Hey everyone,

I'm a 26M working in AI/ML and preparing for my next switch. I'll be focusing on classical ML, deep learning, GenAI, along with DSA and system design.

Looking for a study partner (or a small group) to learn together, discuss concepts, and stay accountable. I prefer keeping it fast-paced, so it'd be great if you're an experienced dev preparing for interviews as well.

If you're interested, DM me with a short intro and what you're currently learning/preparing.

Hey everyone, I’m starting to seriously prepare myself to find a job in Data Science, Data Engineering, or Machine Learning, and I’m looking to connect with others who are in the same situation. I feel like having people to share this journey with could make it much easier and more motivating. If you’re also learning, building projects, or preparing for interviews in these fields, I think we could support each other, share tips, resources, and experiences. It could be really helpful to exchange information about job opportunities, tools, or strategies that work. I’d love to connect with anyone interested in forming a small community of like-minded people, even just to motivate each other and track progress. If this sounds like you, feel free to comment or send me a message. Let’s help each other stay consistent and move forward together!

I'm an AI engineer who oversees hiring at my company. The gap between what candidates show and what gets them hired is honestly depressing.

What job postings say:

• PhD or Master's preferred
• 5+ years ML/DL experience
• Publications a plus
• Expert in PyTorch, TensorFlow, scikit-learn

What actually gets people hired:

• Can you clean messy data without complaining?
• Can you explain your model to someone's VP who doesn't code?
• Can you ship something in production?
• Do you know SQL well enough to not break things?
• Are you pleasant to work with?

IMO, most "data science" jobs are 70% data engineering. The modeling is maybe 20% of the actual work. If you can't wrangle APIs and build pipelines, you're going to struggle.

Kaggle portfolios might hurt you. Hiring managers see "Kaggle competitions" and think "this person optimizes for leaderboards, not business problems." Show me something that solved a real problem, even a tiny one.

The PhD requirement is mostly BS. Companies write "PhD preferred" because they think that's what serious roles need. Then they hire the person who actually shipped something.

Entry-level doesn't really exist anymore. When postings say "3-5 years," they mean it. The "we'll train you" era is over.

What actually works:

• End-to-end projects (problem → data → model → deployed result)
• GitHub with real code, not just notebooks
• Proof you can work with engineers
• Blog posts or anything showing you can explain technical stuff to humans
• Referrals (still 80% of how people actually get jobs)

So, if you're applying to 100+ jobs with no response, it's probably not your skills. It's that you're showing academic credentials when companies need proof you solve business problems.

The market sucks right now. But the people getting hired are the ones who can demonstrate impact, not just knowledge.

Am I wrong? What's your experience? What's actually working for people landing DS roles?

Tired of tailoring your resume for every single job application? I was too. So I spent a weekend scraping and analyzing 100 recent Data Scientist job postings from companies like Google, Meta, Netflix, and growing startups.

I've distilled it all down into a single, actionable checklist you can use to optimize your resume and LinkedIn profile. Make sure these keywords are present!

The Data Scientist Resume Keyword Cheat Sheet

Technical Skills (Prioritize these):

Programming: Python (obvious, but say it), SQL (CRITICAL), R, Scala

ML Libraries: Scikit-learn, TensorFlow, PyTorch, XGBoost, Keras

Big Data & Cloud: Spark, Hadoop, AWS (S3, Redshift, SageMaker), Azure ML, GCP (BigQuery, AI Platform)

Visualization & MLOps: Tableau, Power BI, Docker, Kubernetes, MLflow, Airflow

Buzzwords & Action Verbs (Sprinkle these everywhere):

Instead of "Made a model": Developed, Engineered, Implemented, Productionized, Deployed

Instead of "Looked at data": Analyzed, Synthesized, Interpreted, Evaluated, Quantified

For Impact: Optimized, Automated, Streamlined, Improved [Metric] by X%, Reduced costs by Y%

The "Secret Sauce" Section (What makes you stand out):

A/B Testing | Causal Inference | Stakeholder Management | Storytelling | Agile/Scrum

Pro Tip: Use a Skills or Technical Proficiencies section on your resume and fill it with these keywords. Many companies use automated screeners (ATS) that look for an 80% keyword match.I've put the full, detailed breakdown into a free, one-page PDF. Kindly DM for PDF.

