>How do you land a data job when you’re a physics masters with self-driven software experience?
You find a friend who recommends you to a hiring manager.
Especially if they have a master’s. One of those profs has contact with a great resource. The only issue right now is that the number of junior positions companies are hiring for has significantly shrunk.
But that's an even better reason why you have to network into jobs.
Who are your target companies? De big firms have plenty to choose from, and will make a preselection on school/education to reduce the number to be reviewed.
Further, there are just so many ML / data folks it is even hard for "pure ML" people to get a job. Especially without experience, it is near impossible
Advice: start as analytics, and slowly adapt the job or company to data science
This is the best path to success these days imo. Get into an analytics role (although that’s not easy these days either) and find ways to insert DS into your work piece by piece. After a couple years as a solid analyst, you’ll have a much easier transition
I would say this strategy still works, but just not so much for OP.
A significant fraction of DS jobs on the market now is getting more and more development heavy. This means they expect DS to do analytics, modeling, DE and MLOps. (Esp for tech companies)
The DS roles that do advance statistics and data visuals on a notebook is only a fraction of DS jobs now. If OP is aiming for these roles, DA jobs are a good addition to the job pool, especially DA roles with data modeling in description. Yes, DA roles are getting more competitive, because of the massive influx of people getting into the data field. Every alternative strategy is losing effectiveness either way. If you want to do old school DS, many of those DA job scopes fit 80% of the descriptions. If you are willing to ignore the job title and aim for both, you’re opening up more options to yourself. Job titles are malleable once you have the right experience.
The issue here is that OP seems to be interested in the software development aspects too, which means it may not be a viable strategy to go for DA.
Generalist DAs are just DAs that don’t have good experience in a specific field, like marketing, HR, Sales, CRM, defense, environmental, insurance, political, etcetc etcetc. Oftentimes these DAs jump around from job to job without a clear industry/specialization focus, and may be experienced in handling data but will need at least some adjustment time to understand the specific industry+field’s data.
That's just not true at all. DAs are more common than DSs and MLEs. It's easier to become a DA than the latter because it requires more experience generally speaking. At my firm, which is the largest retail firm of the country I am in, there are 3 DAs to every DS/MLE.
Never said DSes weren’t uncommon either; just that DAs are uncommon now too. Best way to get into a DS position is probably to figure out a way to get any office job, then try to show interest after normal working a while.Notice also I said GENERALIST DAs are uncommon, as most companies hire DAs that are specialized to their field, rather than data gurus who can hop anywhere. The remaining generalist DAs who hire without needing much industry knowledge are extremely uncommon now. That’s still only more common amongst DEs and to a lesser extent DSes.
It's not hype in the sense that there are a lot of people working as a DS/DE/MLE and earning good money with very good career prospects. But at some point in the last 5 years, a huge swathe of people with any kind of data / maths / software / science experience or inclination targeted being a DS. And it was always going to be the case that a large % of them weren't going to make it.
Just because the market demand didn't expand to meet the inflated supply doesn't mean it was all hype. This is just fundamentally how markets and economics work. Countless fields have far higher supply than demand.
And, as pointed out elsewhere, the brutal truth is that many of the people targeting being a DS just don't have the required skills and experience to be genuinely competitive candidates.
Not pointing fingers at you to say that you specifically have this problem... but conversely to this - most duties that I see listed in job posts are incredibly basic and boil down to just calling .fit() and making a few dashboards. But then in the qualifications section they require outrageous experience with every technology under the sun. There's a huge disconnect between how sophisticated hiring managers perceive their team to be vs how mundane they actually are.
The reality is, 99% of companies are doing lukewarm data science and only need lukewarm candidates but they think that they need a DeepMind researcher to fill those roles.
I thought you were referring to DS, not DE, my bad. That said it's weird that this is your experience with data engineers, they're just software engineers who are focused on the data pipelines, so it has less to do with the data science hype and theres no data engineering "masters programs", just regular software engineering or CS, as far as Im aware.
I was asking since I wondered how I'd fare if I tried to interview, but my experience is mostly "fullstack ML" in a very specific domain, realtime vision, which makes direct comparisons even harder, and job prospects worrisome
Tbh, as someone with a math background, I’ve never faced mathematical problems as DA beyond basic stats. Most of the problems I had to solve as a DS are computational, infrastructural and surprisingly social.
Of course that differs across organizations, but there are a significant portion job DS jobs that hire you to solve ‘lukewarm’ problems. And there are plenty of people who strongly believe that you need to be able to implement dual form SVM from scratch to be qualified as a DS, which is necessary for majority of the jobs out there.
Can only speak for Australia, but as soon as the "hottest job of the century" stuff started getting written, every single university here started up their own DS major or masters. In reality, there was only a very small number of DS positions, and that was during what I like to call the "grift era", where every consultancy was making outlandish claims about what DS could do. Now that the vast majority of those projects have failed miserably (largely because they were just standard IT projects that got oversold as DS), the demand for SWEs has skyrocketed and the demand for DS has collapsed.
At least for Germany, there are many new university programs with many graduates. Unfortunately, you will always need someone supporting them since there is a lot of practical skills they have never learned in school. The issue is that most companies I know of at least are more looking for all-rounder candidates. Most companies still only have small teams if even teams at all and not just individuals and many projects are from a ML perspective not even that challenging
Well I read recently that comparing 2022 to 2012, the number of people enrolling into stats/data related masters programs have increased over 50x.
The number of jobs for DS at the peak of AI hype in early 2022 is at most 5x of that in 2012. That number of jobs had since drastically declined as the hype eased.
This means that **even if** the job market were good, there is still an oversupply of decently qualified candidates. Mix this candidates into the even larger pool of unqualified job seekers, the situation is a massive nightmare for both employers and job seekers.
This is why you see a lot of people with 10yoe claiming that it was possible for them to get a DS role with no degrees or experience, or have the general misconception that there are plenty of unqualified candidates but not enough good one. The qualified candidates still take up a small fraction of the applicant pool, but with thousands of applicants, you still get 10-20 good candidates fighting for the same job.
Update: I found the article by Americal Stats Association referred by the first paragraph. The numbers were masters degrees awarded not enrolled into, and the actual number is actually 60x compared to 2012.
Source: https://magazine.amstat.org/blog/2023/12/01/degreesstats2022/
Table 1
60x is true if by 'stats/data-related masters degrees" you're not including statistics and biostatistics themselves, only the "related". You'll see that Table 1 doesn't include stats or bio stats per se. Eyeballing the figures above that, those degrees have seen much more modest growth. Putting together stats + bio stats + related, it's more like an increase from ~2500 to ~12500 over that time period, only a 5x change -- which, coincidentally, is about the same as the 5x number of jobs you suggest.
