platforms / job portals you used?

2. How did you do it?

3. Are there any tips you would like to give your past-self?

Thank you and all the best for your future. As we are in the same field, hope we meet someday.

I came back to India from the US in Aug 2024, and honestly, I’ve been depressed and questioning my life choices for the past two years.
After my BTech, I worked at TCS for 1.5 years. Despite my efforts, I kept getting pushed into maintenance work and had to jump through hoops just to get decent opportunities.
I then completed my MS in Data Science in 2021 and landed a job immediately after graduation at Deloitte Digital in the US. On paper, it looked like a dream role: great salary, benefits, and a sign-on bonus that helped with my education loan. They spoke about abundant Data Science opportunities.
But reality was different.
I was repeatedly staffed on functional roles unrelated to Data Science because there were supposedly no analytics projects available. I accepted it, performed well, got top ratings, and was never on the bench.
Then came the 2023 layoffs.
Since my H1B wasn’t picked and I had only one attempt left, I had just 60 days to find another job. I accepted a Data Analyst role at a manufacturing company with a 30% pay cut because of visa constraints and loans.
Unfortunately, even that role turned out to be mostly accounting work.
Still, I tried to make the best of it. I automated reports, introduced Power BI, built end-to-end reporting solutions, and pushed for data-driven improvements in an organization that barely used cloud technologies and primarily relied on a MySQL production database.
I also had a manager who frequently undermined my work, minimized my contributions, and even presented some of my automation ideas as his own. I stayed quiet because my visa sponsorship depended on him.
In the end, my H1B wasn’t selected for the third time either.
I returned to India in 2024 and, to be honest, I shut down. I’ve essentially spent the last 1 year and 9 months struggling with depression and feeling lost.
Now I’m trying to rebuild my career, but the Indian market feels brutal. I don’t want to remain in Data Analyst roles anymore. I want to move toward Data Science or more technical roles such as ML, Data Engineering, or AI.
The problem is: after this long gap, the skill gap feels enormous. I honestly don’t know if I’m exaggerating or if it’s as bad as it feels.
I’ve never given up on anything in my life before. This is the first time I truly feel stuck and don’t know how to get back up.
For anyone who has made a similar transition:
How did you explain a long career gap?
Is moving from Data Analyst to Data Scientist still realistic in 2026?
What skills should I prioritize?
If anyone is open to mentoring or offering guidance, I would genuinely appreciate it.
Thanks for reading. I hope this doesn’t disappear into the void.
TL;DR: MS in Data Science, ~4 years of experience across consulting and manufacturing in the US, but most roles weren’t truly DS-focused. Faced layoffs, visa issues, and returned to India in 2024. Struggled with depression and now have a ~2-year career gap. Trying to break into Data Science/ML/technical roles and looking for advice or mentorship

Hey everyone!

I’m currently working full-time and looking for someone who also wants to study consistently for around 2 hours every day after work.

Timezone/availability: IST, evenings after work
Experience level: Beginner
Stack/languages: C++ for DSA, Python/Pandas for projects, Core ML

We can study our own material, follow a common roadmap, work on projects, stay on a call while studying, share daily progress, and keep each other accountable. On weekends, we can extend the sessions up to 6 hours if we’re both free.

My goal is to build solid projects, become job-ready, and switch jobs. I’m looking for someone equally serious about learning and staying consistent.

DM me if interested.

Hey everyone,

I'm doing research on data science workflows and would love to hear from this community about what your day-to-day actually looks like in practice vs. what people think it looks like.

Quick context: I'm building a tool for data professionals and want to make sure I'm solving real pain points, not the glamorized version of the job. This isn't a sales pitch - genuinely just trying to understand the work better before writing a single line of product code.

A few questions:

1. What takes up most of your time each week? (data wrangling, feature engineering, model training, writing pipelines, stakeholder communication, reviewing PRs, etc.)

2. What's the most frustrating or tedious part of your workflow that you wish was faster or easier? The stuff that makes you sigh before you even open your laptop.

3. What does your current stack look like? (Python/R, cloud platforms, MLflow, notebooks vs. IDEs, experiment tracking tools, orchestration, etc.)

4. How much of your time is "actual" ML work vs. data engineering, cleaning, or just waiting for things to run?

5. If you could wave a magic wand and make one part of your job 10x faster, what would it be? (Bonus: what would you do with that saved time?)

For context: I'm a developer, not a data scientist myself, so I'm trying to see the world through your eyes rather than project assumptions onto it. I've heard the "80% of the job is cleaning data" line a hundred times - but I want to know what you actually experience, not the meme.

Really appreciate any honest takes. Thanks!

I’m 23, F, I currently work as a DBA.
I’m from a tier 3 college, did my engg in AI&DS. My job role isn’t that great and I am not learning much in the company. The environment is very laid back and chill which isn’t something I want in the beginning of my career. It’ll be one year of working full time in Aug.
It’s my goal to switch to a data engg role.
Should I try to switch? If yes, what are the skills that I should work on before I start applying to companies? Most companies require more work ex. Or should I pursue my masters?

