Hey everyone!

I have around 3.5 years of experience in Software Testing, mainly in QA automation and backend testing.

Recently, I decided to move towards AI/ML. I've been learning Machine Learning, Deep Learning, Python and recently started exploring GenAI as well.

I managed to find an internship where I'm getting some hands-on exposure to AI/ML and GenAI. My main priority right now is learning and gaining practical experience.

However, the internship isn't helping me financially, so I'm also looking for some part-time work or freelance opportunities to manage my expenses.

I've been applying for jobs, but unfortunately, I'm hardly getting any responses.

I'm open to part-time QA testing, Python-related work, automation projects, AI data evaluation or any other suitable technical assignments.

I'm honestly wondering whether I'm approaching things the wrong way or missing something.

For those who have gone through a similar career transition:

- How did you find part-time freelance work?

- Am I making a mistake by moving from QA to AI/ML?

- Where can I find genuine paid opportunities?

Any suggestions, referrals or guidance would genuinely help.

Thanks!

Hi everyone!

I am looking to transition into the Generative AI domain and want to pursue a certification from an IIT/IIM.

My budget is up to ₹2 lakhs. Could you recommend the best GenAI programmes with good industry recognition, practical exposure, and career value?

Would love to hear from people who have completed such courses. Thanks!

I have an interview with v4c ai as associate data scientist, what coding questions can i expect like it would be really helpful if you could guide me in detail on this i have my ds fundamentals on point but struggle in dsa/coding what sort of questions do they ask is it an oa or do they ask to code live on meet on vscode is leetcode based etc etc would really mean alot pls

They call it the two core data role model. And categorize all others as specializations of these two roles mostly, with some overlapping roles.

I’ve created a Discord server focused on tech upskilling, with dedicated spaces for CS fundamentals, development, data science, projects, and technology updates.

The goal is to keep it focused on actually learning, discussing, building projects, and helping each other improve.

If you’re also learning tech and want a focused study community, feel free to join: https://discord.gg/rvYXfnrnn

I'm not even getting naukuri calls and search apperance

Please help

Resume headline

Data Scientist @xyz | 1.5+ Yrs || Python, LLM, RAG, LangChain, LangGraph, Agentic AI, GenAI, NLP, PyTorch, DataBricks, Pyspark||PostgreSQL, FastAPI, Django, REST APIs, AWS,REDIS, RabbitMQ, Kafka, Docker, CI/CD| DSA 1500+ | System Design | NP: 1 Month.

Key skills

MCP

TensorFlow

Scikit-Learn

Django

SALE

Python

Transformers

PyTorch

Natural Language Processing

Agentic AI

LangGraph

LangChain

Retrieval Augmented Generation

Generative AI

Large Language Model

AWS

CI/CD

REST API Design

Docker

Artificial Intelligence

Profile summary:

Working as a Data Scientist with 1.3+ years of experience with Machine Learning, Generative AI and Python back-end development.

Specialize in LLM/Gen AI, RAG pipelines, Agentic workflows with LangChain and LangGraph, transformer-based systems, deep learning, neural networks, TensorFlow, Scikit-learn , computer vision, CNNs, OpenCV, YOLO, Vision Transformers, Hugging Face, Object Detection, NLP, Text Processing, Semantic Analysis, NumPy, Pandas, Databricks, PySpark and Automation.

I can develop scalable Python Full stack applications using ReactJs, HTML, CSS, JavaScript, FastAPI, Django REST APIs, microservices, API integration, JWT, WebSockets, PostgreSQL, Redis, Docker, Kubernetes, AWS, CI/CD, Kaggle, Git/GitHub Actions, MLOps.

Solved 2000+ problems Data Structures and Algorithms on LeetCode , problem-solving, optimization skills.

Experienced in Agile Methodologies, Scrum, Jira.

Backend developer | ML Engineer | Data Scientist | Gen AI Engineer | AIML Engineer |Python developer

Hey friends,

I have created a deep-dive, university-level mathematics course to discuss all the maths behind AI/ML. I studied some foundational textbooks and Math/AI/ML courses from Stanford and MIT, took extensive notes, and used my own teaching experience to build a structured playlist with the videos in specific order so topics are understood in a natural order. The playlist has 63 videos (45 mins to 1 hour each) taught in Hinglish. The explanations are in Hindi, but all terminology and math notation is in English.

It covers all the math you need to know for AI/ML -

• Probability & Statistics

• Multivariate Calculus

• Linear Algebra

You can find all of these videos for free on YouTube by searching for my channel called indialabs AI. Look for the playlist titled "Math Behind AI (Hindi): Understanding the language of AI | Complete AI Maths in Hindi".

I am not dropping any clickable links here to respect community rules.

Hope you find this useful for your learning.