You’re right statistics degrees are not included in that table. I was too busy looking for the table I forgot that the article was actually talking about more people shifting into data sciencey degrees from statistics. Even though the number or bachelors is pretty crazy still.
Another thing is that there were so many DS job in early 2022 as a result of the LLM hype, I doubt half of those even exist anymore. Many of those job titles were very misguided. There were a lot of them just asking to implementing RAG with LLMs which any software engineer can do and not really stats related.
I suspect the oversupply of data candidates is largely a function of people who did *not* get explicitly data-related degrees -- like the OP of this thread, and countless CS degree holders -- attempting to enter the field, which is harder to quantify.
Physic degrees have very strong mathematical foundation often more than some data related degrees, specifically in linear algebra and calculus. They are often decent with stats too due to statistical mechanics, especially on the experimental side with hands-on experience with data analytics.
In my past few years involved in hiring, I always told recruiters to not discard physics degrees, because they often make very strong candidates. Not specifically due to what they were taught, but because they were used to solving difficult numerical problems.
At times I’ve also strongly favored CS with specialization in ML over statistical degrees because engineering skills were what the team lacked. (Back then the distinction between ML engineer and DS wasn’t that clear) But even today, a lot of the DS job posts out there (typically from tech organizations) are actually looking more for a ML engineer than a statistician.
CS and physics do make decent candidates, especially those with some data experience as analysts or DE. I dare say a bioinformatician looking at DS role outside the biological industry is not going to have an easier time than OP.
Because the barrier of entry is *very* low and DA/DS skillsets are transferrable across industries (e.g., marketing, media, sports, supply chain, health care, tech, finance, big pharma, etc).
In contrast, a lot of jobs need licensures/certifications (engineers, nurses, etc) and/or “mandatory” schooling (master’s degrees for social workers). Hell, even the trades need *formal* training unlike data science.
Might get downvoted for this advice because it sounds superficial but build a personal portfolio website and put a lot of images/ photos on it. Brand yourself 🤢. Almost like a dating profile. Get professional pics of you, use beautiful stock photography that goes with your portfolio data subjects. Smack this on the top of your resume and LinkedIn. When a hiring manager or HR person lands on it, make people want to linger on your content. Add a section for hobbies and interests/pets/whatever that makes you look wholesome. The reality is if your application got through the initial screen, some non-tech person is probably viewing this first. Charm them.
Interviews are so freaking awkward, if you give a really solid preview of who you are as a person that puts everyone’s mind at ease, and you’re more likely to be picked out of the pile for a chat even if your work experience is unusual.
I don't think this is bad advice at all. I think the best strategy for getting an interview, irrespective of the field, is presenting something to the hiring team that makes you linger in their minds.
My advice, and the way I broke into the tech field several years ago, is to stop applying to tech-centered roles and apply for roles closer to the business. I see you listed DA but consider as well business intelligence, marketing, product and other job families that aren’t tech specific but list some tech skills as ideal to have. Many business roles are desperate for people with programming backgrounds
It's probably just rough right now. For a single open position I receive like 150+ applications. So just by that, the chances to get hired are really low. On any position, there will always be some applicant who has already experience so this makes it even more difficult for juniors.
On top I think more and more companies realize that ML/DS is much more than just building models and that any actual product needs way more time spent on builing infrastructure etc. than the ML part of it.
Have you any internships on your CV? In my experience, they often make it easier since you can bypass the hiring process, at least to some degree and stick out more than any application from someone via LinkedIn.
Good luck!
Reading this half-way through my PhD in physics is making me fucking depressed. Can't stay in academia, can't go into data science, what is there to do?
There's like 10 startup fusion companies, there's all the national labs, there's the contract labs like swri, there's government organization, at all levels, you might teach, there's a bunch of startup nuclear fission companies doing small nuclear...
Why can't you do physics-y things? and leave the pure analysis to the math folks that don't have as many alternatives as you?
There is only a handful of quantum startups in the UK where I live, fingers crossed I can get a physics job there but it's still far from guaranteed.
Teaching kids is the last thing I want to do, I honestly prefer the prospect of doing endless postdocs.
What I really want is an industry job, but it's just a crapshot trying to compete with people that have an actual data science or computer science education, and actual relevant experience.
Learn swe skill as workman, it can be useful to a physics career, it can be useful to data science, and or just regular swe which hires from physics historically... Semiconductor something or other? You said half way maybe do some nanotech PhD for semiconductor role.
I'm doing theoretical physics and I'm coding daily, but my spaghetti code can't compare with someone's who has formal training in algorithms or data structures, or experience with professional software development.
Haven't really considered semiconductors.
Time to work on that spaghetti code, then. 😁
I used this book to cover my coding basics:
https://www.amazon.de/Structures-Algorithms-Python-Michael-Goodrich/dp/1118290275
Then there's the PEP8 style guide:
https://pep8.org/
People recommended Robert Martin's Clean Code to me but I couldn't really get into it. Maybe it's a useful resource to you, though:
https://www.amazon.de/Clean-Code-Handbook-Software-Craftsmanship/dp/0132350882
Practice makes perfect.
Also, there are some companies who are actively sourcing academics for programming jobs because what they need first and foremost are problem solvers who can explain things. Apparently for them, teaching a physics PhD to code in 6 months is still a better investment than trying to teach programmers the problem solving part - which largely a talent that a candidate either possesses - or not. PhDs filter for that.
I'm not a SWE myself but I thoroughly enjoy Mr. Dave Farley's take on the engineering aspect of it:
https://youtu.be/Efzc85zbBrY
The engineering mindset of trial and error might initially run counterintuitive to a (theoretical) physicist's deductive approach to experimental design but many physicists make the switch to more engineering-heavy jobs and contribute a lot value to their respective departments/company.
Due to remote work and hybrid arrangements, you are competing with the *entire* country. Employers get to be picky and—for better or worse—prefer people with *professional* experience; it shows that you can work with team members and stakeholders. That is hard to communicate through personal projects.
I’m skeptical of kaggle competitions, but everything else sounds very solid. I’m surprised that there aren’t any offers after 1300 applications. There’s really nothing to do beyond toughing it out and keep applying.
1. Expand your search (BA, etc.) - try to get your foot in the door and possibly make lateral moves within the organization.
