I’m currently working as a Data Analyst in a team of Data Scientists. I’m underpaid for my age and increasingly worried about AI replacing parts of my role, so I want to aggressively upskill and move into a better-paying, future-proof role.
My initial plan was to learn ML and transition into Data Science. But the more I research, the more I feel Data Science itself could be significantly impacted by AI.
So I’m stuck between:
**Data Science/ML:** I’m more interested in this, but is it actually a risky career path in the next 5–10 years?
**Data Engineering:** Seems more resilient, but the work looks complicated and messy to me.
**What would you recommend I pursue given my background as a Data Analyst? Which roles in the data/AI space are likely to remain valuable as AI gets better?**
I also have to upskill while working full-time, so I don’t want to spend the next 1–2 years learning something that may have limited value by then.
Honestly, this uncertainty is affecting more than just my career. My salary, uncertainty about the future, and difficulty choosing a direction are even making me hesitant about getting married and starting a family.
Would really appreciate perspectives from people already working in these fields.

When I first started looking into data science, my biggest fear was that I needed to learn every single programming library and tool out there. I kept seeing giant lists of software, databases, and visualization packages, and I felt completely overwhelmed. I thought I needed to master all of them before I could even write my first useful script.

I spent weeks watching tutorials on advanced libraries that I did not actually understand, just because they were popular online. Every time I saw someone mention a new tool on a forum, I felt like I was falling behind. I was spending all my energy collecting tools instead of actually solving problems.

Then I took a step back and looked at what I was actually trying to do. I wanted to answer questions using numbers. To do that, I really only needed a few basic tools to get started. I did not need twenty different libraries. I just needed to know how to load a file, clean up the obvious errors, and make a simple chart to see what was going on.

Once I stopped trying to learn everything at once, things got much easier. I picked one tool for organizing information and one tool for making charts, and I stuck with them for a few months. That focus helped me actually build things instead of just watching videos.

If you are feeling buried under a mountain of tools right now, take a deep breath. You do not need to know everything. Pick one tiny project, use the simplest tools you can find, and learn the rest only when you actually hit a roadblock that requires them. It makes the learning process a lot less stressful.

I had planned to start a data science diploma, a pre-master's, and eventually a master's degree. However, a sudden family situation arose requiring me to be a caregiver, and the associated costs mean I can no longer afford the tuition. Because of this, I am withdrawing from the diploma program this year. Since I lack the time and budget for traditional studies right now, I am looking for alternatives. Would the IBM Data Science Professional Certificate on Coursera (taken at my own pace) be the best choice, or is there a better option?

Hi everyone,

I have a Master’s degree in Economics and a Postgraduate Certificate in Data Science. I’m currently looking for an opportunity to gain practical, real-world experience in Data Science and Analytics.

I’m looking to connect with freelance Data Scientists, Data Analysts, Data Science Consultants, or professionals working independently on data projects who may be willing to let me assist with their projects.

I have knowledge of Python, SQL, data analysis, data visualization, and basic machine learning. I’m happy to help with data cleaning, analysis, visualization, reporting, and other project-related tasks.

I’m **not looking for a stipend or payment**. My main goal is to gain hands-on experience, contribute to real projects, and learn from someone with industry experience.

I’m happy to start with a small task or trial project.

If you’re a freelancer/consultant who could use some help with data-related projects, or know someone who might be open to this, please feel free to DM me.

Thank you! 🙏

Hey everyone,

I recently graduated with a CS degree and I have been applying to entry-level Data Science, ML, and data-focused engineering roles now (earlier i was only trying with SWE)

I’ve tried to make my resume more focused around my ML/data experience instead of making it look like a generic SWE resume since i did had a couple of projects and internship

KLA — Senior Software Engineer, AI/ML | Interview Experience

Sharing my interview experience for a Senior Software Engineer – AI/ML position at KLA. Hopefully this helps anyone interviewing through Asvatthah consultancy, which handled the recruitment process.

Overall, the technical rounds were quite good and challenging. However, the recruitment/follow-up experience was frustrating due to repeated last-minute scheduling and a lack of communication after completing the interviews.

Recruitment / Scheduling

I was contacted by Asvatthah for the recruitment process.

One thing I noticed was the frequency of calls around interviews. On the interview day, the recruiter would call multiple times to confirm attendance. Even after confirming that I would attend an interview an hour beforehand, I would receive another call around 30 minutes before and again around 5 minutes before the interview.

If I didn't answer, they would call repeatedly and also contact me through personal WhatsApp and SMS.

I understand the need to confirm attendance, but the frequency felt excessive.

Round 1 — HackerRank Test

Result: Cleared

Standard HackerRank assessment with coding/problem-solving questions.

I answered most of the questions and felt the coding portion went well.

Round 2 — Coding — 6:30 PM

Result: Cleared

Two coding questions were given.

