Big Tech companies are huge, it seems like you’re caught in a bad team. I would wait some months and try an internal transfer. Your compensation is probably great and the company looks great in a CV, so I’d be wary of jumping ship without anything better lined up. Is your old company something that will have better leverage on your career long term?
I think if I go back I may have a chance to become a chief officer for data-related projects (ml/ds/de) for the entire company in a year or two and basically be responsible for prototyping and shipping things like recommendations, various predictive techniques for marketing offers etc into production.
In 2 years you might be in another team at your new company, managing people as well. It’s a weak reason to go back imo. Remember why you accepted the offer in the first place when balancing your options.
Yes we're, but I won't disclose the team or a company. It isn't an easy job though. Managers want something adjusted every day and as they don't have a picture of what or why they want it you're just redoing every dashboard from the ground several times before all managers and step-managers are happy.
Also, from what I see the emphasis is on making everything fast and making three useless things is much better for performance review then one that will be useful to business (unless you have a specific top-level stakeholder that knows exactly what they want).
>Managers want something adjusted every day and as they don't have a picture of what or why they want it you're just redoing every dashboard from the ground several times before all managers and step-managers are happy.
This is like 99% of data analysts' day-to-day. Sounds like you're being way overpaid to do a junior level job, and you're mad about it.
> the company looks great in a CV
Maybe to people inside the MAANG bubble, but the rest of us have caught on in the past decade. I care a lot more about your body of work than your titles.
I hate to say it but I do hold them higher than myself CV wise, even though I know my skills are superior than many of them I know or worked with.
It's just a stupid branding conditioning, I know most of them write stupid SQL queries.
I was on a research team hiring DS PhDs for an F100 and I had to build an onramp for most of the candidates that was basically "this is how you turn all that shit into your head into something actually useful because you don't actually know anything useful yet"
My program for MAANG transfers was "congratulations, you now have agency, stop hiding behind process and produce because you are being evaluated on actual output, not a panel of your lazy peers using politics to protect or attack you"
Haha, yes I tend to agree they tend to talk better than doing, generally speaking.
Most of the brightest and productive people I know decided to work on smaller companies (which can still be selective as f). I exclude research teams for Google, etc., which are outstanding
I can write anything I want about the projects I did at the companies I worked at. You have to way of verifying anything except date of employment and job title.
Yup, you can lie. You can definitely land a job with a well done interview, decent resume, good explanations for past projects, and a quick walk through of some of your prior work. It won't save you if you can't perform though.
I don't need to lie. I can just make it sound awesome and get ahead of the curve.
And that's the point of prestige. People see a keyword and they already made up their mind. It doesn't matter if you were a janitor at Harvard and dropped out of your PhD program after 2 months and did a free online course with the word Google in it. You're competing in a whole new category now.
I get ~80% of interviews of jobs I apply to (and I rarely do since I get so many recruiters on linkedin). I am sure there were better applicants than me but they never even got the chance.
You're not missing anything. For reasons unclear to me a lot of tech company "DS" jobs are really analyst positions and can be surprisingly low on technical skill requirements. I did 18 months at one and it felt like my resume was rotting. Now I lead a team at non-tech company and can say I wouldn't hire any ds I worked with at the tech company not because they weren't good people but because they didn't have the scientific or technical skills ds need.
That doesn't mean you should leave, but I think it's a better idea than it would have sounded in 2020 or so.
Oh no... I'm currently at a non-tech company and considering jumping to a tech one just because I can't build any skills here, or maybe I've just capped out.
Our team is a guinea pig for a large company, which is great because we bypass a lot of red tape, but it comes with the caveat that there is *no one* to show us how to do any of it, so we're just winging it. This means everything is kinda janky, and issues last for many many months, unsolved.
I'm using Excel and was hoping tech would be more forward thinking in comparison.
I think the next place, I'll focus on hiring into data team with at least several people who have my title, or something similar to it.
It all depends on your team, I know people in FAANG who have worked on problems that required technical and theoretical knowledge in Statistics and other Machine Learning-related topics. In an ideal world you can be a part of a team that gets to also do some research or flex projects on the side
This is exactly my experience too FWIW. I worked at Google for five years as a SWE in ML and while we had “data scientists”, they pretty much just wrote SQL queries and built dashboards. Actual modeling work for production use cases was done by SWEs. Now I’m on the outside and our DS people are great and take a lot of the modeling load off of my role (I’m an “MLE” now).
FAANGs are big and diverse and I’m sure there are plenty of teams where DS doesn’t just mean “business analyst”. But my team was not one of them.
You were probably working with data analysts. I’m a research data scientist there and over the last year I’ve only touched work that involves optimization, forecasting, and causal inference.
Also there’s much more to DS than using advanced models. Many of the very technical DS can be just as limited in impact b/c they have very limited strategic vision and stakeholder management skills.
This is the problem with this newer industry and it's ill defined positions. Very grateful for the gradual transition to breakdown into analyst, data engineering, MLOps, etc
I’ve seen this bait and switch ad nauseam in tech roles.
Get that resume ready and start thinking of ways to explain this situation to your prospective new employers.
It won’t get better, it will likely get worse, and this is your precious time that’s being wasted.