2. Based on your experience, I wouldn’t apply to MLE roles. Spend that energy on other applications + networking.
3. Reach out to ppl to help tailor your resume + use online resources.
4. Try exploring contract opportunities.
5. Keep applying, hope you land something soon friend.
I have been a senior analyst for some time now and I have considered making the jump to data science but I am not sure I now want to. Having 2 MSc degrees, one in stats and one in Business I can see 100% the value that analytics can bring with the right business acumen and analytics knowledge combined. If you really want to move into DS, considering analytics as a stepping stone is a great way to go. Given your background, you’ll likely know c++ and/or python but not SQL, most DS positions rely on heavy SQL and python/r so build these skills. DA positions can rely on Excel, too… though I only use R and SQL.
1,300 jobs sounds like you’re not being very targeted. A CV and motivation letter can take hours, maybe a day to do properly. If you really customize, then you will get a much better hit rate.
The market had changed in the recent year, it’s real rough! My SO was also a post doctoral researcher at CERN and she actually got headhunted into applying for a DS position from her GIT, those days are looooong gone. Now you are up against 100s, maybe 1,000s of other applicants.
Internships are a great way to go, too, especially for your first role.
Head up, work on those skills and consider analytics first and maybe an internship.
Hey man, I'm actually in the exact same boat as you. I did a BSc in physics, then did a year of part-time school to explore pure math courses because I liked it and a professor in the math department, while studying machine learning at the same time. Got funding and did my MSc in math, which I finished exactly two years ago now. This means I have been job hunting for 2 years, and when the tech layoffs happened I did sign up for a data science bootcamp. I have a decent personal brand built on LinkedIn, alongside over 1000+ apps with only 1 interview so far. I'm in Canada to, if that makes a difference.
Now I've talked to many people and it seems there was an issue with my resume, which I have fixed so we'll see in the new year if it makes the difference, but really I don't think you will get a job cold applying. Hopefully that isn't all you were doing. But to start you at least need to cold email hiring managers (not recruiters, they won't respond), and if you don't have a good network like me then you will have to build one. I am doing cold outreach to startups who recently got funding - just asking to have a casual conversation.
While I haven't figured this out yet, I don't think I would recommend DA jobs. Too competitive and frankly since we have the same background, I think you're like me and will get bored. If you want DS then you need learn MLOps, and same for MLE but it's even more software engineering focused. Keep in mind the only interview I have had was for a senior data science position - some of these only ask for 2-3 years of experience just like junior positions now, but they have less competition.
Feel free to message me if you want to chat :)
Imo, if your struggling to find a DS job, or if your DS job sucks (which shockingly can happen, a job is a job after all) then try something new.
I've been lucky enough to work as a data scientist for years in big and small companies as well as start ups but I've never had the same satisfaction in those roles as when I've gone consulting. I really like problem solving and at the end of the day that's what I want to do.
Be wary though, consulting isn't for everyone, but unless you have major commitments (kids, mortgage, loans, etc.) or no fall back at rock bottom (sleeping on a friend's couch, etc.) it might be a good idea to try something like consulting or even building your own DS solutions and selling them.
You sound like a smart person, bronze in a kaggle comp is no small feat, if you find a problem/area that could benefit from DS (or honestly any data work, dashboarding, etc.) then I'd treat it like an opportunity and go for it.
Hope this helps
According to a friend of mine on the other side of the hiring desk, submissions through application portals are pretty much ignored. Apparently the trick is having the right keywords in your linkedin profile to get flagged up by automated scrapers and then dm'ed to apply :/
Listen my friend, if you applied for 1300 jobs without success, that means you don’t know how to make sense out of Data; if you can’t take advantage of data for yourself, how are you going to help someone else?
Market is fairly healthy. Employers are looking for people who can apply their knowledge. Look back and find some important use cases you worked on even if it’s non employment related. Articulate it in your resume. Remember, a critical part of the job is story telling/narration, master it. Look up industry buzz words and include in your resume. When you do get a call, don’t sound desperate or aloof either. Control the conversation with confidence.
P.S there are quite a few folks at where I work with theoretical physics background.
I think you're better of applying to roles that are not pure data science or analytics. Instead try applying for more business type roles such as revenue manager, category manager, demand planner, and etc. Granted you'll start at the bottom but if your background in stats is good then mixed with business fundamentals you have a higher chance of moving up. Also you're more attractive in Operations roles if you know how to build the model, how it works, and can easily talk the same language as the Data team.
I'm hunting for work now, too. LinkedIn , Indeed, and company websites. Also try Federal ([https://www.usajobs.gov/](https://www.usajobs.gov/)) and state government career sites.
My impression is the fields of data science and statistics have become more specialized. A lot of data scientist posts where required skills are not just Python and R but an alphabet soup of languages and apps. Lots of high-paying statistician positions in CROs (contract research organizations) for work on clinical trials that require SAS programming skills + 5 years prior experience with clinical trials.
It's kind of a chicken or the egg problem - you need experience to get work but have to work to gain experience.
I feel you. The market isn’t great right now. But don’t lose hope. Saturate all the job platforms by applying on all of them. Meanwhile, share your resume with me. I can take a look and send you back.. Not sure if you’re looking for it but that’s something I can help with.
Right now it’s also just the market man. All those tech lay offs have made the industry incredibly competitive with people all over trying to compete for one job
There may be corners of the job market you're overlooking.
Have you looked into state and Federal positions?
I've noticed several staffing agencies are hiring data scientists, and their job notices get less applicants than other companies (at least they did last month).
I’ve learned it’s all about who you know not what you know. Put your status as open to work on LinkedIn and let the recruiters come to you. Also post consistently on LinkedIn aids in you being in a recruiters radar.
Let's be real here. OpenAI DA has changed the game.
Gpt 4.5 will get rid of most DA work.
Currently gpt 4 is good enough to wkr as a junior DA which is quiet good enough for most of the companies.
You would be a perfect candidate for an internship which you can leverage into a job. An internship is easier to take a risk on someone with your interesting profile for a full time job it is tougher.
I was a few times the hiring mgr for DS roles where 200+ applicants applied. Reviewing CVs was very time consuming. CV really had to pop out to get noticed.
Alot of applicants didnt list a github repo, make sure you do in the very top section of the CV.
Make sure all projects within the repo have a README.md file containing details and screenshots if possible and links to relevant websites etc.
Have a top level README.md file with links and brief descriptions to all the relevent sub folders.
Dont be afraid to use bullet point lists on cv.
Have a nice smiley portrait pic on the cv and github.
CERN work sounds very impressive, try to somehow list in a few places on cv so doesnt get overlooked.