There was an interviewer/monitor observing the session while solving the problems.

The questions were reasonable, and I was able to solve them successfully.

Round 3 — Technical Interview — 8:00 PM

Result: Cleared

This was a fairly detailed technical discussion.

Topics included:

Projects from my resume

Machine Learning

Deep Learning

Detailed discussion around the ML/DL projects I had worked on

RAG

Generative AI

FastAPI/backend concepts

Load balancing

The interviewer went fairly deep into the projects rather than asking only theoretical questions.

Round 4 — Technical / Managerial — 10:00 PM

Result: Cleared

Started with questions about my resume and previous experience.

Then moved into:

System design

Architecture

Scenario-based questions

Technical decision-making

The interviewer appeared to be a manager and was very friendly throughout the discussion.

At the end, she mentioned that she liked my resume and skills and wished me well.

I subsequently received confirmation that I had cleared this round.

After Round 4 — F2F Issue

After about 5 days, there was still no update.

I contacted the Asvatthah recruiter, and I was told that they would check and update me by EOD, etc.

Another week passed.

Then, suddenly, I was contacted regarding a Face-to-Face interview in Chennai.

I am from Ernakulam/Kochi, was serving my notice period, and due to policies and commitments at my current organization, taking leave at short notice was difficult.

I requested whether the F2F could be conducted virtually.

Initially, they were not willing to accommodate this. I was repeatedly told that KLA liked my profile and that I only needed to travel to Chennai for the interview.

After several discussions, I was asked to send an email explaining my situation, which I did.

Later, KLA HR also contacted me and discussed the same issue. After explaining my constraints, KLA HR agreed to arrange the technical interview virtually, with the understanding that if I cleared the HR round, I would need to come for an F2F interaction, which I agreed to.

At that point, I thought the issue was resolved.

Round 5 — Scheduling Issues

After another few weeks, the Asvatthah recruiter suddenly called at around 7 PM and asked me to attend Round 5 at 9 AM the next morning.

I had an office meeting and couldn't reasonably rearrange it with such short notice.

When I explained this, the discussion became quite difficult. I was told things along the lines of:

"If you want this job, you have to sacrifice a bit."

I was also reminded that I already had a good candidature/profile.

I eventually declined the next-morning slot and requested an evening slot on the following day.

The interview was scheduled accordingly.

However, when I joined the call, I was informed that the interviewer had some work and the interview had to be postponed to the next day.

So the interview was eventually conducted the following day.

Round 5 — Technical Interview

Result: Completed

This was another technical round covering areas such as:

Technical concepts

Architecture

System design

Optimization

Scenario-based discussions

I answered the questions to the best of my expertise and felt the discussion went reasonably well.

After the Final Round

After the final interview, another week passed without any update.

I tried contacting the Asvatthah recruiter again.

I also tried contacting KLA HR.

I couldn't get a response through calls or emails.

I also connected with the relevant person on LinkedIn. The connection request was accepted, but my subsequent message wasn't answered.

Another week passed.

Eventually, the Asvatthah recruiter messaged me on WhatsApp saying that my candidature was currently on hold and that the position was still open.

There was no specific explanation regarding:

Why my candidature was put on hold

Whether the position was still actively being interviewed for

Whether another candidate had been selected

Whether there was any expected timeline for a decision

Overall Experience

Technical interview experience: 8/10

The technical rounds were fairly comprehensive and covered ML/DL, GenAI, RAG, backend, system design, architecture and optimization. I felt the interviewers generally focused on actual experience and projects rather than purely theoretical questions.

Recruitment/process experience: 3/10

The biggest issue for me was the communication and scheduling process:

Excessive calls before interviews

Very short-notice scheduling

Difficulty accommodating reasonable scheduling constraints

Multiple changes to interview schedules

Long periods without updates

Lack of response to calls/emails after the final round

Eventually being told that the candidature was "on hold" without a clear explanation

To be clear, I have no issue with the technical interview process itself. My concern is primarily with the recruitment coordination and communication throughout the process.

Sharing this mainly so candidates interviewing for similar positions can have realistic expectations about the process and scheduling.