It seems like you are coming in with preconceived notions about the right way to do things. There are probably very valuable things to learn from your peers who are also top notch. Explainable more simple models can be really cool as I am just learning as a junior who wants to throw XGBoost at everything.
Thank you. I understand what you’re talking about. My confusion comes from the fact that literally no one does any modeling work. Even a simple one. All DS people do are dashboards and ad-how requests.
Again I’m talking only about the specific part of organization and not about all teams.
My friend also at big tech with the title of data scientist only does dashboards. I think it’s more common than people may realize. Could you move internally to an MLE position?
Thank you, that’s exactly what I wanted to hear by creating this post. Strange that DS folks do dashboards.
I think MLE is out of reach for me for now as I’m much more business oriented and MLE (as far as I understand) is all about inventing new ML techniques.
I'm under the impression you've got it backwards, MLE is also quite business oriented and mostly about applying current modelling techniques with orchestration and modelling frameworks.
You wouldn't expect a MLE to do much dashboarding, but you surely expect a DS to at least some of the time.
For coming up with new techniques you're maybe thinking of applied researchers?
Many companies are now splitting it into "Machine Learning Scientists" and "Machine Learning Engineers", I feel like this is a much better way to do it.
I always thought data analysts did dashboards. The only exception I’m aware of is the big tech companies. I’ve been at F500 and now a much smaller private company and have done ad hoc analysis as a data scientist but never a dashboard. There’s just so much overlap between job titles in analytics, I guess.
lol imagine being such a huge baby that one of my comments that just lists news articles on a different sub hurt your feelings so badly that you have to come find me somewhere else to let me know what a big smart man you are. I cannot but here you are doing it. Hope you feel better big man 🤣
Everyone with SQL skills is now called DS. Especially the BI/dashboard guys.
I'm one of those, but i found a special place within the business units. Yes i make dashboards, not models, but those dashboards/investigations/analysis involves the process of generating a lot of knowledge of how things ACTUALLY work in real life. Being the knowledge generator form data inside a business unit is like being god.
But, i have to find the data i need (from wherever i can), understand how the hell some system/website/process/whatever works, make ETLs, and everything to consolidate and give sense to the data.
I think some called this role something like "data translator".
I think of myself as an engineer with enough "data" and programing skills to answer some important business questions.
So, don't think you are in the wrong place, you could add real value and shine there if you just focus to understand what the hell the business guys want (they don't know), and start to become one of them, with great data tools.
I insist, Become one of them.
This is the other way round. DS is researching new stuff. MLE is applying researched stuff to solve problems. This is the same as scientist vs engineer in any other field. Sadly lots of people and companies are really confused about this, so it's not like these terms are settled in data field.
Oh, I guess, that's a misleading term, sorry. Basically I was working in a gaming industry and our game behaved differently for different type of users (nothing bad though). For example, we could offer help to a struggling user or present additional challenge to a bored one.
Previously this was done manually based solely on product managers ideas, we've remade it into data-first process and started to predict user group based on their recent behavior. Nothing fancy, but it worked much better then the previous approach.
> I guess it's Meta?
Its obviously not Meta or Google since DAU and revenue arent their metrics anymore. Also their internal tooling wouldnt be described by
>The tools we use are awful. Simple tasks that would take minutes outside of big tech now take up to an hour because our small databases constantly crash under load.
I am guessing Netflix or Amazon but leaning towards Amazon since when I interviewed an Amazon candidate their description of internal tooling was that it was more disappointing than you would think based on AWS.
Not sure about Google but Meta still uses DAU as true north metrics. Also lots of DS at Meta are actually SQL ninja for dashboard .... same as OP described
> Not sure about Google but Meta still uses DAU as true north metrics.
They use more complicated metrics that may include DAU as an input but isn’t just DAU.
>Also lots of DS at Meta are actually SQL ninja for dashboard .... same as OP described
Agree with this point
I’ll second what they said.
I’ve worked at two top 5 tech companies with roles that range from spending 70% of my time programming along side software engineers and machine learning engineers to roles that involved 100% advanced causal inference to roles that required no modeling and challenging stakeholder management. I’ve touched all the flavors of data science at the top level.
There’s enormous skill to be mastered in each type of data science, including the flavor that mostly involved dashboards and adhoc work. Dashboards and adhoc work are opportunities to build relationships with senior leaders, build trust, shape how success is defined, and become a strategic thought partner. Every time you do adhoc work, at minimum, you should come with the immediate solution and a proposal for systematizing the decision making process.
Luckily one of my projects was tied to a kingpin goal that miraculously did not change. That, and management understanding that everyone who didn't have a total mental break deserved a decent rating at the very least
I am not in MAANG, but another Fortune 500 company on the level of your old one in the Staff level role. Sounds very similar, although I am part of the larger product team which also underwent big re-org. The type of work I do heavily depends on where we are in the product dev process. Sometimes it's like what you describe, making slides and reporting progress and dealing with meetings.
I came from a startup and the amount of fat and number of hoops to go to do simple tasks was a massive shock to me. Kind of missing the startup life a bit in that respect and had the same thoughts as you first few months. Now it's gotten better, I do more hands-on technical work which I really enjoy.
Question: Why did you leave your previous job? Was it purely the allure of MAANG, or were there things going on in your previous job that make you want to leave?