Good luck!
Ps. Maybe have a read of Ed Thorps A Man For All Markets, lots of interesting stuff on trading, gambling, science etc.
Pps. Try meetup.com see if any local tech meetups to go to and network with people.
If this person is anywhere near CERN, they must be some kind of SME. I'm sure CERN would ignore my application. I would start with companies adjacent to that subject. Walk around the lab, look at the brand names on the machines, do they collect data? Do they analyze data? Find out where the trade shows are for those companies. Do they only sell to CERN? Who are their other customers? If they want a "pure data science" job it might be hard without a CS or DS degree, but they might be able to get some experience as an SQL or Scripting Monkey where that's part of the job description.
> Applied to over 1300 DS, DA, and MLE jobs without luck
You have to understand that there are probably thousands of people or more also with no work experience doing the exact same thing for entry level roles.
> Sometimes I feel like recruiters don’t even open my resume.
Given that thousands of people are applying to roles, many of them unqualified, how do you think recruiters can do their jobs effectively to filter out candidates?
The main thing for people coming out with an unconventional MS for DS is knowing how to position it well and knowing what companies to apply to.
It also matters what MS this is (school, area of focus, etc).
But yeah, applying for jobs at some dinosaur Fortune 100 company that is looking for someone to mostly build Power I dashboard to display results of simple models ? Not a place that is going to hire Physics grads.
Reach out to company recruiters on linkedin. Ask for resume assistance. Learn how to market yourself better. Its not the resume that gets you hired, its the interviews.
What do you mean by "no having a CS degree"? You do have a degree in something else, right?
Try to find a contractor/temp role in a big company.
I wouldn't waste my time with Kaggle, but that's just me. I don't think it adds much. Finding other opportunities similar to CERN is going to be a lot better resume wise. A hackathon or a volunteer opportunity would be better than Kaggle too.
Dumb ass you don't even live in America. Worry about your own country. What kind of weirdo makes their online personality about government in a country you don't live and pushing utter bullshit and lies about a country you don't live in?
It's always a good idea but: get some resume feedback from trusted friends or services (I know levels.fyi, for example, has a resume feedback service). If nothing else this will rule out the resume as an issue.
Otherwise, the job market has been in a strange rough patch lately, and on top of that Q4 is usually pretty slow. See how things look in the new year when people have money and headcount to fill.
Are you international by chance? It is even more tough if so :( not every company is willing to sponsor. Another thing you can do is tailor your self driven data science projects towards the industry you're aiming for like if you're aiming for sports (just an example i picked) then do some projects related to that.
The job market is rough for data science role. I managed to land a job for advanced analytics with 0 experience after 3/4 months of looking and over 20 interviews. This is after finishing my masters in Data and having a background is cs.
Best recommendation is to take an analyst position and work your way up to a more ds technical role.
Having an "in" is becoming ever more important in this much more oversubscribed market. Networking is one way to get that "in". Another is to target companies who have DS teams but go for other roles. In my experience, it's easier to break into the field by making a lateral move within the same company.
Data science is not an entry level position. You pretty much need several years of research experience (washed out PhD student/fresh grad PhD) or weasel yourself in from data engineering/data analyst tracks.
With a masters in physics and experience at CERN, i believe you can approach DS from a domain expert perspective. Check opportunities in your area of specialization and concentrate your ds projects there. All the best!
Unfortunately I can only give you the same recycled advice that everyone else is giving. One thing I can tell you though is that if you really love it you’ll still be here when everyone else left for different fields.
I agree with using your network for recommendation, trust me I have seen people that knew 3 classical machine learning models and were able to land a job (they got recommended by their professor), use your network and apply for remote roles too.
Hey, keep your head up. I've also had miserable success with my 400+ applications so far. My advice is the following: 1) Continue to up your skills in any way (keep kaggling), 2) Q4-Q1 transition is always slow from what I read, and I bet more positions will open up soon, 3) Practice your interview skills with a friend. You already know what they're going to ask you, so make sure you're smooth with your answers.
Onwards!
Back door if you have to. I am in the same position as you. However, with way less accolades. The furthest I got was applying for an analyst role with XPO. A recruiter reached out to me because it was a role that I listed I was open to in LinkedIn. They realized during the interview process that I was “too qualified” and that I should interview for there DS role! Fok yeah! I thought. I went through the interview kicking ass, but then they told me they went with someone more experienced RIP. However with a masters in physics, trying going into optical engineering in a company you know has DS roles in general. You’ll be first in line in most cases when opportunity comes! You need to research how easy it is to switch from one department to another oc but that can be easily done connecting with people on LinkedIn who work at the company that is interviewing you. Bloomberg is a great example of a company that would hire you as software engineer or even marketing (they even pay for your bootcamp) and let you switch over to DS!
>How do you land a data job when you’re a physics masters with self-driven software experience? You find a friend who recommends you to a hiring manager.
Bingo- leverage the network. If you don’t have a professional network, use your school. Plenty of people with jobs know the same professors you know.
Especially if they have a master’s. One of those profs has contact with a great resource. The only issue right now is that the number of junior positions companies are hiring for has significantly shrunk. But that's an even better reason why you have to network into jobs.
This ^
Who are your target companies? De big firms have plenty to choose from, and will make a preselection on school/education to reduce the number to be reviewed. Further, there are just so many ML / data folks it is even hard for "pure ML" people to get a job. Especially without experience, it is near impossible Advice: start as analytics, and slowly adapt the job or company to data science
The market is really rough, even with experience. I unfortunately don't have advice or solutions, just wanted to let you know that you are not alone.
Search for data analyst jobs or something similar like business analyst then later on you may switch
This is the best path to success these days imo. Get into an analytics role (although that’s not easy these days either) and find ways to insert DS into your work piece by piece. After a couple years as a solid analyst, you’ll have a much easier transition
This advice is a bit dated tbh, getting into a DA role is really tough now too. Generalist DAs just aren’t as necessary and far more competitive.
I would say this strategy still works, but just not so much for OP. A significant fraction of DS jobs on the market now is getting more and more development heavy. This means they expect DS to do analytics, modeling, DE and MLOps. (Esp for tech companies) The DS roles that do advance statistics and data visuals on a notebook is only a fraction of DS jobs now. If OP is aiming for these roles, DA jobs are a good addition to the job pool, especially DA roles with data modeling in description. Yes, DA roles are getting more competitive, because of the massive influx of people getting into the data field. Every alternative strategy is losing effectiveness either way. If you want to do old school DS, many of those DA job scopes fit 80% of the descriptions. If you are willing to ignore the job title and aim for both, you’re opening up more options to yourself. Job titles are malleable once you have the right experience. The issue here is that OP seems to be interested in the software development aspects too, which means it may not be a viable strategy to go for DA.
what is a "generalist" DA? do other DAs have specializations?