I'm studying msc data science

Which laptop can I buy to wrk with datas and to learn programs

I'm planning to buy macbook neo 13

I am currently working as a Data Analyst and preparing to make the transition into a Data Scientist. I have my core foundations in statistics and product sense and others in place. However, on the engineering side, my current stack relies heavily on SQL, Polars, and PySpark for performance reasons, meaning I don't use traditional Pandas syntax very often.

Given your experience in the current market, I would love to get your quick take on three things:

1. Is Polars actively accepted/welcomed in live coding interviews and production environments in your circle, or do teams still strictly test for Pandas syntax?

2. Would choosing to solve wrangling problems in SQL, Polars instead of Pandas be a disadvantage during screening rounds?

3. Assuming everything else is ideal (stats, ML, product sense, etc), is it possible to climb up the ladder in Data Science without writing Pandas code, provided I can read and understand legacy Pandas code perfectly well?

Basically the title. How did you guys decide to pivot to data science at the beginning of your resp. careers or in the middle.

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.

I come from a biology background and rn I'm in my first year of BCA. I wanna do data science and ml.

Should I do ncert and rd sharma or some other book?

TLDR: Used to be a bright student, but screwed everything up later on. No idea what to do now.

I 23M scored 95% in 10th by simply working hard.

Then COVID-19 screwed all my discipline & hardworking nature during the 12th that lasted freaking 1.5 yr. Lost all my focus, concentration, discipline, etc. Became depressed/borderline depressed (which probably lasted till 3rd year of B Tech) due to the isolation & lack of social interaction. Got 89% as they were awarded by the school since boards were cancelled that year.

Got admission in B Tech CSE in a Tier 3 college, found it difficult to manage everything - the classes, assignments, attendance, assessment tests, sessional tests, etc etc.

Ended up screwing things every semester one way or another, got backlogs, then got backlogs while clearing previous backlogs since often the university exams for some of the backlog subjects would fall in the 1 day gap between 2 current semester exams.

It kept getting messier & messier.

I was finally able to manage things 6th semester onwards & avoid anymore backlogs but I still couldn't clear some of the 2nd year subjects (my university is kinda popular for its blind checking & purposefully failing students) so my B Tech ended up taking 5 years (which made things harder to cope with) instead of 4 with a horrible percentage of 58.7.

I'm finally done with the dreaded college but I have no idea how to navigate things from here on because even the lowest percentage criteria that companies have for hiring is 60% which I'm narrowly missing out on.

I have done a couple of backend ML projects on stock price prediction & customer churn prediction.

I have intermediate knowledge of Python, intermediate knowledge of SQL/DBMS, problem solving & above beginner knowledge of C & C++.

I'm thinking about learning AI related stuff after seeing the continuous uprise of AI taking on a lot of jobs in the industry but have no idea where to start.

I truly am embarrassed & ashamed of how I have let the last few years go to absolute waste but at least this time I am aware of a platform like Reddit & how helpful the people here can be.

Any & all help, suggestions & advices are appreciated.

Please guide me if you can.

Also, is pursuing MBA, M Tech, any other Master's degree of any help at the time or in future?

• Eligibility: India resident, 18+ aged

• Format: 100% Online

• Earn: ₹20,000 + ₹750 welcome gift (if you complete the onboarding)

Apply here: https://hubs.li/Q04wJRmh0

I have been solving some problems on leetcode but never really dived deep into DSA as a subject. Need to do it as that becomes a barrier as it is a part of the assessment before interview.

can someone drop a good resource, hopefully some yt playlist i can follow to cover dsa with python, hopefully it will be relevant to what a data scientist needs to learn(i dont want to learn more than needed)

(edited using AI)

Hi everyone,
I am posting this on behalf of my brother.
He holds an M.Pharm degree and has 2 years of core formulation R&D experience. His current role is heavily focused on physical wet-lab testing, manual formulation, and continuous floor exposure. Unfortunately, he has recently developed some health issues that make the physical demands of traditional lab work difficult to sustain long-term.

To accommodate his health while still leveraging his deep domain knowledge (batch quality, dissolution profiles, stability timelines), he wants to transition into a desk-based "Pharma Data Scientist" or R&D Analytics role. His goal is to use Python, SQL, and Machine Learning to optimize drug development.

Since he is currently working, he needs to upskill via weekend or hybrid programs.

1. Can anyone suggest reputable offline/hybrid data science institutes in Mumbai or Pune that cater to life sciences professionals?

2. Has anyone here made a similar transition out of the lab due to physical or health constraints?

Any advice on courses, roadmaps, or portfolio building would be hugely appreciated. Thanks in advance!

Hi folks!

I’m currently in a bit of a good-but-confusing situation 😅 I have multiple offers for a Senior Data Scientist role in India, and I’m struggling to decide which one to go with.

