What do you do that you earn six figures?

[u/fidgit86]

It seems like a lot of people make a lot of money and it seems like I’m missing out on something. So those of you that do, whats your occupation that pays so well?

[u/r5d400]

data scientist

[u/HeavyFuckingMetalx → u/r5d400]

This is what I’m aspiring to be.

[u/AtomicKitten99 → u/HeavyFuckingMetalx]

ML engineering and research scientists do what data scientists did 10 years ago. A lot of “data science” positions were business analytics positions 10 years ago.

[u/r5d400 → u/AtomicKitten99]

this is so correct lol
I work with engineer and research but we're definitely a minority. almost every DS is actually an analyst these days

[u/proverbialbunny → u/r5d400]

It's actually going in the other direction in very recent years. There are so many ML Eng jobs out there today with the title of DS, just because DS pays less than ML Eng and DS is a sexier job title. This muddies the water.

I'm on the eng R&D side myself, not ML Eng, but startup space figuring out how to solve problems no one in the world has figured out. It's tons of fun!, but not for the faint of heart. ^_^

[u/r5d400 → u/proverbialbunny]

why does everyone want to take over the same words. like, get your own job title, people! lol

[u/proverbialbunny → u/r5d400]

Data scientists are supposed to be experts at classifying, yet DS is the worst classified job title.. lol.

Before I had the DS job title I was a Research Engineer. I actually do mostly the same work today just with newer tools (notebooks in R or Python instead of Excel and Perl). I find it arbitrary. Oh, companies want to call what I do DS now? Okay. Whatever works.

[u/AtomicKitten99 → u/r5d400]

Because of the salary and perception. Facebook initially called their analytic data scientists “decision scientists”, and a lot of them still put data scientist on their resume and squirmed at the new title.

[u/BinodBoppa → u/proverbialbunny]

The sad part is, every word of paragraph 1 is true😢. Was interning at a startup with a job desc of basically applying GBMs and then analysing the results to eliminate bias but those bastards lied and forced me to do OCR+Bert fine tuning on a worthless dataset.. and then didn't even pay me those $100. Good thing is, their plan backfired in front of the investors and ig those VCs tore these guys a second asshole.

[u/dbirdflyshi → u/r5d400]

My favorite explanation between data analyst and scientist these days is this: analysts focus on the why is the data that way and the how did the environment get this way and scientists focus on the what

[u/SEA_tide → u/AtomicKitten99]

I've had to explain to many people that if you're talking to someone in the field who got their degree more than ~5 years ago, chances are their degree program was not technically called data science. For the longest time, it seemed like data analytics was going to be the desired name, even though many people were majoring in things such as economics, statistics, mathematics, management information systems (MIS), and computer science.

[u/AtomicKitten99 → u/SEA_tide]

I’ve had to talk to all my recruiters that those programs are complete trash if you’re targeting a core role. If somebody has a master’s in data science and a completely unrelated undergraduate, don’t even screen them. Those programs are all about vanity, name-dropping, and generating a shit ton of cash off the ML buzz. The fact that the name of the program doesn’t actually indicate an area of study, is that subject to opinion, and the fact that nobody offers a Ph.D in data science should indicate that the degrees are empty.

Considering this is about financial independence, I can’t think of a better way to be living paycheck-to-paycheck with a respectably high-paying job than to get one of those degrees @ $70-100k. Not only is this price high, but it’s for online instruction with C team instructors (e.g. Berkeley’s program runs through the school of information and not the stats, engineering, or CS departments).

UIUC and Ga Tech’s MS in CS programs can be done part-time for a fraction of the cost, and their material is legitimate and hireable.

[u/SEA_tide → u/AtomicKitten99]

My "data science" master's is technically in applied economics as data science programs didn't really exist a decade ago, even though many of the courses are similar. My undergrad majors are very closely related and I've been employed in analytical positions my entire professional career since. I'm reasonably sure I've not gotten interviews because my degree doesn't say data science.

I've considered going and getting another degree, but am amazed at the cost of some programs and don't want to duplicate what I already know with a degree similar to what I already have. Georgia Tech has been on my radar for years and a number of managers seem to really prefer MBAs, but I'm just not sure.

[u/mwedzi → u/AtomicKitten99]

I'm now a 'data scientist' but until recently I was a 'research scientist'.

[u/shameOnDandD → u/HeavyFuckingMetalx]

Just want to say that a data scientist is really downstream from many other data jobs - data architecture, engineering, augmented insights. Data is a big thing right now because it's the new oil. I guess data is a big deal if it can get presidents elected.