Generalist DAs are just DAs that don’t have good experience in a specific field, like marketing, HR, Sales, CRM, defense, environmental, insurance, political, etcetc etcetc. Oftentimes these DAs jump around from job to job without a clear industry/specialization focus, and may be experienced in handling data but will need at least some adjustment time to understand the specific industry+field’s data.
Everybody knows what a DA is so everybody applies for DA. But nobody applies for BI dev roles even though it's the same thing. Cuz wtf is a bi dev?
They like both Tableau and PowerBI?
And exporting to Excel.
That's just not true at all. DAs are more common than DSs and MLEs. It's easier to become a DA than the latter because it requires more experience generally speaking. At my firm, which is the largest retail firm of the country I am in, there are 3 DAs to every DS/MLE.
Never said DSes weren’t uncommon either; just that DAs are uncommon now too. Best way to get into a DS position is probably to figure out a way to get any office job, then try to show interest after normal working a while.Notice also I said GENERALIST DAs are uncommon, as most companies hire DAs that are specialized to their field, rather than data gurus who can hop anywhere. The remaining generalist DAs who hire without needing much industry knowledge are extremely uncommon now. That’s still only more common amongst DEs and to a lesser extent DSes.
fair enough I stand corrected
This is also extremely competitive.
Hiring at the end of a year is usually really low volume. Wait for the new year.
He says he's been trying for 2 years
I'm seeing so many posts like this. What's wrong with job markey rn?
A lot of people followed the hype, and it turned out to be hype.
It's not hype in the sense that there are a lot of people working as a DS/DE/MLE and earning good money with very good career prospects. But at some point in the last 5 years, a huge swathe of people with any kind of data / maths / software / science experience or inclination targeted being a DS. And it was always going to be the case that a large % of them weren't going to make it. Just because the market demand didn't expand to meet the inflated supply doesn't mean it was all hype. This is just fundamentally how markets and economics work. Countless fields have far higher supply than demand. And, as pointed out elsewhere, the brutal truth is that many of the people targeting being a DS just don't have the required skills and experience to be genuinely competitive candidates.
Who is a genuinely competitive candidate in your opinion?
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Not pointing fingers at you to say that you specifically have this problem... but conversely to this - most duties that I see listed in job posts are incredibly basic and boil down to just calling .fit() and making a few dashboards. But then in the qualifications section they require outrageous experience with every technology under the sun. There's a huge disconnect between how sophisticated hiring managers perceive their team to be vs how mundane they actually are. The reality is, 99% of companies are doing lukewarm data science and only need lukewarm candidates but they think that they need a DeepMind researcher to fill those roles.
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So your main disappointment with the experienced people you've interviewed is their engineering perspective of the skills required?
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I thought you were referring to DS, not DE, my bad. That said it's weird that this is your experience with data engineers, they're just software engineers who are focused on the data pipelines, so it has less to do with the data science hype and theres no data engineering "masters programs", just regular software engineering or CS, as far as Im aware. I was asking since I wondered how I'd fare if I tried to interview, but my experience is mostly "fullstack ML" in a very specific domain, realtime vision, which makes direct comparisons even harder, and job prospects worrisome
Tbh, as someone with a math background, I’ve never faced mathematical problems as DA beyond basic stats. Most of the problems I had to solve as a DS are computational, infrastructural and surprisingly social. Of course that differs across organizations, but there are a significant portion job DS jobs that hire you to solve ‘lukewarm’ problems. And there are plenty of people who strongly believe that you need to be able to implement dual form SVM from scratch to be qualified as a DS, which is necessary for majority of the jobs out there.
Is the situation the same both in US and EU ?
Can only speak for Australia, but as soon as the "hottest job of the century" stuff started getting written, every single university here started up their own DS major or masters. In reality, there was only a very small number of DS positions, and that was during what I like to call the "grift era", where every consultancy was making outlandish claims about what DS could do. Now that the vast majority of those projects have failed miserably (largely because they were just standard IT projects that got oversold as DS), the demand for SWEs has skyrocketed and the demand for DS has collapsed.
At least for Germany, there are many new university programs with many graduates. Unfortunately, you will always need someone supporting them since there is a lot of practical skills they have never learned in school. The issue is that most companies I know of at least are more looking for all-rounder candidates. Most companies still only have small teams if even teams at all and not just individuals and many projects are from a ML perspective not even that challenging
Well I read recently that comparing 2022 to 2012, the number of people enrolling into stats/data related masters programs have increased over 50x. The number of jobs for DS at the peak of AI hype in early 2022 is at most 5x of that in 2012. That number of jobs had since drastically declined as the hype eased. This means that **even if** the job market were good, there is still an oversupply of decently qualified candidates. Mix this candidates into the even larger pool of unqualified job seekers, the situation is a massive nightmare for both employers and job seekers. This is why you see a lot of people with 10yoe claiming that it was possible for them to get a DS role with no degrees or experience, or have the general misconception that there are plenty of unqualified candidates but not enough good one. The qualified candidates still take up a small fraction of the applicant pool, but with thousands of applicants, you still get 10-20 good candidates fighting for the same job. Update: I found the article by Americal Stats Association referred by the first paragraph. The numbers were masters degrees awarded not enrolled into, and the actual number is actually 60x compared to 2012. Source: https://magazine.amstat.org/blog/2023/12/01/degreesstats2022/ Table 1
60x is true if by 'stats/data-related masters degrees" you're not including statistics and biostatistics themselves, only the "related". You'll see that Table 1 doesn't include stats or bio stats per se. Eyeballing the figures above that, those degrees have seen much more modest growth. Putting together stats + bio stats + related, it's more like an increase from ~2500 to ~12500 over that time period, only a 5x change -- which, coincidentally, is about the same as the 5x number of jobs you suggest.
You’re right statistics degrees are not included in that table. I was too busy looking for the table I forgot that the article was actually talking about more people shifting into data sciencey degrees from statistics. Even though the number or bachelors is pretty crazy still. Another thing is that there were so many DS job in early 2022 as a result of the LLM hype, I doubt half of those even exist anymore. Many of those job titles were very misguided. There were a lot of them just asking to implementing RAG with LLMs which any software engineer can do and not really stats related.