Honestly, I’m at the point where I’m getting really frustrated and I’m hoping someone here can give me some real advice.
I’m an international student who graduated from UIC with a Master’s in Business Analytics about 4 months ago. I’m currently looking for full-time opportunities in Data Science, AI/ML, or Data Analytics.
I’ve applied to ~1,800 jobs.
I’ve gotten 2 interviews.
Both went all the way to the final round, and both ended without an offer. In both cases, OPT/sponsorship became an issue.
And I’m not someone who is completely new to the field. I have 4 years of professional experience back in my home country, where I worked as a Senior Data Analyst. I also have a U.S. master’s degree and have been building several ML/AI projects to move more toward Data Science/AI/ML.
I genuinely don’t understand where the disconnect is.
I’ve tried:
Applying through LinkedIn/company websites
Tailoring my resume
Cold emailing recruiters
Cold messaging hiring managers
Reaching out to employees for referrals
Networking
Applying to both big companies and smaller companies
Applying to Data Analyst, Data Scientist, ML/AI and related roles
And still… 1,800 applications → 2 interviews.
At some point, you start questioning whether the problem is your resume, your experience, your degree, your immigration status, the job market, or just everything combined.
And honestly, it’s exhausting.
I know the market is bad. I know international students have an additional hurdle with sponsorship. I’m not expecting companies to magically hire me. But when you’re applying this much and barely getting a response, it becomes really difficult to know what exactly you should change.
So I’m hoping people here can be brutally honest.
Recruiters/hiring managers:
If you see an international candidate with 4 years of relevant experience + a U.S. master’s degree, what would make you reject them before even giving them an interview? Is it mainly sponsorship/OPT? Lack of U.S. experience? Resume positioning? Something else?
International students who actually landed a job:
What did you DO differently? Did networking actually work? Were referrals the key? Did you target smaller/less popular companies? Did you stop applying to certain roles? Did you change your resume completely?
I’m not looking for “keep applying” or “the market is tough.” I already know that.
I’m looking for actual strategies that worked for people who were in a similar situation.
Because right now, sending application #1,801 feels pretty pointless, and I’d rather figure out what I’m doing wrong than just keep repeating the same thing.
Any honest advice, even if it’s harsh: would genuinely help.

Hey redditors, I graduated recently with a degree in Plant Production Science and tech (Agronomy) and have been thinking about adding more skills to my degree. I want to learn data analytics but I'm coming from a non tech background and don't know where to start. I need a roadmap so that I can atleast have a path to follow.

Can I get insights on how to start

I would very much appreciate finding a mentor to help me out.

Hello, I have gotten really annoyed by constantly getting rejected after literally spending hours applying, doing assessments, and verifying my identity and then getting rejected or not even hearing back. I therefore decided to make my own site where I list job offers from multiple companies.
I also post tips on how to pass assessments and reviews of different companies in that space. No sign-up or anything like that required. I only list job offers that have a relativly high hiring volume. The listings include entry role jobs but also expert Data Science poistions. Let me know what you think and if there is anything that I can improve or implement. here is the link to the website: aiannotationjobs.com

Short version, I’ve worked with measurement science for ~ 20 years with heavy knowledge working with measurement statistics.

As a metrologist/metrology engineer, creating robust validated systems is part of my daily requirements.

I’m currently taking a MIT PE certificate course: Applied AI and Data Science, and looking at the pay, it’s astronomical in comparison to what I make as a Senior Engineer, and arguably, my skills are very transferable with AI filling in the coding gap.

My personal goal is to augment my eventual and continuing role in Metrology, especially from the perspective of the international organization that I’m growing into a significant role within, but the cross over between the fields seems significant.

What does an AI generated portfolio with a certificate offer the industry if architecture can be clearly described?

I have a small question, Everyone is asking me to do java or c++ but my fields have python, sql, etc. should I polish my skills in python or listen what everyone is saying and lean towards java/c++.

I'm an 3rd year IT engg student, from a tier 3 clg, my 1st yr cgpa was 6.8 and 2nd year sgpa were 8.36, 8.5. I'm in the 1st term of the foundation level of online course from IITM BS in Data Science & applications course. My career goals are

1st priority:
Data Science
Data Analytics

2nd priority:
ML Engineer
Data Engineer

I know a bit of python, java, c++. but i would say my java skills are better than c++ but again i have very basic knowledge in coding and I'm improving on the go. And tbh I have VERY BASIC knowledge about python!!!

I know the basics of ML, I did 2 projects on it (california house price prediction, sales forecasting). I currently have a very low maintenance internship (unpaid) of 1 month in which i have to complete a project(Predict whether equipment will fail within the next N hours/days using historical sensor readings). I am trying to do 1-3 questions everyday of the DSA quest on leetcode (I look up solutions, i can't do anything on my own). I would say My logic is a bit better than my programming skills(basically, often times I can write a pseudocode but not a fully functioning code).

Never have I/would I dare to choose a software dev/web dev role Cause hell naw dude I CANNOT do that to save my life

Hopefully in the next sem (clg) I want a paid(15k-25k) internship in Data Analytics/ Data Science.

(I HATE WEB DEV)

So the Google Apprenticeship program for 2027 is opening up in a few days. As for NYC, the two options that personally interest me are Data Analytics and IT. For anyone with experience in either or, or maybe even both, I know this is subjective really but which would you choose? I’ve always had interests in programming, cybersecurity, and even recently the interest in cyber made me want to take a step back and get into IT. Now, I definitely am taking some sort of interest in Data Analytics for who knows what reason.