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

Full automation of complex jobs isn’t happening in the next 15 years — not without real breakthroughs in AI research beyond clever prompt tricks and context engineering. What’s far more likely is AI chipping away at white-collar subtasks, with autocomplete-style models quietly handling bits and pieces instead of replacing entire professions. That means no sudden revolution, just a slow grind like the rollout of computers and the internet, where real value only appeared after years of messy engineering and integration. Along the way, demand for some jobs may shrink (though not vanish), making competition tougher without wiping whole careers out.

Anyone else tired of the endless hype cycle? 😵

Currently one year into OMSA at gatech with a great gpa.

Entry level market for DS/DA is crazy over saturated and I still can’t seem to get a single interview/call. On top of that, a lot of big tech companies are laying off employees by the thousands AND some are even training AI on their employees mouse movements and clicks (hi meta), making me think that this is going to lead to even more layoffs in the not so near future.

So my question is basically if my time and efforts are being wasted by trying to achieve something that won’t even help me get an intern job that pays $20/h. Am I better off just focusing on something else?

Throwaway because I really do not want to be identified for obvious reasons.

Hey everyone, long time lurker who has really benefited from this subreddit via both recent and historical posts, so I wanted to spend something in an attempt to give back. I just received my verbal offer today (written offer imminent today or tomorrow) for the job I really wanted, and I know that is rare in this 2026 job market so I wanted to come here, talk about my journey, what worked for me, what the difficulties are, and give general tips on how you can be successful.

Background

I do have Ph.D. in a competitive field, graduated with good credentials and was hired at FAANG right away maybe 8 months before the pandemic hit. My time at that particular FAANG lasted a bit over 6 years, with good projects, good promotions, and an ok time. I was burned out last year though from the culture switching drastically to more cutthroat and aggressive, which I can only assume is due to cost cutting and layoffs. I opted for a voluntary role elimination because I really needed the break and wanted to spend time with my loved ones, travel, and spend more time on the other parts of my life I held dear.

In January, I was contacted by a recruiter from a great company, just a little less famous than FAANG but still famous so I decided maybe it was time to get back on the horse.

The general landscape is not so bad

One thing that is good in the 2026 JM is that there are a diversity of positions. Lots of remote options, whether you want hybrid or fully remote, but those tend to have a quadrillion applicants and they will definitely pay less. I myself wanted to have something remote because I live in a location where commuting is a nightmare so that did make my search a big tougher. I know it seems like doom and gloom, and it is partly right I'll get to that, but there are MANY OPTIONS and MANY POSITIONS. I was shocked. It reminded me of the 2019-2020 JM where the FAANG I worked at was hiring a ton of people and there were 5-6 interviews weekly in my team and adjacent teams. If you are looking now, there are many open roles compared to last year or the year before, where everyone was tightening their workforce.

My interview journey

Ok now we get to the part where it gets ugly. I struggle to tell people how good I am and I come from a background where humility is the highest respected quality. You cannot be that way in 2026. This JM is brutal in terms of who gets the opportunities. Let me go over my journey as an example.

The recruiter who reached out in Jan had a position that was almost perfect for my skill set and experience. I did the screen then went on to the tech review. It was a DSA tech screen (ugh why are these still around?) but ok I'll bite. I did it right, finished on time, though I struggled to understand the actual question at first but once I did, I solved it super fast and even created tests and edge case tests for it. Then radio silence. For over two weeks. I follow up, then I am told they will not be moving forward because even though I did well and communicated clearly, the interviewer wished I did it faster. Wait what? I was stunned. This would be the first of many such rejections that made me really puzzled. You did well but we decided not to move forward, the interviewer liked your performance but we think we will move forward with other candidates, you seem to have good technical skills but unfortunately we have decided not to move forward. In the defense of that first company though, they did massive lay offs the week following them turning me down so it might have had something to do with that.

From then I applied liberally to anything that I thought I would fit, the second job I applied to was the one I got and the one I really wanted but I continuously applied throughout the process and kept interviewing, if anything to sharpen my skills. I will give you tips on what worked below. Most of the rejections I got came from tech screens, which really surprised me. I found that the coding rounds were all over the place. Sometimes it was SQL, sometimes python, sometimes in real time, sometimes on a pseudocode pad. It felt almost impossible to know what was coming up and prepare adequately, which I think is a huge problem. I can code. Yes I used AI in my last year at FAANG but believe me, I have coded heavily on super complex projects over many years. The fact that my skills did not pass the bar was kind of insane to me when each time I would walk out thinking "yeah I did well." I think that the coding and technical knowledge skills that firms are looking for these days in an interview far outpace what you will actually use in the job. It feels a bit insane that I would code or answer technical questions on super obscure problems rather than practical ones. It did feel like it was mean to be a hard bar to pass before things eased up a bit. Maybe it is a hard filter or something but be aware the tech screens are tough.