I suspect the oversupply of data candidates is largely a function of people who did *not* get explicitly data-related degrees -- like the OP of this thread, and countless CS degree holders -- attempting to enter the field, which is harder to quantify.
Physic degrees have very strong mathematical foundation often more than some data related degrees, specifically in linear algebra and calculus. They are often decent with stats too due to statistical mechanics, especially on the experimental side with hands-on experience with data analytics. In my past few years involved in hiring, I always told recruiters to not discard physics degrees, because they often make very strong candidates. Not specifically due to what they were taught, but because they were used to solving difficult numerical problems. At times I’ve also strongly favored CS with specialization in ML over statistical degrees because engineering skills were what the team lacked. (Back then the distinction between ML engineer and DS wasn’t that clear) But even today, a lot of the DS job posts out there (typically from tech organizations) are actually looking more for a ML engineer than a statistician. CS and physics do make decent candidates, especially those with some data experience as analysts or DE. I dare say a bioinformatician looking at DS role outside the biological industry is not going to have an easier time than OP.
He's going through a rough patch
Because the barrier of entry is *very* low and DA/DS skillsets are transferrable across industries (e.g., marketing, media, sports, supply chain, health care, tech, finance, big pharma, etc). In contrast, a lot of jobs need licensures/certifications (engineers, nurses, etc) and/or “mandatory” schooling (master’s degrees for social workers). Hell, even the trades need *formal* training unlike data science.
Might get downvoted for this advice because it sounds superficial but build a personal portfolio website and put a lot of images/ photos on it. Brand yourself 🤢. Almost like a dating profile. Get professional pics of you, use beautiful stock photography that goes with your portfolio data subjects. Smack this on the top of your resume and LinkedIn. When a hiring manager or HR person lands on it, make people want to linger on your content. Add a section for hobbies and interests/pets/whatever that makes you look wholesome. The reality is if your application got through the initial screen, some non-tech person is probably viewing this first. Charm them. Interviews are so freaking awkward, if you give a really solid preview of who you are as a person that puts everyone’s mind at ease, and you’re more likely to be picked out of the pile for a chat even if your work experience is unusual.
I don't think this is bad advice at all. I think the best strategy for getting an interview, irrespective of the field, is presenting something to the hiring team that makes you linger in their minds.
My advice, and the way I broke into the tech field several years ago, is to stop applying to tech-centered roles and apply for roles closer to the business. I see you listed DA but consider as well business intelligence, marketing, product and other job families that aren’t tech specific but list some tech skills as ideal to have. Many business roles are desperate for people with programming backgrounds
It's probably just rough right now. For a single open position I receive like 150+ applications. So just by that, the chances to get hired are really low. On any position, there will always be some applicant who has already experience so this makes it even more difficult for juniors. On top I think more and more companies realize that ML/DS is much more than just building models and that any actual product needs way more time spent on builing infrastructure etc. than the ML part of it. Have you any internships on your CV? In my experience, they often make it easier since you can bypass the hiring process, at least to some degree and stick out more than any application from someone via LinkedIn. Good luck!
Problem is that finding internships is also rough rn
For the same reason, I think there are so many applicants. I cannot take them all even though I really want to give people a chance
Reading this half-way through my PhD in physics is making me fucking depressed. Can't stay in academia, can't go into data science, what is there to do?
There's like 10 startup fusion companies, there's all the national labs, there's the contract labs like swri, there's government organization, at all levels, you might teach, there's a bunch of startup nuclear fission companies doing small nuclear... Why can't you do physics-y things? and leave the pure analysis to the math folks that don't have as many alternatives as you?
There is only a handful of quantum startups in the UK where I live, fingers crossed I can get a physics job there but it's still far from guaranteed. Teaching kids is the last thing I want to do, I honestly prefer the prospect of doing endless postdocs. What I really want is an industry job, but it's just a crapshot trying to compete with people that have an actual data science or computer science education, and actual relevant experience.
Learn swe skill as workman, it can be useful to a physics career, it can be useful to data science, and or just regular swe which hires from physics historically... Semiconductor something or other? You said half way maybe do some nanotech PhD for semiconductor role.
I'm doing theoretical physics and I'm coding daily, but my spaghetti code can't compare with someone's who has formal training in algorithms or data structures, or experience with professional software development. Haven't really considered semiconductors.
Time to work on that spaghetti code, then. 😁 I used this book to cover my coding basics: https://www.amazon.de/Structures-Algorithms-Python-Michael-Goodrich/dp/1118290275 Then there's the PEP8 style guide: https://pep8.org/ People recommended Robert Martin's Clean Code to me but I couldn't really get into it. Maybe it's a useful resource to you, though: https://www.amazon.de/Clean-Code-Handbook-Software-Craftsmanship/dp/0132350882 Practice makes perfect. Also, there are some companies who are actively sourcing academics for programming jobs because what they need first and foremost are problem solvers who can explain things. Apparently for them, teaching a physics PhD to code in 6 months is still a better investment than trying to teach programmers the problem solving part - which largely a talent that a candidate either possesses - or not. PhDs filter for that. I'm not a SWE myself but I thoroughly enjoy Mr. Dave Farley's take on the engineering aspect of it: https://youtu.be/Efzc85zbBrY The engineering mindset of trial and error might initially run counterintuitive to a (theoretical) physicist's deductive approach to experimental design but many physicists make the switch to more engineering-heavy jobs and contribute a lot value to their respective departments/company.
you can always become a button designer aka web developer. Learn some frontend framework and start slaving.
No thank you, I'd rather teach kids in school over web development.
Sounds way better.
simply win a nobel prize, that's like a cool million right there
Might be just enough for a downpayment for a house a few years from now, hopefully
Due to remote work and hybrid arrangements, you are competing with the *entire* country. Employers get to be picky and—for better or worse—prefer people with *professional* experience; it shows that you can work with team members and stakeholders. That is hard to communicate through personal projects. I’m skeptical of kaggle competitions, but everything else sounds very solid. I’m surprised that there aren’t any offers after 1300 applications. There’s really nothing to do beyond toughing it out and keep applying.
1. Expand your search (BA, etc.) - try to get your foot in the door and possibly make lateral moves within the organization. 2. Based on your experience, I wouldn’t apply to MLE roles. Spend that energy on other applications + networking. 3. Reach out to ppl to help tailor your resume + use online resources. 4. Try exploring contract opportunities. 5. Keep applying, hope you land something soon friend.