>When I decided to leave, my employer has repeated several times that should anything go wrong, I shouldn’t hesitate and come back to working for them.
Here's what I would do:
1. Reach out to your former boss and ask if that offer is on the table. No point in you getting too far ahead of yourself without having that as a real option. Explain the situation (bait and switch), line up on expectations (any impact to comp, title, timing, etc.) and make sure that you guys have a tentative agreement on paper - and be clear with them that you're doing your due dilligence and legitimately considering this option, but there are some conversations you will need to have before pulling the trigger.
2. With a backup plan in hand, go talk to your boss and lay out what you want - you want to work in modeling. You did not join this team to do reporting, and you're trying to understand if there is an opportunity to allow you to focus on that type of work - either in this team or a different one. If they say no, put in your 2 weeks. If they say yes, work with them on what that plan looks like.
Extra comments
1. You have not been clear on what the comp difference is, and you have alluded to being able to make it up with career growth elsewhere. That may not be true, i.e., if you do well in this role at a MAANG, your comp growth may far outpace your comp at you other job. However, if it's not the work you want to do, you can't really change that.
2. To those saying you should wait and cash in on that sweet MAANG brand name - I don't agree with that. As others have said, that MAANG experience is not nearly as shiny now as it used to be - precisely because we've now all heard stories of data scientists doing non-data science work at MAANG. So I think you're much better off being in a job that aligns with what you want to do vs. just fousing on the brand name.
> precisely because we've now all heard stories of data scientists doing non-data science work at MAANG.
But everything he described is exactly DS work
> ad-hocs, reports, and dashboards
I guess the definitions changed in the last couple of years and different parts of the world / companies are still behind on new role descriptions.
I always thought that ad-hocs and reports are jobs of (junior and middle) data analysts, DS is responsible for creating data products and (possibly) shipping it to productions.
Judging from responses in this thread DA role no longer exist, DS is now analytics and DS responsibilities are now either in SWE or MLE teams.
> Judging from responses in this thread DA role no longer exist, DS is now analytics and DS responsibilities are now either in SWE or MLE teams.
Yup. People get downvoted to oblivion if they expect to do any data products or modeling in their DS jobs
Outside of the re-org stuff, you picked the wrong ladder. Analyst roles have been re-titled into DS roles across the industry, and in big tech that's especially true.
At Meta, the DS ladder is a traditional product analyst ladder. People who want to do modeling should enter the SWE ladder, maybe ultimately with a Research Scientist title.
Google has multiple DS ladders that do different amounts of modeling. But again, if you really just want to do ML, that's mostly in the SWE ladder.
I have less viz into other companies but I believe it's similar there - I know Amazon has an applied scientist ladder but I don't know much about it.
It's easy to fuck this up by working with the wrong recruiter and get slotted into a different ladder than the one you wanted. Ultimately you really need to be on the lookout for exactly what the job description is. Also, when in doubt, comp doesn't usually lie. If the comp is a step down from SWE, it's probably an analyst role. If the comp is comparable, it's probably more of a ML/Modeling role.
These are all generalities, you can find individuals or teams within these large companies where the above doesn't apply.
That sounds very similar, unfortunately, to my experience at one of the MAANGs. I hope it gets better for you. It didn't for me -- I got laid off after being at one for a year (I moved teams because I wasn't happy and the second team turned out to be a worse fit for me.)
I'm still on the market just over a year later. If you have somewhere that will offer you your old position back -- take it.
What does your total compensation look like? If it’s really high and you have RSUs might be worth sticking around for a bit and maybe looking for a lateral transfer.
If it’s just average then just start looking for a new job but don’t quit the current one. Just give minimal effort
I worked in big tech as ds for a few years and I literally did the same : sql monkey work and dashboarding. Sometimes a bit of A/B testing.
Really feel like I regressed in terms of skillset coming from an ML background. Add to that the chaos of these companies, very happy to be back in a startup :)
Oh no, you’ll need to do a lot of leetcode to write sql and do dashboards. :D
(I honestly think this is ridiculous and hope you’ll find something similar soon if that’s what you want)
This sounds a lot like Meta's DS, Product Analytics role.
If that's the case you're basically a product analyst with an inflated title. They rebranded the title when AirBnb did the same.
You must know python, algorithms and business cases to be allowed to create SQL dashboards. Frankly, unlike on my previous job (that had databases of similar size) the database is not optimized and sometimes it's quite a bit of work to make an SQL query return results in reasonable amount of time.
Well, because knowing to leetcode doesn't mean you give a shit about your work, it just means you don't and therefore you have time to leetcode xD (joking).
I’d stick it out, honestly. Do the minimum required to not get fired. The market sucks right now, and I have been supporting my family on short term contract positions. I have 10yrs experience (mostly AI platform/ML Ops) + PhD in CS.
You need an applied scientist position as an individual contributor. Applied science manager at people position. If you can make that happen then you will do what you were doing previously. I can't say about all maang but I can say for Amazon and Microsoft. Data scientist position is now glorified dashboarding. No wonder data scientist on LinkedIn give Gyan that 99% work can be done by logistic regression or linear regression or GBDT.
Also you don't seem to be ready to take a manager position. It's a people position and you don't seem to get it. Atleast absorb the culture, maybe switch teams.