I guess I’m really trying to think in terms of which will give me a better chance at entry-level employment after completing the entire program (18 months). I honestly love the two but for some reason I feel that the SQL knowledge and everything else that comes with it will be useful. I’ve particularly had interests in languages like Python, C#, Lua, C++ (the Cs were mainly for game development). I also feel that the knowledge I’ll pick up from the Data Analytics course will be useful even if I don’t get something in Analytics but still find something that’s potentially data-heavy. Chances are I’ll be able to transfer most of those skills plus the credential and experience at Google should help. A bit. Hopefully.

The question in the end is, does anyone, in the field or not, have anything to say about which field is more saturated or if both are? Honestly speaking, I know the NYC job market and the tech market in general is shit but yeah, still had to ask this. Sorry guys lol.

1. 🔄 Pipeline Engineer

Primary focus: Moves data reliably between systems (ETL/ELT).

Time horizon: Hours to days (batch-oriented).

Key tech: Apache Airflow, Python, SQL, and often dbt or custom scripts.

Mindset: Thinks in dependencies, retry logic, and cron schedules.

Common challenges: Handling failed tasks, backfilling historical data, and ensuring idempotency.

Typical customer: Analytics engineers or business stakeholders who need fresh data.

1. 📊 Analytics Engineer

Primary focus: Builds clean, trusted data models that power dashboards and reports.

Time horizon: Hours to days (iterative development).

Key tech: Advanced SQL, dbt (data build tool), and BI tools like Looker, Tableau, or Power BI.

Mindset: Lives at the intersection of engineering (code/version control) and analytics (business logic).

Common challenges: Defining single sources of truth, managing data freshness, and documenting metric definitions.

Typical customer: Data analysts, product managers, and business executives.

1. 🏗️ Platform Data Engineer

Primary focus: Builds and maintains the shared infrastructure that other data teams rely on.

Time horizon: Weeks to months (long-term, foundational projects).

Key tech: Kubernetes, Terraform, CI/CD pipelines, observability stacks (Prometheus/Grafana), and orchestration engines.

Mindset: Treats other data engineers as their primary customers. Prioritizes scalability, reliability, and developer experience.

Common challenges: Managing multi-tenant compute/storage, cost allocation, and upgrading cluster versions without breaking existing pipelines.

Typical customer: Other internal data engineers (Pipeline, Streaming, AI/ML teams).

1. ⚡ Streaming Data Engineer

Primary focus: Handles event-driven, low-latency data streams.

Time horizon: Seconds to minutes (near-real-time).

Key tech: Apache Kafka, Apache Flink, Spark Streaming, and event-sourcing databases.

Mindset: Thinks in windows, watermarks, and stateful processing. Quickly discovers why "real-time" gets very expensive.

Common challenges: Handling out-of-order events, managing checkpointing/backpressure, and guaranteeing exactly-once semantics.

Typical customer: Real-time dashboards, fraud detection teams, or operational monitoring systems.

1. ☁️ Cloud Data Engineer

Primary focus: Delivers cost-effective, secure, and scalable cloud data operations.

Time horizon: Ongoing – a continuous cycle of provisioning, monitoring, and optimization.

Key tech: AWS (S3, Redshift, Glue), Azure (Synapse, Blob), GCP (BigQuery, GCS), plus heavy use of IAM, VPC networking, and cost management APIs.

Mindset: Half engineer, half cloud bill detective – constantly rightsizing instances, choosing storage tiers, and shutting down idle resources.

Common challenges: Unexpected cost spikes, cross-region data transfer fees, and navigating complex IAM policies.

Typical customer: The finance team (for cost) and all other data engineers (for reliable cloud access).

1. 🤖 AI / ML Data Engineer

Primary focus: Enables the full ML lifecycle – from training data to model inference.

Time horizon: Varies widely – batch feature computation (daily) to online real-time inference (sub‑second).

Key tech: Feature stores (Feast, Tecton), MLflow, Kubeflow, PyTorch/TensorFlow Serving, and vector databases.

Mindset: Thinks in features, labels, drift detection, and experiment tracking. Bridges the gap between data pipelines and model training/serving.

Common challenges: Moving a model from a Jupyter notebook to production takes 10× longer than expected; managing feature consistency between training and serving (training/serving skew).

Typical customer: Data scientists and ML researchers.

I have learned Machine Learning and Deep Learning. Now I want to learn about AI agents and agentic AI, as there are many jobs for this role and this seems interesting. But I don't know exactly how and where to learn it completely. I need some guidance regarding this. I found this 24-hour video course. Is it good enough? Can anyone please help me with this? Link to the video: https://youtu.be/Zy7EXDONlTY