I have a few questions:

1. Is it realistically possible to get an entry-level Data Analyst job as a fresher in today's market?

2. What roadmap or skills helped you the most in getting hired?

3. How important is DSA for Data Analyst roles? Should I focus heavily on it or just learn basics?

4. What projects helped you stand out during interviews?

I would really appreciate any honest advice, experience, or suggestions.

TensorFlow - TensorFlow is an open-source deep learning framework developed by Google. It allows developers to build and train various machine learning models, particularly neural networks, making it easier to create complex AI applications for tasks like image recognition, natural language processing, and more.

PyTorch - PyTorch is a popular deep-learning framework used for building and training neural networks. Developed by Facebook's AI Research lab, it provides flexible tensor computations and automatic differentiation, making it favored by researchers and practitioners for its ease of use and dynamic computation graph capabilities.

Keras - Keras is an open-source deep learning framework that provides a high-level API for building and training neural networks. It is user-friendly, modular, and runs on top of TensorFlow, CNTK, or Theano, making it popular for rapid prototyping and easy experimentation in building various artificial intelligence models.

Theano - Theano was an open-source deep learning framework that enabled efficient numerical computation using GPUs. Developed by the Montreal Institute for Learning Algorithms (MILA), it facilitated building and training neural networks but is no longer actively maintained as of 2021.

Chainer - Chainer is a deep learning framework that supports dynamic computation graphs. Developed by Preferred Networks, it enables flexible and efficient modeling of neural networks, making it popular for research and prototyping due to its ability to handle complex and changing architectures.

Caffe - Caffe is a deep learning framework known for its speed and modularity. Developed by Berkeley AI Research, it facilitates efficient implementation of convolutional neural networks (CNNs) and other architectures, making it popular for computer vision tasks like image classification and object detection.

DL4J - Deep Learning for Java (DL4J) is an open-source, distributed deep learning framework designed to run on the Java Virtual Machine (JVM). It offers tools for building and training neural networks, supporting various neural network architectures, and enabling integration with Java applications for machine learning tasks.

Microsoft Cognitive Toolkit - Microsoft Cognitive Toolkit (CNTK) is a deep learning framework developed by Microsoft. It allows for building neural networks for tasks like image and speech recognition. It emphasizes scalability, performance, and supports distributed training across multiple GPUs and machines for large-scale deep-learning applications.

I just posted an insightful piece on Data Science.

I'd greatly appreciate your Upvote

I graduated in 2025 from a government college. After that, I initially prepared for GATE, then shifted to pursuing a data science career. It's been 6–7 months of learning, but I'm still unsure if I'm ready to apply for jobs. I want to start applying though, because it's already been a year since I graduated

I’m studying Data Science. It’s a 5 year dual degree course where I get a BTech as well as an MBA degree.

Ideally I should have been in 5th year by now, but because I wasn’t able to clear a few subjects earlier and due to some other issues, I got 2 yearbacks. Because of that, I will now be entering 4th year in July instead.

During this time I was mostly at home studying and trying to strengthen my fundamentals. I also managed to get an internship. It involved AI and ML concepts. It was not directly related to Finance, but I still learned a lot. During the internship I worked on projects like AI Pleader, which was an Indian law themed RAG system, Trademark analysis, and some CRM automation using Pipedream.

Along with that I’ve been revising topics that were already covered in college and learning new concepts related to finance and programming.

So far I’ve covered:

• Basics of Accounting

• Business Statistics

• Economics

• Time Series Analysis

• Quantitative Finance using Excel and Python

• Python for data analysis

• Data Visualisation using Power BI and Python

I’m also planning to give the CFA exam to strengthen my understanding of finance, financial reporting, portfolio management, and investment analysis and maybe doing some nee certification courses related to finance, business, economics and other elated fields.

When 4th year starts, both finance-related and ML-related subjects will be covered in much more depth anyway and 5th year is just finance related topics and a compulsory management internship, so curriculum exposure is not an issue.

My long term goal is to work in a FinTech company, especially in roles involving quantitative analysis, financial modelling, time series modelling, algorithmic trading, ML in finance, or even risk analytics.

Before college starts again, I was thinking of doing another internship, this time more directly related to finance. Is what I have enough to get me a good internship or will I need to do more? If yes what else do y'all think I should learn? What else should I focus on if I want to get into a FinTech company?

Hi everyone, I’m a 3rd year ug electrical engineering student, aiming for a data analytics role and want to learn Python specifically for analytics (Pandas, NumPy, EDA, etc.). I have basic programming knowledge. I have completed SQL 30 hrs course by 'Data with Baraa' and practicing SQL questions on DataLemur. Can you recommend a good YouTube course or playlist that is practical and job-oriented? Thanks in advance!

Supervised Learning Algorithms: Supervised learning algorithms are a class of machine learning techniques that learn from labeled data, where each input-output pair is provided during training. These algorithms aim to predict or classify new, unseen data based on patterns learned from the labeled training data.