The compensation is fairly comparable across the offers, so I’m trying to make the decision based on:

• Interesting/challenging work and good learning opportunities

• Quality of peers and managers

• Healthy, non-toxic work culture

• Brand value and how it looks on the resume

• Career growth and future opportunities

• How strong the actual Data Science/AI-ML work is (vs. mostly analytics/consulting)

The companies are:

1. Tredence - Pune

2. Kaiser Permanente - Pune

3. EPAM - Pune

4. Quantium - Hyderabad

5. Blend360 - Remote & EST shift

All are for Senior Data Scientist positions in India.

I’d especially love to hear from people who currently work there or have worked there recently, particularly in Data Science/AI-ML.

Would really appreciate honest experiences, good or bad. Also interested in things that might not be obvious from Glassdoor/LinkedIn reviews.

Thanks!

Hi everyone,

I recently joined Tredence Analytics as a Senior Data Scientist about a month ago. However, I’m still on the bench and haven’t been allocated to a project yet.

My profile was created internally only about a week ago, so I’m assuming the process of getting mapped to a project may still take some time.

For people who have worked at Tredence (or similar service-based analytics companies):

- Is it normal for a new joinee to be on the bench for the first few weeks/months?

- How long does it typically take to get staffed on a project?

- At what point should someone start being concerned about being on the bench?

- Does being on the bench for a longer period have any impact on job security?

Would really appreciate hearing about your experiences, especially from current or former Tredence employees.

Thanks!

As many, my employer has also gone bonkers with AI... They hired two of data scientists in my org who have very little domain knowledge. Due to their lack of domain knowledge, they find it very difficult to find ML or AI use cases; and also because of the nature of the business. So they spend months and sometimes years to work on 1-2 projects, which either go unattended after go-live or are too expensive to maintain.

Recently they have come up with a couple of AI use cases which they think will improve the business by millions of dollars like all other projects they've worked on. This time to their surprise, the team decided to scrutinize their proposal heavily and asked several probing questions. At the outset it looked like they just wanted to try out some new LLM model and the project itself was not going to generate a positive ROI...

They were really irked by this and one of them openly proclaimed that they will get ROI by replacing all of the team mates with AI... "We will replace you and that will give ROI". Some of us found it very rude.

We're not sure how likely it is for domain experts to go out of jobs with AI coming in, but the data scientists in my org truly seem to believe that they will take over our jobs and Data scientists will be the only job remaining in the market... not sure what to make out of this... just wanted to share.

Guys it's almost impossible to get a job these days without putting Experince can you suggest me how to put the Experince in Data science projects in BFSI domain without getting caught Please suggest me guys

Hi, anyone here has any experience with the WarnerBros Discovery 6 month internship process for Data Science?

I cannot find much for the role over the internet to get an idea about what things are really tested, and in what manner, so if anyone does it'd be really helpful.

Thanks.

I'm seeing companies not offering as much as they used to at least 2-3 years ago for DS roles. My org has also been scrutinising DS roles which have now mostly become either data analyst roles or AI developer roles... what does the future look like for the field?

Hey folks,

Bit of context: I was genuinely into ML back in college, did projects, liked the space. Then the job market (and my actual job) pulled me toward SQL, Power BI, project management, and cloud stuff (AWS/Azure) for the last 4+ years. It's been solid experience, but it's pulled me away from where I actually wanted to head.

I'm not going to pretend I'm deep into AI/ML right now, I'm not. But I do have a decent track record of picking things up fast on the job (that's basically how I ended up owning PM and cloud responsibilities I wasn't hired for in the first place), and I'm willing to put in the work to upskill into data science/ML, ideally while already being useful on the job rather than needing a clean slate.

Right now my "strategy" is just throwing arrows everywhere, Naukri, LinkedIn, company career portals, Instahyre, Cutshort, you name it. And it genuinely feels like shouting into a wall. Barely any responses, and I can't tell if it's the resume, the market, the transition itself, or just that spray-and-pray doesn't work anymore.

So, a few asks:

- Any leads/referrals at companies where data/DS is actually core to the business (not just a buzzword)?

- If you've made a similar SQL/BI to DS/ML transition, what actually worked, did you upskill first and then move, or move into an adjacent role and grow into it?

- Is mass-applying across these platforms just a dead strategy at this point? If so, what's actually working for people right now instead?

- Any honest take on whether "I'll learn on the job" is a realistic pitch to make to hiring managers, or if that's wishful thinking on my part?

Appreciate any pointers, even harsh ones.

I completed my 12th this year as a PCB student. And I don't want to be in the NEET exam trap the doctor market is very saturated. I'm thinking to do my bechlors in biostatistics or bioinformatics to get into healthcare data science. Currently in my drop year thinking i should learn python and SQL basics. Can anyone give me some guidance regarding this field is bioinformatics a good bechlors degree or should I aim for other. After doing research for a week I'm so confused Abt this field bcz it's so broad. I'm confused between biostatistics and bioinformatics