[u/HeavyFuckingMetalx → u/shameOnDandD]

So are you saying don’t search for jobs with just “data scientist” as the title?

[u/catdog918 → u/HeavyFuckingMetalx]

I was searching for jobs with business analyst in the title but ended up getting one without that title but doing the same stuff

[u/HeavyFuckingMetalx → u/catdog918]

What’s the title you have?

[u/catdog918 → u/HeavyFuckingMetalx]

I’m technically a collections analyst. When I searched analyst instead of specifying business/data analyst this job listing popped up.

[u/dumpsterthroaway → u/shameOnDandD]

Yeah its importance became really evident to me especially in 2004 and even more in 2008

[u/wntrsux → u/r5d400]

So you're a glorified statistician with some R and python chops

[u/sshwifty → u/wntrsux]

This is why data science sucks for a lot of people. You end up cleaning data sets, or putting together APIs and rarely do any science, and when you do it is some outdated framework like R.

[u/rawlskeynes → u/sshwifty]

Don't know why you're getting downvoted. This is true for an awful lot of data science roles.

[u/sshwifty → u/rawlskeynes]

Yep. I worked 3 different "Data Science" rolls across three companies. All totally different, all very little 'science' and a lot of busy work. I get about a message a week from recruiters looking for data scientists and I still turn them down.

[u/r5d400 → u/sshwifty]

I totally agree with that you're saying and it's unfortunate that the words 'data scientist' can go anywhere from 'does some nice plots and power points with tabular data' to 'does classical stats' to 'does machine learning' and to 'ships production code'.

I do the last two, I don't enjoy the second and I absolutely hate making power points. but yea we're all called the same thing. the truth is most data scientists are actually analysts and not scientists. but it is what it is.

I no longer care about the names, I read the full job description and minimum requirements and ask what the work is like on the interviews. unfortunately that's time consuming.

since I do ML, any role that lists ML as a 'preferred' is a direct no from me. it's gotta be a hard requirement for the job, otherwise I know it's not a good fit. I also reject any descriptions that 50% of the text is about how you'll be explaining things to stakeholder. while I do explain things to stakeholders (who doesn't), any job that describes itself around this basic requirement, in my experience, is not doing technically advanced things

[u/Mountain_Calla_Lily → u/r5d400]

What are usually the requirements for the job? Also, are there a lot of WFH situations?

[u/r5d400 → u/Mountain_Calla_Lily]

most jobs I'm personally interested in require a masters degree, although in practice most of the coworkers have a phd everywhere I go (I only have the masters). so it's more research oriented, which is what I like. it's not a hard requirement to have a masters to do ML though, it's just that jobs that filter on that requirement are usually more technical. less likely they're hiring phds to do plots and power points, you know. some go as far as asking for published papers (I have a few, but nothing super noteworthy) although that's rarely a hard requirement.

I specialize mostly in deep learning. so reqs are often knowing something like tensorflow/pytorch, be up to date with the literature of ML, sometimes spark. def gotta know stats and python too, and the bread and butter libs like scikit learn, pandas, numpy.

the jobs I prefer also require you to write production level code, so like, soft SWE skills. as in, the code I write ships directly into production, I don't send my code to a SWE who then makes a bunch of changes to productionize it (some jobs are like that, not my jam). as a result, I always have to do leetcode in my onsites (which the vast majority of DS don't have to do, because they're analysts) and I never get asked to do SQL on the onsite (and it's seemingly never in the requirements, although it IS a requirement, but probably one that seems basic next to all the more technical stuff they ask for)

many jobs I'm interested in are called MLE or research scientist. but many are still called DS, so it depends. naming conventions are god-awful in this field, so you really can't escape reading the description to see what the company thinks a given title means

[u/Practical-Camera-399 → u/r5d400]

I'm looking at getting a masters in data science. How much math is involved? I studied earth sciences and never got past Calc 2. I've been working on a decision tree project using scikit learn at work and would much rather be doing SQL, python, other data things, than outside in the heat or cold for a consulting/mining company. And I am a research scientist (: but while I think it would be awesome to be an engineer at NASA, i don't think I have the work ethic to make it through all the math classes

[u/r5d400 → u/Practical-Camera-399]

How much math is involved?

that will totally depend on the program, so you should look at their curriculum to find out. some are definitely more geared towards training people to be analysts, and have lots of courses that are business oriented, about finding insights, that sort of thing, and some simple A/B tests.

and other are more geared towards actual statistical modeling, machine learning, working with really large data sizes, that sort of thing. those can be way more math heavy. you should understand calc 2 to properly understand something like deep learning. you don't "need" to know it to train a model because libraries have gotten much easier to use these days, but for anything more complex where you need to come up with your own designs, you gotta understand how it actually works.