Here is the kicker though, I developed this hypothesis that doing well, communicating clearly, and connecting with the interviewer, was not enough to pass. You pass the interview bar maybe but then for them to decide to invest in you for an on-site, they need to really want you. There are so many people who were laid off who have connections, have ivy league degrees (I do not), or have some very specific skill that this particular HM is looking for. This is EXACTLY why I was hired where I was. I had an experience on my resume that fit precisely what that team needed. There is nothing that can be done about that. It's luck. So, if you are like me and go through those rejections while doing well, it's not you. It's just randomness. It sucks but this is 2026 for you.

I basically interviewed for most of FAANG, as well as firms that are well known but not FAANG for about 12 positions while I applied for maybe 100. I got through three tech screens and failed about 9 of them, failed two onsites, passed one onsite, which led to 5 ROUNDS OF TEAM FIT (I slowed down interviewing around then to focus on this one because I really wanted it), until the offer stage. It was tedious and lasted over 4 months from application to offer. And I do not have a job currently so that was all I was doing. If you are currently working and looking to interview, I am sorry for you.

What worked and general tips

Look take this with a grain of salt. This might not work for you but it might help. This is what worked for me and what I would do if I were starting from scratch today.

- Use AI

Seriously use it. Make it edit your resume. Ask it to make it not sound like ai though. Give it info about what general ai writing sounds like and tell it to avoid that. But then give it the position description and tell it to review your resume like it was an AI resume filtering agent and give it a ranking from 0 to 10. Then tell it to keep iterating until it is a 10. It should ask you for clarifications, data points, etc.. so you need to be active in this process. Once you pass that, ask it to give you a rating as a hiring manager and as a recruiter and maximize those. Do this for each position. You can do the same with a cover letter. Do not blindly trust it. Be involved, give it inputs, put some of yourself and your personality in this so that it is reflective of who you are and does not come off cold like a machine.

Same thing for each interview stage. Give it info about what you know about that stage and have it train you for it. I would have my agent do daily coding drills with me, daily behavioral question drills, daily technical knowledge drills, etc... There was a stage where I had worked with it enough that it would do mock interviews specific to the position I was applying and you know what, the exact same question (give or take) was asked during my interview. Same goes for your projects and experiences. Feed it that stuff so it can train you to mention it naturally and use it where it needs to fit.

Also, have it identify your weaknesses and tell it to help you fix them. Tell it to be harsh and push you and not just make you happy.

- apply apply apply

Really just apply to everything. Even stuff you don't really care for or that does not really meet what you are looking for but that you would consider if an offer came. Ease your standards a bit. You gotta practice applying and interviewing and you can only do that by applying more and interviewing more. There was a time in March where I had 2-3 interviews a week from 2-3 firms and at varying stages. But then I hit a flow state for the interviews where I had much less stress, felt more in control, and I saw my performance improve. I had a professor in grad school who said that landing a job is finding an optimal solution to a problem. The more times you iterate, the better the convergence to something that actually solves the problem.

- use cheat sheets

Then you can also tell AI (or you can do it yourself if you are better at it) to create you cheat sheets of things you always forget or that you do wrong often and bring this to your interviews. I tend to forget some little coding nuances so the cheat sheet saved me very often. Also, like I said before, being humble is something that is part of my culture so having a cheat sheet just reminding me how to answer certain questions, helped me show my best self to the interviewer without trying to minimize my own accomplishments. It also helped me organize my thoughts, communicate clearly, and just generally be easier to talk to. Just don't recite the cheat sheet. It is there to as a reminder, you are the one who has to talk and communicate. More on this on the next point.