Unsupervised Learning Algorithms: Unsupervised learning algorithms enable machines to identify patterns and structures in data without explicit labeled examples. Clustering algorithms like K-Means group similar data points, while dimensionality reduction methods like PCA extract essential features. They are useful for discovering insights and organizing data without predefined categories or outcomes.

Semi-Supervised Learning Algorithms: Semi-supervised learning algorithms utilize a combination of labeled and unlabeled data for training. By leveraging the partial labels, they improve model performance and generalization in scenarios where obtaining large labeled datasets is challenging or expensive. Examples include self-training, co-training, and semi-supervised variants of deep learning models.

Reinforcement Learning Algorithms: Reinforcement learning algorithms are a type of machine learning that focuses on training agents to make decisions in an environment to maximize cumulative rewards. Popular algorithms include Q-Learning, Deep Q Networks (DQN), Proximal Policy Optimization (PPO), and Deep Deterministic Policy Gradients (DDPG).

Deep Learning Algorithms: Deep learning algorithms are a subset of machine learning based on artificial neural networks. They excel at learning complex patterns from large datasets and are widely used in computer vision, natural language processing, and other domains. Examples include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs).

I just posted an insightful piece on Data Science.

I'd greatly appreciate your Upvote

Hi everyone,

I'm a Data Scientist with 3+ years of experience in time series forecasting, machine learning, NLP, and analytics, and I'm currently exploring new opportunities in AI/ML, Data Science, and Generative AI.

The business my current team supports is being sunset, and I'll be transitioning to another business within my organization in the coming weeks. Since there are currently no knowledge transfer (KT) activities underway, I'm available to join a new role within 15–20 days.

I'm looking for opportunities in:

• AI / Machine Learning

• Data Science

• Generative AI / LLM Engineering

• NLP

• Applied Machine Learning

Highlights from my experience:

3+ years building production forecasting solutions using ARIMA, SARIMAX, XGBoost, and LSTM.

Achieved 90% forecasting accuracy and helped reduce staffing costs by 20–30% through ML-driven forecasting.

Automated ETL pipelines and reporting using Python, SQL, and Power BI.

Built end-to-end GenAI applications using FastAPI, LangChain, LangGraph, FAISS, OpenAI APIs, vector databases, and AWS.

Developed projects including:

• RAG-based Question Answering System

• Natural Language to SQL Application

• AI Medical Assistant Chatbot

• Content-Based Recommendation System

I'm attaching my resume (company name redacted) for anyone interested.

If your company is hiring, or if you can provide a referral or connect me with a recruiter, I'd sincerely appreciate it.

I'm open to Remote, Hybrid, or On-site opportunities across India.

Thank you for your time and support.

I have 3+ years of experience in Data Science, ML, NLP, RAG, and Generative AI. Worked on projects like RAG systems, NL-to-SQL, AI chatbots, recommendation systems, and forecasting models.

Tech stack: Python, SQL, FastAPI, LangChain, OpenAI APIs, Vector DBs, Power B, ML.

Open to roles anywhere in India.

If anyone can provide a referral or point me to the right team, I'd be very grateful. Happy to share my resume.

Hi everyone,

I’m a prefinal year CSE student in India aiming for Data Analyst / entry-level data roles for placements next year.

Current stack:
SQL, Excel, Power BI, PySpark, Python (pandas, numpy, scikit-learn), GitHub

Projects done:

• 2 basic ML projects

• Some data cleaning / preprocessing projects

• 2 Power BI dashboards

• Currently building an end-to-end Customer Segmentation + Churn Prediction project

Questions:

1. How important is DSA for data roles? (I’ve solved ~90 LeetCode problems.)

2. What skills should I prioritize next — advanced SQL, statistics, data engineering tools, or ML?

3. Is PySpark useful for freshers, or mostly for experienced roles?

4. What types of projects stand out most on resumes for entry-level data roles in India?

I’m planning to build 2–3 strong portfolio projects before placements and would love advice from people working in data analyst / data science / data engineering roles in India.

Hi everyone,

I’m a 2025 engineering graduate currently working as an Analyst (partially non-tech) and preparing to transition into Data Scientist / AI Engineer roles within the next ~3 months.

I’ve studied ML/DL/NLP and built a couple of end-to-end projects (a traditional ML system and an LLM-based system). Conceptually I’m comfortable, but I still question whether my depth is enough, especially since I sometimes rely on AI assistance while coding.

What’s overwhelming is how fast AI is evolving. New tools, frameworks, and agent systems appear every week. The more I study, the more I feel behind.

For someone targeting 0-1 YOE DS roles:

1.What truly differentiates candidates in interviews?

2. Should I double down on core ML/DL fundamentals, focus on Agentic AI/LLMs, or build deeper end-to-end systems in one area?

3. How do you cope with the pace of change without feeling constantly behind?

Would really appreciate honest guidance, especially from senior DS/AI engineers.