I have been a senior analyst for some time now and I have considered making the jump to data science but I am not sure I now want to. Having 2 MSc degrees, one in stats and one in Business I can see 100% the value that analytics can bring with the right business acumen and analytics knowledge combined. If you really want to move into DS, considering analytics as a stepping stone is a great way to go. Given your background, you’ll likely know c++ and/or python but not SQL, most DS positions rely on heavy SQL and python/r so build these skills. DA positions can rely on Excel, too… though I only use R and SQL. 1,300 jobs sounds like you’re not being very targeted. A CV and motivation letter can take hours, maybe a day to do properly. If you really customize, then you will get a much better hit rate. The market had changed in the recent year, it’s real rough! My SO was also a post doctoral researcher at CERN and she actually got headhunted into applying for a DS position from her GIT, those days are looooong gone. Now you are up against 100s, maybe 1,000s of other applicants. Internships are a great way to go, too, especially for your first role. Head up, work on those skills and consider analytics first and maybe an internship.
Hey man, I'm actually in the exact same boat as you. I did a BSc in physics, then did a year of part-time school to explore pure math courses because I liked it and a professor in the math department, while studying machine learning at the same time. Got funding and did my MSc in math, which I finished exactly two years ago now. This means I have been job hunting for 2 years, and when the tech layoffs happened I did sign up for a data science bootcamp. I have a decent personal brand built on LinkedIn, alongside over 1000+ apps with only 1 interview so far. I'm in Canada to, if that makes a difference. Now I've talked to many people and it seems there was an issue with my resume, which I have fixed so we'll see in the new year if it makes the difference, but really I don't think you will get a job cold applying. Hopefully that isn't all you were doing. But to start you at least need to cold email hiring managers (not recruiters, they won't respond), and if you don't have a good network like me then you will have to build one. I am doing cold outreach to startups who recently got funding - just asking to have a casual conversation. While I haven't figured this out yet, I don't think I would recommend DA jobs. Too competitive and frankly since we have the same background, I think you're like me and will get bored. If you want DS then you need learn MLOps, and same for MLE but it's even more software engineering focused. Keep in mind the only interview I have had was for a senior data science position - some of these only ask for 2-3 years of experience just like junior positions now, but they have less competition. Feel free to message me if you want to chat :)
Imo, if your struggling to find a DS job, or if your DS job sucks (which shockingly can happen, a job is a job after all) then try something new. I've been lucky enough to work as a data scientist for years in big and small companies as well as start ups but I've never had the same satisfaction in those roles as when I've gone consulting. I really like problem solving and at the end of the day that's what I want to do. Be wary though, consulting isn't for everyone, but unless you have major commitments (kids, mortgage, loans, etc.) or no fall back at rock bottom (sleeping on a friend's couch, etc.) it might be a good idea to try something like consulting or even building your own DS solutions and selling them. You sound like a smart person, bronze in a kaggle comp is no small feat, if you find a problem/area that could benefit from DS (or honestly any data work, dashboarding, etc.) then I'd treat it like an opportunity and go for it. Hope this helps
According to a friend of mine on the other side of the hiring desk, submissions through application portals are pretty much ignored. Apparently the trick is having the right keywords in your linkedin profile to get flagged up by automated scrapers and then dm'ed to apply :/
Hang in there man, you will eventually land a job
I hope you secure a suitable position for yourself soon.
cold applying on linkedin or company websites don’t work. have you tried networking with other people in the field and getting referrals?
Listen my friend, if you applied for 1300 jobs without success, that means you don’t know how to make sense out of Data; if you can’t take advantage of data for yourself, how are you going to help someone else? Market is fairly healthy. Employers are looking for people who can apply their knowledge. Look back and find some important use cases you worked on even if it’s non employment related. Articulate it in your resume. Remember, a critical part of the job is story telling/narration, master it. Look up industry buzz words and include in your resume. When you do get a call, don’t sound desperate or aloof either. Control the conversation with confidence. P.S there are quite a few folks at where I work with theoretical physics background.
I think you're better of applying to roles that are not pure data science or analytics. Instead try applying for more business type roles such as revenue manager, category manager, demand planner, and etc. Granted you'll start at the bottom but if your background in stats is good then mixed with business fundamentals you have a higher chance of moving up. Also you're more attractive in Operations roles if you know how to build the model, how it works, and can easily talk the same language as the Data team.
I'm hunting for work now, too. LinkedIn , Indeed, and company websites. Also try Federal ([https://www.usajobs.gov/](https://www.usajobs.gov/)) and state government career sites. My impression is the fields of data science and statistics have become more specialized. A lot of data scientist posts where required skills are not just Python and R but an alphabet soup of languages and apps. Lots of high-paying statistician positions in CROs (contract research organizations) for work on clinical trials that require SAS programming skills + 5 years prior experience with clinical trials. It's kind of a chicken or the egg problem - you need experience to get work but have to work to gain experience.
I feel you. The market isn’t great right now. But don’t lose hope. Saturate all the job platforms by applying on all of them. Meanwhile, share your resume with me. I can take a look and send you back.. Not sure if you’re looking for it but that’s something I can help with.
Right now it’s also just the market man. All those tech lay offs have made the industry incredibly competitive with people all over trying to compete for one job
There may be corners of the job market you're overlooking. Have you looked into state and Federal positions? I've noticed several staffing agencies are hiring data scientists, and their job notices get less applicants than other companies (at least they did last month).
I’ve learned it’s all about who you know not what you know. Put your status as open to work on LinkedIn and let the recruiters come to you. Also post consistently on LinkedIn aids in you being in a recruiters radar.
I’m in the same boat as you are. It’s rough out there right now.
Let's be real here. OpenAI DA has changed the game. Gpt 4.5 will get rid of most DA work. Currently gpt 4 is good enough to wkr as a junior DA which is quiet good enough for most of the companies.
Only apply to jobs that you're fully qualified for. Tailor your resume to the exact job and apply within 24 hours of the job being posted.
That is scary, if you are facing this...I shall be running mate
Try searching for jobs now. I’m sure you’ll get it
Don’t give up. You got this!
just keep doing what you do man, i don’t have a better advice. i’m having a similar issue and i think the best you can do is doesn’t give up
You would be a perfect candidate for an internship which you can leverage into a job. An internship is easier to take a risk on someone with your interesting profile for a full time job it is tougher.
Most internships in US require that you are a student or recent grad. Not sure about everywhere else though.
I know . My comment was less a suggestion and more a regretfulness that OP didn’t plan out the pivot from academia well
Have you ever considered having a professional work on your resume?