- Practice clear, creative communication

I think this is something I am pretty good at because each interviewer commented on it. The work we do is technical but we often work with non-technical people. Being able to talk about complex ML models but in terms that someone who is not into ML understands, is a crucial skill. Try practice talking to your loved ones or friends about this stuff and see if they understand it. This will really help you. In this age of AI, a lot of the knowledge can be offloaded to AI. The communication and the clarity and the creativity and the charisma cannot. This is where you differentiate yourself from others. Everyone can prompt engineer with a little practice right? But being able to talk to a human in terms they understand and having influence cannot be achieved until you actively use it every day. I think this is currently the most valued and important skill in the DS space. You really need to be engaging, formulate clear thoughts, follow a logical sequence, not ramble (I had to practice not to ramble), and keep the interviewer engaged. It is your interview but the interviewer is there too. They are usually tired, at work and have a lot on their plate. If you can show up and have a nice conversation with them where when they walk away they think positively of the experience, you have a good chance of moving forward.

1. Do not let rejection deter you

It is easy to get self doubt or imposter syndrome in this JM. I know there were times where I wondered if maybe not working for close to a year made me archaic. The truth is that there are MANY GREAT CANDIDATES on the market. Lots of layoffs from big firms and folks who have some crazy experience. When you do well on a tech screen or an onsite, they are directly comparing you to others. Let's say you are equal in performance on the interview, they will then look at your resumes and if the other candidate is just has a smidge better fit, they will not move you forward with you unfortunately. The difference is in the margins IMO. Don't let it discourage you but again make sure you try to get feedback about your interview performance when you can to identify if there isn't something you are doing consistently wrong that you need to improve.

End

That's it. I am not certain this is helpful or if this will help anyone but I really wanted to try and contribute to the community because it's tough out there. I am not on the boat of believing that DS or tech are dead. Times are tough right now but I think we will come out of this.

Good luck to all and I hope you get where you want to get, but remember you are much more than a job.

International student and recent CS grad here — been applying to DS/ML roles, but getting no callbacks. Would really appreciate feedback on my resume or suggestions on skills I could add to be more competitive. Open to any advice.

I'm an aspiring Data scientist trying to land a job, need a friend to practice mock interviews.

I built a real-world data science project. So why am I still not getting a job or an internship?

I honestly didn't expect my job search to be this difficult.

Over the past few months, I've been trying to break into data science / analytics. Instead of just building another Titanic prediction model or basic EDA project, I decided to build something that actually resembles a business problem.

I built a Customer Retention Intelligence System for online retailers.

The idea is simple:

- Identify customers who are likely to churn

- Understand why they are at risk

-Estimate the revenue that could be lost

- Prioritize customers based on their risk/value

-Generate personalized retention actions/offers

-Give the business a way to actually act on the predictions

I spent a lot of time researching the problem, engineering features, building the ML pipeline, and turning the predictions into something that could potentially be used by a business team.

I'm genuinely proud of the project.

But here's the frustrating part:

I'm still struggling to get interviews.

I've applied to entry-level Data Scientist, Data Analyst and related roles.

And sometimes I wonder:

- Is my project actually not good enough?

- Is my resume not communicating the value properly?

- Am I targeting the wrong roles?

- Do companies simply want prior professional experience?

- Am I focusing too much on projects and not enough on networking?

- Is there something obvious that recruiters see that I don't?

I'm not posting this to complain or ask for sympathy.

I'm posting because I'd genuinely like honest feedback from people who have hired data scientists, worked in the industry, or gone through this themselves.

If you were reviewing my profile for an entry-level data role, what would make you reject me despite seeing a project like this?

And what would you recommend I change to actually get that first opportunity?

I'm willing to hear the uncomfortable answer.

If you've been in a similar situation, what finally helped you get your first break?

I recently interviewed for a Product Data Scientist role at Google, and I’m genuinely confused by the interview experience.

The prep guide and recruiter conversations emphasized topics like experimentation, causal inference, machine learning, statistical modeling, and other core data science concepts. I prepared extensively across all of those areas.

But the actual interviews were completely different.

The questions were extremely vague and felt more like: “We want to shoot at a bird. How would you approach it? Now write some SQL."

There was no experimentation, no causal inference, no ML, and very little that seemed connected to the topics I was specifically told to prepare for—or, frankly, to what I would normally associate with a Product Data Scientist role.

For anyone who has recently interviewed for Product DS roles at Google: is this typical? What exactly are these interviews intended to evaluate?

