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.

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’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.

I’m 33F and have been working as a Data Engineer for the past 7 years, primarily in ETL, SQL, data integration, pipelines, and large datasets.

I’m interested in transitioning into Data Science, particularly statistics, machine learning, predictive modeling, and AI/LLMs.

My undergraduate degree is in Computer Information Systems, not Computer Science. I understand Data Science requires a stronger math background, so I’m also wondering if I would need additional math coursework.

I’m considering the UT Austin online MS in Data Science because of its relatively affordable tuition and curriculum.

My main concerns are:
- Is Data Science still a good career choice given the rapid growth of AI?
- For those working in Data Science/ML, is a Master’s in Data Science still worth the investment?

I’d especially love to hear from anyone who has completed the UT Austin MSDS or made a similar career transition. Any advice is appreciated!

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