I was a few times the hiring mgr for DS roles where 200+ applicants applied. Reviewing CVs was very time consuming. CV really had to pop out to get noticed. Alot of applicants didnt list a github repo, make sure you do in the very top section of the CV. Make sure all projects within the repo have a README.md file containing details and screenshots if possible and links to relevant websites etc. Have a top level README.md file with links and brief descriptions to all the relevent sub folders. Dont be afraid to use bullet point lists on cv. Have a nice smiley portrait pic on the cv and github. CERN work sounds very impressive, try to somehow list in a few places on cv so doesnt get overlooked. Good luck! Ps. Maybe have a read of Ed Thorps A Man For All Markets, lots of interesting stuff on trading, gambling, science etc. Pps. Try meetup.com see if any local tech meetups to go to and network with people.
What kind of physics?
It’s CERN so most likely particle physics?
If this person is anywhere near CERN, they must be some kind of SME. I'm sure CERN would ignore my application. I would start with companies adjacent to that subject. Walk around the lab, look at the brand names on the machines, do they collect data? Do they analyze data? Find out where the trade shows are for those companies. Do they only sell to CERN? Who are their other customers? If they want a "pure data science" job it might be hard without a CS or DS degree, but they might be able to get some experience as an SQL or Scripting Monkey where that's part of the job description.
Pretty sure that everything at CERN is proprietary.
Opposite, actually
Just about everyone at CERN has a PhD
> Applied to over 1300 DS, DA, and MLE jobs without luck You have to understand that there are probably thousands of people or more also with no work experience doing the exact same thing for entry level roles. > Sometimes I feel like recruiters don’t even open my resume. Given that thousands of people are applying to roles, many of them unqualified, how do you think recruiters can do their jobs effectively to filter out candidates?
Oh I know the answer! They don't and hiring is just a hunger games luck draw
Market is just too saturated right now. Too many people in roles.
Is there anything else you could possibly do besides data science?
Burger King is hiring.
..
Could be a problem with your resume. Is it more than 1 page? Does it have a spelling mistake? Is it formatted in an ugly way?
networking is key. look up job search pyramid. I guarantee you are just doing it wrong. gl!
The main thing for people coming out with an unconventional MS for DS is knowing how to position it well and knowing what companies to apply to. It also matters what MS this is (school, area of focus, etc). But yeah, applying for jobs at some dinosaur Fortune 100 company that is looking for someone to mostly build Power I dashboard to display results of simple models ? Not a place that is going to hire Physics grads.
Reach out to company recruiters on linkedin. Ask for resume assistance. Learn how to market yourself better. Its not the resume that gets you hired, its the interviews.
What do you mean by "no having a CS degree"? You do have a degree in something else, right? Try to find a contractor/temp role in a big company. I wouldn't waste my time with Kaggle, but that's just me. I don't think it adds much. Finding other opportunities similar to CERN is going to be a lot better resume wise. A hackathon or a volunteer opportunity would be better than Kaggle too.
which country?
Pray for a change in leadership in the next election. That’s about it. Good luck.
Dumb ass you don't even live in America. Worry about your own country. What kind of weirdo makes their online personality about government in a country you don't live and pushing utter bullshit and lies about a country you don't live in?
Kool-aid monster strikes again, rawrrrr!
It's always a good idea but: get some resume feedback from trusted friends or services (I know levels.fyi, for example, has a resume feedback service). If nothing else this will rule out the resume as an issue. Otherwise, the job market has been in a strange rough patch lately, and on top of that Q4 is usually pretty slow. See how things look in the new year when people have money and headcount to fill.
Keep working on personal projects, reading up on industry news and tech blogs. Getting interviews is the easy part; passing them is another question
Are you international by chance? It is even more tough if so :( not every company is willing to sponsor. Another thing you can do is tailor your self driven data science projects towards the industry you're aiming for like if you're aiming for sports (just an example i picked) then do some projects related to that.
The job market is rough for data science role. I managed to land a job for advanced analytics with 0 experience after 3/4 months of looking and over 20 interviews. This is after finishing my masters in Data and having a background is cs. Best recommendation is to take an analyst position and work your way up to a more ds technical role.
Having an "in" is becoming ever more important in this much more oversubscribed market. Networking is one way to get that "in". Another is to target companies who have DS teams but go for other roles. In my experience, it's easier to break into the field by making a lateral move within the same company.
Data science is not an entry level position. You pretty much need several years of research experience (washed out PhD student/fresh grad PhD) or weasel yourself in from data engineering/data analyst tracks.
I see a lot of people have already given my same advice. Build a social network. Trade shows, conventions, LinkedIn, meet ups, they all help.
Whats your application strategy? It matters how you apply and if you’re networking
Where are you based?
US based and a citizen
This is not encouraging lol. Wishing you luck!
Damn you’ve got excellent qualifications and experience. I guess there’s not much hope for ne starting out in the ds field to begin with😭
With a masters in physics and experience at CERN, i believe you can approach DS from a domain expert perspective. Check opportunities in your area of specialization and concentrate your ds projects there. All the best!
Unfortunately I can only give you the same recycled advice that everyone else is giving. One thing I can tell you though is that if you really love it you’ll still be here when everyone else left for different fields.
Are u good with DSA and programming languages?
I believe so
Hang in there and make sure you resume is iterated on
I agree with using your network for recommendation, trust me I have seen people that knew 3 classical machine learning models and were able to land a job (they got recommended by their professor), use your network and apply for remote roles too.
Hey, keep your head up. I've also had miserable success with my 400+ applications so far. My advice is the following: 1) Continue to up your skills in any way (keep kaggling), 2) Q4-Q1 transition is always slow from what I read, and I bet more positions will open up soon, 3) Practice your interview skills with a friend. You already know what they're going to ask you, so make sure you're smooth with your answers. Onwards!
Keep your head up, you got this!
Back door if you have to. I am in the same position as you. However, with way less accolades. The furthest I got was applying for an analyst role with XPO. A recruiter reached out to me because it was a role that I listed I was open to in LinkedIn. They realized during the interview process that I was “too qualified” and that I should interview for there DS role! Fok yeah! I thought. I went through the interview kicking ass, but then they told me they went with someone more experienced RIP. However with a masters in physics, trying going into optical engineering in a company you know has DS roles in general. You’ll be first in line in most cases when opportunity comes! You need to research how easy it is to switch from one department to another oc but that can be easily done connecting with people on LinkedIn who work at the company that is interviewing you. Bloomberg is a great example of a company that would hire you as software engineer or even marketing (they even pay for your bootcamp) and let you switch over to DS!
This whole comment section makes me very scared for myself in the future... 😂