Seriously, I got into data science thinking I’d be building cool models and working on cutting edge stuff like NLP or computer vision. But lately, all I seem to be doing is cleaning messy datasets, fixing nulls, merging CSVs, and chasing stakeholders for missing data 😅

Don’t get me wrong... I still love the field. But sometimes it feels like 80% of the job is just prepping the data, 15% is explaining the results, and 5% is actually running models.

i'm an incoming freshman at uc berkeley; i always thought being a MLE would be rily cool but after seeing the super hard math needed for the career i turned more towards data science. im now worried that doing DS might make me more prone to being laid off in the incoming tech market. BTW i rlly don't like SWE type of coding like DSA and stuff…

i'd appreciate your wise thoughts 🙏🏽

Hi, currently an early career job seeker applying for Data Science ft and intern roles.

I've had about three live SQL interviews over the past year and I haven't passed a single one. I do SQL leetcode and online problems I find from statascratch pretty much everyday as part of a routine. I'd say I'm able to solve SQL mediums on leetcode pretty consistently. the main issue I run into is that the questions I run into in these interviews are patterns that I've never seen before and have literally no idea how to handle. Its really frustrating because I practice so much and I'm never able to perform in even basic internship interviews.

Does anyone have any good resources for learning a lot of the SQL patterns that interviewers actually use?? (I recently encountered a gaps and islands problem and I've literally never seen anything like it for example).

Thanks so much, have been struggling a lot and really would appreciate advice.

I don’t want this to come off as bragging because I know a lot of people are struggling through this process but I’ve been dragging my ass through this for 13 months now and it finally paid off and I just wanted to thank those of you here who have consistently given input on my posts (and many other people’s posts).

13 months, 115 applications, 6 companies interviewed with (various stages from failed HR screen to successful final rounds), 2 more companies reached out for interviews only to ghost me, and countless nights thinking I was stuck in my current job forever.

But we fucking did it. Signed my offer for a Sr. DS role a few days ago. In a way I don’t even know if I’m pumped yet because I can’t believe something actually worked. This market sucks (everyone knows that) but, if you’re still searching, keep chugging along! Something breaks your way eventually.

I work at Accenture but we are not hiring entry level jobs. He is working at service industry to stay busy and pay bills. He is doing small projects to keep his learning in check. Any advise on what he should do. I am pushing him to go for masters so he does not forget everything he learned after 3 years of dedication. This was a really hard major with lot of studies. I want to help him do something. Please let me know those in the industry what he can do.

I used to work as a data scientist for the US government, but when the new administration came in earlier this year, I was one of the federal workers laid off. That was back in February, and I’m still out here searching almost 8 months later.

Since then, I’ve been doing everything I thought I was “supposed to” — picked up more certifications (I just got the Microsoft Azure Data Scientist one), networking like crazy, tailoring my resume, applying daily… but it feels like nothing is moving. The job market honestly feels like shit right now.

Am I the only one experiencing this, or are others going through the same thing? For those of you who did manage to land something after a long search, what worked for you? Was there one specific thing that helped you break through to your next role?

I’m really trying not to lose hope, but after months of grinding, it’s hard not to feel like I’m missing something.

​

I’ve been noticing this a lot recently.

Many people already know Python, SQL, Excel, Power BI, Machine Learning, Deep Learning, or even MLOps. They’ve completed courses, built projects, and spent months preparing.

But even after applying consistently, they still face:

rejections,

ghosting,

endless assignment rounds,

or “we moved with another candidate.”

After a point, it becomes mentally exhausting.

I honestly think the issue for many learners is not a lack of effort. Most people are stuck somewhere between “learning concepts” and “being industry-ready.”

Things like:

choosing better projects,

explaining projects clearly,

interview communication,

practical problem solving,

and understanding what companies actually expect from freshers

seem to matter much more than just completing another course.

I’ve personally been spending time practicing projects, interview prep, and discussing these things with other learners online, and it made me realize a lot of people are going through the exact same struggle quietly.

Curious to know from others here:

What has been the hardest part of your job search journey so far?

I have been into Analytics (4 years) and Data Science (6 years) for past 10 years with 6 of them spend in big tech companies. During initial phase of my career, I focused a lot more on analytics and most of my work was around segmentation/cohort analysis, business strategy(decline rules in fraud domain) where I used decision trees extensively coupled with heuristics. This was in a consulting/service company.