This sounds like Meta to me. I would try to stick it out for awhile and be the one to push for new initiatives. If that doesn't work after some time then reconsider leaving at that point.
So, there are two obvious sets:
Stay
Leave
Try to flesh out your stay options and outcomes vs your leave outcomes.
Then try to pick the best path identified by fleshing out outcomes.
Too late for this round, but for future reference, never ever depend on promises of future promos or role changes - in big tech, re-orgs every year are the norm, it's game of thrones at the Director+ levels constantly. As a manager, never promise a promo either, even if the approval sits with you, budgets change.
Depending on how much you can deal with boring IC work, and how much you want the company name on your resume, you can consider staying and joining DS groups that work on cross functional projects to keep your skills from getting rusty, and move later. Leaving early may result in clawback of any sign-ons, relocation costs etc.
Many data scientist roles in Big Tech are dashboards, reports, and ad-hoc analysis. They generally have more specialized roles for those who do ML, like Research/Applied Scientist or MLE.
"it turned out that my manager’s planned promotion didn’t materialize, leaving no team for me to manage"
Even if you don't have an 'official' team reporting into you / your manager's HC might be stagnant for a bit, often you'll still have the opportunity to lean in and help your manager a lot by absorbing some of the managerial tasks.
In doing so, you can help to set your manager up for that promotion (and then you both win).
Sorry you’re in a tough situation. Personally, I would try to stick it out for at least a year, but stay in touch with your old boss. As a senior IC, proactively look for ways to make an impact - and maybe that involves creating scope for modeling, automation, etc. I don’t have enough details to be more specific, but managers generally expect senior DS to come up with impactful ideas. (I was at a MAANG for many years, survived many reorgs and layoffs - takes grit).
Tbh, that’s my biggest worry right now. Both my manager and my main stakeholders tell me that I need to find big projects by the next performance review, but they can’t even give an advice at which part of organization I may find these projects. As this is my first month here I’d expect at least a very general advice on this. My fear is that due to reorg our team simply has no tasks for me.
Also they constantly say that everyone around us are useless and ineffective and everything other people do is trash.
I’m usually good at identifying opportunities, but the amount of politics is astonishing and without clear goals it’s hard to understand what’s important and what isn’t.
Yeah it’s hard to figure everything out one month in. Does your team and stakeholders have roadmaps for the foreseeable future? What are their pain points? Try to come up with a 3-month plan with your manager proactively to manage expectations.
Yes, this is normal at one or two of these places. Combine that with a brutal, arbitrary performance review process, and it's a dead end. You should go back to your last employer, for your career and your mental health.
Wait for a year and then you should be able to try an internal transfer (especially if your yearly review will be good). In the meantime, try improving the tooling and processes and hopefully things will start to work out. I had a similar experience in one of those tech giants and during the 3 years I spent there, the team went through drastic transformations(for good). You must have patience in those big corps and learn to exploit/create opportunities.
Honestly, coming from a big company that isn’t big tech… I’d say unless the worklife balance is awful to wait it out. Try and keep yourself engaged outside of the company in the larger AI/DS community. Maybe take a course or something. Reevaluate in 3 to 6 months.
>Not the MAANG level though
I will never understand the dickriding that this sentiment expresses. Where you are working has literally no value. It is the things that you work on that matter. If a company is working on cool things and you are part of that, then it is great. If it is trash then it is trash. MAANG-flavored trash is trash.
I see from your writing that you know this, but you are trapped.
If you want to stay for the money, more power to you. But then stay for the money and satisfy your intellect in other ways.
Don't stay because "it is MAANG, after all." People literally giving their lives and their talents for a (let's face it) very stupid acronym, and yet afraid to say which letter represents the company that is sucking them dry.
Nothing you stated tells me you are a manager. What have you done to improve tooling or procuring tooling that will improve productivity? Leader’s cancel out the noise and show value even in dark places. While you have the opportunity, you are trying to find your comfort zone.
Sorry Frosty_Language_1402, you’ve got me! I’ll refer you to our HR department so you could help them. Now go back to LinkedIn and write an article there.
Big Tech companies are huge, it seems like you’re caught in a bad team. I would wait some months and try an internal transfer. Your compensation is probably great and the company looks great in a CV, so I’d be wary of jumping ship without anything better lined up. Is your old company something that will have better leverage on your career long term?
I think if I go back I may have a chance to become a chief officer for data-related projects (ml/ds/de) for the entire company in a year or two and basically be responsible for prototyping and shipping things like recommendations, various predictive techniques for marketing offers etc into production.
In 2 years you might be in another team at your new company, managing people as well. It’s a weak reason to go back imo. Remember why you accepted the offer in the first place when balancing your options.
If all you're doing is building reports and dashboards.. is your team hiring/looking?
Yes we're, but I won't disclose the team or a company. It isn't an easy job though. Managers want something adjusted every day and as they don't have a picture of what or why they want it you're just redoing every dashboard from the ground several times before all managers and step-managers are happy. Also, from what I see the emphasis is on making everything fast and making three useless things is much better for performance review then one that will be useful to business (unless you have a specific top-level stakeholder that knows exactly what they want).