I hope what I'm saying makes sense. anyone can call "fit" on a sklearn model without knowing much about what the model is even doing, but you need to understand it to progress further than the most basic of things. however, for several jobs, 'the most basics of things' is all you'll have to do. which is ok, different strokes for different folks, but you should keep that in mind when picking a program. it's hard to migrate to technically heavy jobs if you do math-light program

I'd start by taking a look at what jobs you'd want to be doing in 3-5 years, and work backwards from there, ask yourself if the curriculum seems like it would teach you the fundamentals of what you need to know for the role

[u/ilikebourbon_ → u/Practical-Camera-399]

I earned my masters in DS after a career in mobile infrastructure (cyber security and rugged routers).

I’m an NLP data scientist and the bulk of my job is heavy on statistics and data cleaning….text is tedious. But I enjoy the subject and the skills. The weekly 4+ LinkedIn messages are validating as well. My job has some math involved but the bulk of my work is stats, sql, Python, SAS, tableau. However, understanding linear algebra helps tremendously. Especially for semantic analysis

[u/r5d400 → u/Mountain_Calla_Lily]

just noticed I forgot to answer the second part of your question. yes, there are lots of WFH jobs, I do get pinged by recruiters for remote positions every now and then. but do they pay as well as the office positions? that is hard to tell.

tbh I haven't gone through with any interviews for remote positions to actually get an offer and see what it's like. I prefer being in the office for now (well, covid aside..). so idk how they pay, but they exist

[u/MikeyCyrus → u/sshwifty]

What do you do now?

[u/sshwifty → u/MikeyCyrus]

Full stack development lol. Turns out I prefer specific tasks with exact outcomes instead of analysis, or building analytical tools.

[u/Tal_Onarafel → u/sshwifty]

Damn, R is outdated? What are people using now, Python? I thought R was cutting edge rip

[u/killerbootsmanthanks → u/Tal_Onarafel]

I use Python

[u/ymao0000 → u/Tal_Onarafel]

I guess im alone in this opinion based on your replies but no, R is not outdated. It’s always been more cutting-edge than Python, and always will be, specifically because it’s “not a real language” (I.e., niche) and the ecosystem doesn’t need to be as stable or back-compatible as Python.

Run of the mill DS that’s using old/tried-and-true methods will use Python more effectively, but new tools are almost always written in R first. R is also catching up in some areas it has historically under-performed in like GIS and many-dimensional array analysis. Speed is also becoming largely irrelevant - in both languages it really just depends on how much C/C++ integration you have anyway.

[u/r5d400 → u/ymao0000]

Speed is also becoming largely irrelevant - in both languages it really just depends on how much C/C++ integration you have anyway.

this isn't true for people who work with large datasets or who productionize online models. where does anyone ever use an R model for online predictions? they don't. R is more geared towards academics and it has its uses, and I'd accept that *some* stats tools are written in R first, usually the ones being written by those same stats people in academia. but it's nowhere near acceptable for big data uses, where in python you can use pyspark, and even if the dataset fits in memory, numpy is much faster for most operations than its R equivalent libraries. you'll never see google, microsoft, facebook etc using R in their deployed models. at most it will be used in early prototyping stages with small dataset samples, and only if their scientist is really into it

but it really isn't true about the R implementation being the first for most state of the art models like deep learning or boosted trees etc, where most authors of conference papers publish their code in python, not R. just check papers from ICML, CVPR, Neurips, which are some of the top conferences for ML and you'll see way way more python than R being released

[u/ymao0000 → u/r5d400]

It’s me - I’m the guy that uses R for online predictive models. It’s just a small part of my current job, but there’s plenty of us. There just isn’t the library support in Python to accomplish some new methods, or handle all use cases. Sure, R might need to be ported to something more efficient if usage really goes up, but that probably won’t be Python.

I work in both R and Python and I really don’t see a difference in speed for big datasets, although Python has better out-of-the-box parallel support. Again, the libraries that are handling the really big datasets (at least that I use) are written in C, C++, or Fortran, and the difference between a Python or R front end is negligible. ML isn’t my specialty and I can’t comment confidently either way, but (e.g.) the TensorFlow libraries work just fine in R. Is there a difference in front-end libraries that make Python inherently better, or is an abundance of Python code in ML literature a legacy effect from the early involvement of large companies?