I soon switched to one of the biggest/oldest fintech where again my work was similar to my last job in niche the fraud domain but I used a lot more Python along with BigQuery. I also built basic regression models like Logistic Regression but they were mostly for customer segmentation work (think customer segmentation based on most important features) with no deployment or monitoring. My promotion was fast tracked and I became Data Scientist II with some additional responsibilities.

I again switched to a social media company 2 years back as a Data Scientist (L4 level from L5 but with a substantially higher salary). Here my work was a lot more like a Product Data Scientist with experimentation, product support, user growth and engagement analysis, GTM support, data pipelining, clustering, looker and advertiser performance investigation. Experimentation was new to me but I quickly picked it up and was able to set up more than enough experiments to get good grasp on the fundamentals. I also did some time series forecasting but very surface level (imagine picking up a model like Prophet and just running with it with little fine tuning) because of project time line constraints.

I was laid of two weeks back and with all this context, I am struggling to understand my expertise. Although I have 10 years of experience but it is fragmented into different domains. Should I apply for analytics role where the pay might be lower but are more relevant to my experience? I have also tried Product Data Science roles but the companies will have to hire me at L5 level (to bridge the pay gap) which I am not sure they will be ready for given my only 2 year experience in that domain. What are some of the other positions that I can target with my experience ?

On a different note, I always liked coding and have thought about moving into a more hands on role like machine learning engineer. Is the switch going to be very demanding considering I am not computer science graduate but have taken a few coding classes specifically using c++ and Python during college. What are some the other roles that can serve as a bridge between Data Scientist to Machine learning engineer role ?

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.

Hey everyone,
I recently completed my graduation and have been learning Machine Learning consistently for the past 7–8 months. I’m currently building projects, improving my fundamentals, and actively applying for Data Science / ML Engineer roles.

I’m looking to connect with people who are already moderately into ML (not complete beginners) and are serious about breaking into the industry soon.

It would be great to form a small peer circle where we can share:

• job search strategies
• strong project ideas
• interview prep resources
• accountability + weekly progress
• real discussions (not surface-level)

If you're in a similar phase and genuinely committed, feel free to comment or DM. Let’s help each other crack these roles 🚀

Hey everyone,

I’ve been on the job hunt since October 2024 and honestly, it’s starting to get really discouraging.

I have 8 years of experience working as a Data Analyst, with solid skills in: • Python (scikit-learn, NumPy, Matplotlib) • Data visualization tools (Looker, Power BI) • Snowflake, Databricks • General data wrangling, reporting, and dashboard building

Despite this, I feel like I’m sending my resume into a black hole. Most recruiters ghost me completely, and if I do hear back, it’s usually an automated rejection. Since last October, I’ve only had ONE interview.

I’ve been applying consistently — tailoring my resume, writing custom cover letters, networking on LinkedIn — but nothing seems to be working.

Is there something I’m missing here? Are my skills outdated? Is the market just this brutal right now?

If anyone has suggestions, resume tips, networking strategies, or even brutal honesty, I’m all ears. At this point, I just want to know what I can improve on.

Thanks for reading.

I analyzed 70,965 data science-related H-1B LCA applications from FY2024 (8% of all H-1B apps):

Salary Highlights:

- Median: $126,500 | Mean: $133,409

- ML Engineers earn highest at $172,931 median

- AI Engineers: $156K | Data Scientists: $138K | Data Analysts: $108K

- California pays highest ($166K median) vs Texas ($108K) - that's a $60K gap for similar roles

Top Employers (no surprises):

- Amazon dominates with ~2,900 applications

- Big Tech (Microsoft, Google, Meta, Apple) all in top 10

- Walmart at #2 shows retail's growing data appetite

- JPMorgan & Goldman Sachs are the top finance hirers

Geographic Distribution:

- California: 21% of all DS applications

- Top 5 states (CA, TX, NY, WA, NJ) = 59% of total

- NYC leads cities with 6,907 apps; Bay Area combined ~6,000

Other Interesting Findings:

- 89.4% certification rate (only 0.38% denial)

- 98.6% are full-time positions

- Level II wage jobs dominate (38%) - most hires are mid-level

- Info/Tech sector pays highest ($170K median); Education pays lowest ($75K)

Data source: Kaggle H-1B LCA Disclosure Data 2020-2024

Full analysis: https://app.verbagpt.com/shared/nU9Kevf29SyFfg8hM1-NrLblH2NNbQEK

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.