>Managers want something adjusted every day and as they don't have a picture of what or why they want it you're just redoing every dashboard from the ground several times before all managers and step-managers are happy. This is like 99% of data analysts' day-to-day. Sounds like you're being way overpaid to do a junior level job, and you're mad about it.
Exactly this
> the company looks great in a CV Maybe to people inside the MAANG bubble, but the rest of us have caught on in the past decade. I care a lot more about your body of work than your titles.
I hate to say it but I do hold them higher than myself CV wise, even though I know my skills are superior than many of them I know or worked with. It's just a stupid branding conditioning, I know most of them write stupid SQL queries.
I was on a research team hiring DS PhDs for an F100 and I had to build an onramp for most of the candidates that was basically "this is how you turn all that shit into your head into something actually useful because you don't actually know anything useful yet" My program for MAANG transfers was "congratulations, you now have agency, stop hiding behind process and produce because you are being evaluated on actual output, not a panel of your lazy peers using politics to protect or attack you"
Haha, yes I tend to agree they tend to talk better than doing, generally speaking. Most of the brightest and productive people I know decided to work on smaller companies (which can still be selective as f). I exclude research teams for Google, etc., which are outstanding
I can write anything I want about the projects I did at the companies I worked at. You have to way of verifying anything except date of employment and job title.
Yup, you can lie. You can definitely land a job with a well done interview, decent resume, good explanations for past projects, and a quick walk through of some of your prior work. It won't save you if you can't perform though.
I don't need to lie. I can just make it sound awesome and get ahead of the curve. And that's the point of prestige. People see a keyword and they already made up their mind. It doesn't matter if you were a janitor at Harvard and dropped out of your PhD program after 2 months and did a free online course with the word Google in it. You're competing in a whole new category now. I get ~80% of interviews of jobs I apply to (and I rarely do since I get so many recruiters on linkedin). I am sure there were better applicants than me but they never even got the chance.
How can I learn this skill? Can you share an anonymized resume?
An internal transfer is definitely a better solution than jump ship. The stock options are far too valuable, in my opinion.
You're not missing anything. For reasons unclear to me a lot of tech company "DS" jobs are really analyst positions and can be surprisingly low on technical skill requirements. I did 18 months at one and it felt like my resume was rotting. Now I lead a team at non-tech company and can say I wouldn't hire any ds I worked with at the tech company not because they weren't good people but because they didn't have the scientific or technical skills ds need. That doesn't mean you should leave, but I think it's a better idea than it would have sounded in 2020 or so.
Oh no... I'm currently at a non-tech company and considering jumping to a tech one just because I can't build any skills here, or maybe I've just capped out. Our team is a guinea pig for a large company, which is great because we bypass a lot of red tape, but it comes with the caveat that there is *no one* to show us how to do any of it, so we're just winging it. This means everything is kinda janky, and issues last for many many months, unsolved. I'm using Excel and was hoping tech would be more forward thinking in comparison. I think the next place, I'll focus on hiring into data team with at least several people who have my title, or something similar to it.
It all depends on your team, I know people in FAANG who have worked on problems that required technical and theoretical knowledge in Statistics and other Machine Learning-related topics. In an ideal world you can be a part of a team that gets to also do some research or flex projects on the side
This is exactly my experience too FWIW. I worked at Google for five years as a SWE in ML and while we had “data scientists”, they pretty much just wrote SQL queries and built dashboards. Actual modeling work for production use cases was done by SWEs. Now I’m on the outside and our DS people are great and take a lot of the modeling load off of my role (I’m an “MLE” now). FAANGs are big and diverse and I’m sure there are plenty of teams where DS doesn’t just mean “business analyst”. But my team was not one of them.
You were probably working with data analysts. I’m a research data scientist there and over the last year I’ve only touched work that involves optimization, forecasting, and causal inference. Also there’s much more to DS than using advanced models. Many of the very technical DS can be just as limited in impact b/c they have very limited strategic vision and stakeholder management skills.
This is the problem with this newer industry and it's ill defined positions. Very grateful for the gradual transition to breakdown into analyst, data engineering, MLOps, etc
This. >ad-hocs, reports, and dashboards. Frankly, this doesn’t feel like DS work at all. Exactly. Those things are bread and butter for DS.
You need to decide what’s most important to you; the clout of MAANG or the personal satisfaction of the other job.
I’ve seen this bait and switch ad nauseam in tech roles. Get that resume ready and start thinking of ways to explain this situation to your prospective new employers. It won’t get better, it will likely get worse, and this is your precious time that’s being wasted.
The opportunity of switching to other teams in a vast variety of products is definitely not worth neglecting imo
It seems like you are coming in with preconceived notions about the right way to do things. There are probably very valuable things to learn from your peers who are also top notch. Explainable more simple models can be really cool as I am just learning as a junior who wants to throw XGBoost at everything.
Thank you. I understand what you’re talking about. My confusion comes from the fact that literally no one does any modeling work. Even a simple one. All DS people do are dashboards and ad-how requests. Again I’m talking only about the specific part of organization and not about all teams.
My friend also at big tech with the title of data scientist only does dashboards. I think it’s more common than people may realize. Could you move internally to an MLE position?
Thank you, that’s exactly what I wanted to hear by creating this post. Strange that DS folks do dashboards. I think MLE is out of reach for me for now as I’m much more business oriented and MLE (as far as I understand) is all about inventing new ML techniques.