Python has its uses and I’m not trying to say it isn’t a great choice sometimes, but i think in data science overall you can’t ignore R if you want to be at the top of the game.

[u/mydoghasocd → u/r5d400]

I’m in academia - stats/public health/bioinformatics- and I code exclusively in r, all papers I read have r code in their appendices, entire bioinformatics processing pipelines are available in R. Have never even considered learning python, because r is so versatile and I can’t imagine ever needing anything for “production”

[u/Fattyblob → u/Tal_Onarafel]

Definitely not outdated. In fact, data analysis, manipulation, and visualization are better in R than in Python (in my opinion). On the other hand, model deployment and production are easier in Python. If you come from a statistics/math/academic background, R will likely make more sense. If you come from a SWE/Computer science background, you’ll probably prefer Python.

I’m biased towards R, so take my opinion with a grain of salt :)

[u/monkeystoot → u/Fattyblob]

Tidyverse ftw

[u/sshwifty → u/Fattyblob]

What you said. I came from computer science and prefer not R. Going from a OO language to R is jarring. Even coming from Matlab or Mathematica to R is a bit of a hard adjustment. R has it's place for sure, but it is not for everyone.

[u/split-shift → u/Tal_Onarafel]

R is still around, I just love to hate on it. It is the Oracle of Data Science. It works, but is kinda a pain. 5 years ago R was the thing, but now so much is written in Python and Python has mostly cought up in terms of capability/usability that it makes less sense than Python. R is useful, but so was (is) Ruby

[u/the_monkey_knows → u/split-shift]

R will continue to be around. Rstudio is light years ahead of jupyter, and R is more geared towards statistics, which is the foundation of data science. For machine learning and integration into other platforms python is a good choice, but R isn’t going anywhere. I believe it shines as a standalone tool for quick data analysis and exploration at the very least.

[u/Ixolich → u/the_monkey_knows]

Yeah, this is pretty much where I'm at. If I'm building a model that requires actual work it'll be in python, but if I get called in for some quick exploratory analysis I still default to R.

Like with most things, it's about using the right tool for the right job.

[u/This-Moment → u/the_monkey_knows]

Cobol also isn't going anywhere... :D

Edit; Also Fortran is still hot.

[u/mydoghasocd → u/This-Moment]

You joke but I need to code a massive project in Fortran this year...

[u/Positive_Jackfruit_5 → u/mydoghasocd]

Yep. A ton of statistical functions found in R and Python still call Fortran code written in the 80s

Fortran is still very much alive, just existing in the background

[u/BinodBoppa → u/mydoghasocd]

What are you working on btw?

[u/This-Moment → u/split-shift]

I want you to know I upvoted you for dunking on Ruby. :D

[u/Tal_Onarafel → u/split-shift]

Thanks for elaborating!

[u/r5d400 → u/Tal_Onarafel]

lol its not cutting edge, but it's still widely used in academia (some of its available libraries for frequentist stats are *still* better, as in have more features, than its python counterparts) and is generally beloved by classical statisticians.

many companies still use it, but R is pretty terrible as a programming language. python is more versatile, and more efficient (in cpu/memory etc usage).

DS has people who come from stats and those are the ones that tend to like R. but DS also has people who come from CS and engineering, and those often prefer python.

if you're doing really large datasets and online predictive models, no way you can stick with R. at that point you really need to make the transition to a real language, and python is the most popular choice. but if you're just doing some offline analysis, R is fine

[u/alurkerhere → u/Tal_Onarafel]

R is not a true pipeline, while Python is. Most companies will prefer Python because you can integrate it and run stuff.

I do prefer R for quick scripts and stats stuff; it is quite intuitive to use with tidyverse nowadays.

[u/Regular-Progress-664 → u/sshwifty]

It’s because it’s such a buzz word (or job title). I worked as a data analyst (mostly cleaning and querying data) and the marketing and sales execs were amazed at creating a drag and drop dashboard in Tableau (which I learned on the job googling everything). I used basic descriptive stats and they were very grateful for my “data science” skills. Writing a basic SQL query was like god mode to them. Most businesses need basic data analytic skills, not advanced statistical methods.

[u/sshwifty → u/Regular-Progress-664]

Exactly. I created some dashboards in Kibana with ElasticSearch, used built in queries and aggregations, and you would think I had invented some new math by the bosses' reactions. The only time I have seen real data science is in hard core analytics like timeseries prediction of massive datasets (weather, stocks). But the majority of positions appear to be the stuff needed to get to the science part (curating, entry, framework development, combining data sets).