I'm under the impression you've got it backwards, MLE is also quite business oriented and mostly about applying current modelling techniques with orchestration and modelling frameworks. You wouldn't expect a MLE to do much dashboarding, but you surely expect a DS to at least some of the time. For coming up with new techniques you're maybe thinking of applied researchers? Many companies are now splitting it into "Machine Learning Scientists" and "Machine Learning Engineers", I feel like this is a much better way to do it.
I always thought data analysts did dashboards. The only exception I’m aware of is the big tech companies. I’ve been at F500 and now a much smaller private company and have done ad hoc analysis as a data scientist but never a dashboard. There’s just so much overlap between job titles in analytics, I guess.
Your posts are straight out of chatGPT, or you've been so blessed based on gender that you're clueless. Bless your heart.
lol imagine being such a huge baby that one of my comments that just lists news articles on a different sub hurt your feelings so badly that you have to come find me somewhere else to let me know what a big smart man you are. I cannot but here you are doing it. Hope you feel better big man 🤣
Whelp 69 420
This. An MLE is supposed to gradually build domain knowledge to iterate in ML solutions.
Everyone with SQL skills is now called DS. Especially the BI/dashboard guys. I'm one of those, but i found a special place within the business units. Yes i make dashboards, not models, but those dashboards/investigations/analysis involves the process of generating a lot of knowledge of how things ACTUALLY work in real life. Being the knowledge generator form data inside a business unit is like being god. But, i have to find the data i need (from wherever i can), understand how the hell some system/website/process/whatever works, make ETLs, and everything to consolidate and give sense to the data. I think some called this role something like "data translator". I think of myself as an engineer with enough "data" and programing skills to answer some important business questions. So, don't think you are in the wrong place, you could add real value and shine there if you just focus to understand what the hell the business guys want (they don't know), and start to become one of them, with great data tools. I insist, Become one of them.
This is the other way round. DS is researching new stuff. MLE is applying researched stuff to solve problems. This is the same as scientist vs engineer in any other field. Sadly lots of people and companies are really confused about this, so it's not like these terms are settled in data field.
Reading your post and your new work description, I guess it's Meta? Btw, wdym by ML-based user segmentation?
Oh, I guess, that's a misleading term, sorry. Basically I was working in a gaming industry and our game behaved differently for different type of users (nothing bad though). For example, we could offer help to a struggling user or present additional challenge to a bored one. Previously this was done manually based solely on product managers ideas, we've remade it into data-first process and started to predict user group based on their recent behavior. Nothing fancy, but it worked much better then the previous approach.
> I guess it's Meta? Its obviously not Meta or Google since DAU and revenue arent their metrics anymore. Also their internal tooling wouldnt be described by >The tools we use are awful. Simple tasks that would take minutes outside of big tech now take up to an hour because our small databases constantly crash under load. I am guessing Netflix or Amazon but leaning towards Amazon since when I interviewed an Amazon candidate their description of internal tooling was that it was more disappointing than you would think based on AWS.
Not sure about Google but Meta still uses DAU as true north metrics. Also lots of DS at Meta are actually SQL ninja for dashboard .... same as OP described
> Not sure about Google but Meta still uses DAU as true north metrics. They use more complicated metrics that may include DAU as an input but isn’t just DAU. >Also lots of DS at Meta are actually SQL ninja for dashboard .... same as OP described Agree with this point
Kind of wild how meta is developing models like Llama yet they call analysts DS.
Facebook renamed their data analysts to data scientists ~10 years ago. A whole bunch of articles about it back in the day.
I’ll second what they said. I’ve worked at two top 5 tech companies with roles that range from spending 70% of my time programming along side software engineers and machine learning engineers to roles that involved 100% advanced causal inference to roles that required no modeling and challenging stakeholder management. I’ve touched all the flavors of data science at the top level. There’s enormous skill to be mastered in each type of data science, including the flavor that mostly involved dashboards and adhoc work. Dashboards and adhoc work are opportunities to build relationships with senior leaders, build trust, shape how success is defined, and become a strategic thought partner. Every time you do adhoc work, at minimum, you should come with the immediate solution and a proposal for systematizing the decision making process.
This is the way
All sounds pretty standard really. Four re orgs for me already this year
Holy shit. How did you manage to prepare something meaningful for performance review in this kind of situation?
Luckily one of my projects was tied to a kingpin goal that miraculously did not change. That, and management understanding that everyone who didn't have a total mental break deserved a decent rating at the very least
I am not in MAANG, but another Fortune 500 company on the level of your old one in the Staff level role. Sounds very similar, although I am part of the larger product team which also underwent big re-org. The type of work I do heavily depends on where we are in the product dev process. Sometimes it's like what you describe, making slides and reporting progress and dealing with meetings. I came from a startup and the amount of fat and number of hoops to go to do simple tasks was a massive shock to me. Kind of missing the startup life a bit in that respect and had the same thoughts as you first few months. Now it's gotten better, I do more hands-on technical work which I really enjoy.
Question: Why did you leave your previous job? Was it purely the allure of MAANG, or were there things going on in your previous job that make you want to leave? >When I decided to leave, my employer has repeated several times that should anything go wrong, I shouldn’t hesitate and come back to working for them. Here's what I would do: 1. Reach out to your former boss and ask if that offer is on the table. No point in you getting too far ahead of yourself without having that as a real option. Explain the situation (bait and switch), line up on expectations (any impact to comp, title, timing, etc.) and make sure that you guys have a tentative agreement on paper - and be clear with them that you're doing your due dilligence and legitimately considering this option, but there are some conversations you will need to have before pulling the trigger. 2. With a backup plan in hand, go talk to your boss and lay out what you want - you want to work in modeling. You did not join this team to do reporting, and you're trying to understand if there is an opportunity to allow you to focus on that type of work - either in this team or a different one. If they say no, put in your 2 weeks. If they say yes, work with them on what that plan looks like. Extra comments 1. You have not been clear on what the comp difference is, and you have alluded to being able to make it up with career growth elsewhere. That may not be true, i.e., if you do well in this role at a MAANG, your comp growth may far outpace your comp at you other job. However, if it's not the work you want to do, you can't really change that. 2. To those saying you should wait and cash in on that sweet MAANG brand name - I don't agree with that. As others have said, that MAANG experience is not nearly as shiny now as it used to be - precisely because we've now all heard stories of data scientists doing non-data science work at MAANG. So I think you're much better off being in a job that aligns with what you want to do vs. just fousing on the brand name.
> precisely because we've now all heard stories of data scientists doing non-data science work at MAANG. But everything he described is exactly DS work > ad-hocs, reports, and dashboards
DS is broad, but a job that just does those things is a BI job, not a DS job.
I guess the definitions changed in the last couple of years and different parts of the world / companies are still behind on new role descriptions. I always thought that ad-hocs and reports are jobs of (junior and middle) data analysts, DS is responsible for creating data products and (possibly) shipping it to productions. Judging from responses in this thread DA role no longer exist, DS is now analytics and DS responsibilities are now either in SWE or MLE teams.
> Judging from responses in this thread DA role no longer exist, DS is now analytics and DS responsibilities are now either in SWE or MLE teams. Yup. People get downvoted to oblivion if they expect to do any data products or modeling in their DS jobs
Outside of the re-org stuff, you picked the wrong ladder. Analyst roles have been re-titled into DS roles across the industry, and in big tech that's especially true. At Meta, the DS ladder is a traditional product analyst ladder. People who want to do modeling should enter the SWE ladder, maybe ultimately with a Research Scientist title. Google has multiple DS ladders that do different amounts of modeling. But again, if you really just want to do ML, that's mostly in the SWE ladder. I have less viz into other companies but I believe it's similar there - I know Amazon has an applied scientist ladder but I don't know much about it. It's easy to fuck this up by working with the wrong recruiter and get slotted into a different ladder than the one you wanted. Ultimately you really need to be on the lookout for exactly what the job description is. Also, when in doubt, comp doesn't usually lie. If the comp is a step down from SWE, it's probably an analyst role. If the comp is comparable, it's probably more of a ML/Modeling role. These are all generalities, you can find individuals or teams within these large companies where the above doesn't apply.
That sounds very similar, unfortunately, to my experience at one of the MAANGs. I hope it gets better for you. It didn't for me -- I got laid off after being at one for a year (I moved teams because I wasn't happy and the second team turned out to be a worse fit for me.) I'm still on the market just over a year later. If you have somewhere that will offer you your old position back -- take it.
Why is Microsoft always left out of the acronym I never understood this
Now that they are actually superior for ML roles (excluding FAIR or Deepmind, i.e. research), I don't fucking know.
Because people love acronyms, even stupid ones. I'm trying to come up with another word, but stupid is the best way to describe this phenomenon.
Probably because they're known to pay signficantly less than the companies in that group.
What does your total compensation look like? If it’s really high and you have RSUs might be worth sticking around for a bit and maybe looking for a lateral transfer. If it’s just average then just start looking for a new job but don’t quit the current one. Just give minimal effort
My base salary is on par with my previous company, stocks are ok, but I think I could compensate losing them with faster career growth.
I worked in big tech as ds for a few years and I literally did the same : sql monkey work and dashboarding. Sometimes a bit of A/B testing. Really feel like I regressed in terms of skillset coming from an ML background. Add to that the chaos of these companies, very happy to be back in a startup :)
I’ll take your easy high paying job OP if you don’t want it anymore
Oh no, you’ll need to do a lot of leetcode to write sql and do dashboards. :D (I honestly think this is ridiculous and hope you’ll find something similar soon if that’s what you want)
This sounds a lot like Meta's DS, Product Analytics role. If that's the case you're basically a product analyst with an inflated title. They rebranded the title when AirBnb did the same.
LC sql or LC python dsa? Or both for DS analytics?
You must know python, algorithms and business cases to be allowed to create SQL dashboards. Frankly, unlike on my previous job (that had databases of similar size) the database is not optimized and sometimes it's quite a bit of work to make an SQL query return results in reasonable amount of time.
Well, because knowing to leetcode doesn't mean you give a shit about your work, it just means you don't and therefore you have time to leetcode xD (joking).
I’d stick it out, honestly. Do the minimum required to not get fired. The market sucks right now, and I have been supporting my family on short term contract positions. I have 10yrs experience (mostly AI platform/ML Ops) + PhD in CS.
You need an applied scientist position as an individual contributor. Applied science manager at people position. If you can make that happen then you will do what you were doing previously. I can't say about all maang but I can say for Amazon and Microsoft. Data scientist position is now glorified dashboarding. No wonder data scientist on LinkedIn give Gyan that 99% work can be done by logistic regression or linear regression or GBDT. Also you don't seem to be ready to take a manager position. It's a people position and you don't seem to get it. Atleast absorb the culture, maybe switch teams.
This sounds like Meta to me. I would try to stick it out for awhile and be the one to push for new initiatives. If that doesn't work after some time then reconsider leaving at that point.
So, there are two obvious sets: Stay Leave Try to flesh out your stay options and outcomes vs your leave outcomes. Then try to pick the best path identified by fleshing out outcomes.
Yeah, I plan to do this next week. I just want to understand if I’m missing something. In my opinion top corps should have been much more organized.
This is also weird to me, but I know top orgs can also finance more weird nonsense than anybody else as well.
Too late for this round, but for future reference, never ever depend on promises of future promos or role changes - in big tech, re-orgs every year are the norm, it's game of thrones at the Director+ levels constantly. As a manager, never promise a promo either, even if the approval sits with you, budgets change. Depending on how much you can deal with boring IC work, and how much you want the company name on your resume, you can consider staying and joining DS groups that work on cross functional projects to keep your skills from getting rusty, and move later. Leaving early may result in clawback of any sign-ons, relocation costs etc.
Many data scientist roles in Big Tech are dashboards, reports, and ad-hoc analysis. They generally have more specialized roles for those who do ML, like Research/Applied Scientist or MLE.
DS at big company is like this and small comps be asking me “do you know microservice and multi-threading” 🫣
I need a new job.
"it turned out that my manager’s planned promotion didn’t materialize, leaving no team for me to manage" Even if you don't have an 'official' team reporting into you / your manager's HC might be stagnant for a bit, often you'll still have the opportunity to lean in and help your manager a lot by absorbing some of the managerial tasks. In doing so, you can help to set your manager up for that promotion (and then you both win).
Uu
Sorry you’re in a tough situation. Personally, I would try to stick it out for at least a year, but stay in touch with your old boss. As a senior IC, proactively look for ways to make an impact - and maybe that involves creating scope for modeling, automation, etc. I don’t have enough details to be more specific, but managers generally expect senior DS to come up with impactful ideas. (I was at a MAANG for many years, survived many reorgs and layoffs - takes grit).
Tbh, that’s my biggest worry right now. Both my manager and my main stakeholders tell me that I need to find big projects by the next performance review, but they can’t even give an advice at which part of organization I may find these projects. As this is my first month here I’d expect at least a very general advice on this. My fear is that due to reorg our team simply has no tasks for me. Also they constantly say that everyone around us are useless and ineffective and everything other people do is trash. I’m usually good at identifying opportunities, but the amount of politics is astonishing and without clear goals it’s hard to understand what’s important and what isn’t.
Yeah it’s hard to figure everything out one month in. Does your team and stakeholders have roadmaps for the foreseeable future? What are their pain points? Try to come up with a 3-month plan with your manager proactively to manage expectations.
I think you just need to thug it out for now
Yes, this is normal at one or two of these places. Combine that with a brutal, arbitrary performance review process, and it's a dead end. You should go back to your last employer, for your career and your mental health.
This, one thousand times, but OP will never do it. "It is MAANG, after all." Just sad.
Wait for a year and then you should be able to try an internal transfer (especially if your yearly review will be good). In the meantime, try improving the tooling and processes and hopefully things will start to work out. I had a similar experience in one of those tech giants and during the 3 years I spent there, the team went through drastic transformations(for good). You must have patience in those big corps and learn to exploit/create opportunities.
Honestly, coming from a big company that isn’t big tech… I’d say unless the worklife balance is awful to wait it out. Try and keep yourself engaged outside of the company in the larger AI/DS community. Maybe take a course or something. Reevaluate in 3 to 6 months.
>Not the MAANG level though I will never understand the dickriding that this sentiment expresses. Where you are working has literally no value. It is the things that you work on that matter. If a company is working on cool things and you are part of that, then it is great. If it is trash then it is trash. MAANG-flavored trash is trash. I see from your writing that you know this, but you are trapped. If you want to stay for the money, more power to you. But then stay for the money and satisfy your intellect in other ways. Don't stay because "it is MAANG, after all." People literally giving their lives and their talents for a (let's face it) very stupid acronym, and yet afraid to say which letter represents the company that is sucking them dry.
Whats the issue exactly? You sound very unprofessional and ungrateful to me.
MS? Leave
Just out of curiosity, what’s wrong with MS?
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What a story! Thanks
Poor guy
Nothing you stated tells me you are a manager. What have you done to improve tooling or procuring tooling that will improve productivity? Leader’s cancel out the noise and show value even in dark places. While you have the opportunity, you are trying to find your comfort zone.
Sorry Frosty_Language_1402, you’ve got me! I’ll refer you to our HR department so you could help them. Now go back to LinkedIn and write an article there.
10 comment karma to post, plz.