[u/ilikebourbon_ → u/Regular-Progress-664]

Stored procedures and suddenly you’re Jesus

[u/othybear → u/Regular-Progress-664]

I’ve been a statistician for the last decade, and it’s been fascinating watching the buzz word job and components that go on around “Data scientist”. I’m perfectly happy in my niche academia job, which is very slow moving so it doesn’t fall into fads (but luckily is aware enough of the fads that my boss keeps giving me excellent pay raises).

[u/catdog918 → u/Regular-Progress-664]

Bro exactly lol. I showed my manager a tableau dashboard the other week and her jaw dropped.

[u/FoosFights → u/Regular-Progress-664]

Nowadays Data Science, Analytics, and Business Intelligence are pretty much interchangeable to leadership at most companies. We have each of those three departments at my company and all do all the same stuff which centers around various aspects of data analysis.

[u/nobadchainsmokers → u/sshwifty]

Why is R outdated?

[u/Elesday → u/nobadchainsmokers]

Don’t worry it’s not.

[u/Momps → u/sshwifty]

This is precisely why I stopped doing it and became a BA. Why fix people's shit constantly when I can put in a project to get it fixed permanently

[u/Keenanm → u/wntrsux]

You say that like it's a bad thing. I play money ball with real estate agent data and get paid handsomly for it. Nobody even gives a shit what my PhD is in, they only care that my predictions are accurate and make the company $$$.

[u/wntrsux → u/Keenanm]

No. Just clarifying it for others, so they are not intimidated.

[u/FckMitch → u/Keenanm]

How does one get into this line of work?

[u/Keenanm → u/FckMitch]

There are a few common paths. The longest path with the highest likelihood of success is to get a PhD in a math heavy STEM subject (math, physics, stats, computer science, etc.). Other STEM PhDs can work as well (Meteorology, evolutionary.biology, etc). An intermediate path with slightly lower chance of success is to get a MS in Data Science. The shortest path with more variance in outcomes is to attend a 12-16 week Data Science bootcamp. I believe there are online and in person versions. Sometimes these programs can be called incubators as well. In these bootcamps you will learn what you need to know for the job, but the hardest part will be to get that first company to hire you. Once you get 1 company, no matter how small, to hire you as a Data Scientist it becomes much easier to work your way up to higher paying opportunities. If you are interested in the bootcamp path it might be worth checking out a self-taught course on coursera or an equivalent website to see if you find the subject matter interesting. One other potential path of self-teaching. Teaching yourself SQL can help unlock low level Data Analyst positions. Those often pay very solid money and open doors for you to gain exposure to Data Scientists. You can further learn on the job and work your way up to Data Scientist from an Analyst position. This path is helpful in that you get paid as you learn and grow and theoretically your salary will increase over time.

[u/Redwinedreamz → u/wntrsux]

I feel this comment hard. As someone who has worked in data analysis/business intelligence (take your pick of job title), working with established data scientists can be frustrating.

"Oh, I don't know SQL. Just clean the data up for me so I can import it into SAS."

Or

"I need you to transform this data into all of these variables and send to me as a flat file so I can import into R and run my linear regression models."

And when I say, "Hey, I could probably help out since I know some R and have built my own regression models and decision trees in the past."

The response is, "Um, no. We have the analysis part. Just fix the data."

That's not my job anymore, but man did that suck.

[u/Redwinedreamz → u/[deleted]]

Lol I know! But many of the older generation data scientists are statisticians who use SAS and have no other programming knowledge.

Personally, I despise SAS with everything inside of me.

That incident happened in 2018, but we worked really hard to ditch it and move to R.

[u/SEA_tide → u/Redwinedreamz]

Here I thought 80% of my work was supposed to be cleaning the data...

My grad school program was partially funded by SAS and as a result I learned their software, but every company seems to discontinue its use once I got there. Even had one contract job ask me to purchase a SAS license so I could show that I knew how to use it; I immediately walked away.

[u/r5d400 → u/wntrsux]

hahah I'm originally an EE and I identify myself more with the linear algebra (like deep learning, derivatives etc) and computer science side of things, than with the classical stats, that I rarely use.

I do know R but hate it and wouldn't pick a job that required it. most DS are more into the business-insights sort of thing though, so I'm definitely more math/coding oriented than your average DS (which makes it harder to find jobs I don't hate). I use python, and write production code for data pipelines and that sort of stuff.

in other words, I'm a data plumber

[u/wntrsux → u/r5d400]

You're a software engineer for data scientists. So am I. Serving their R Algo via python REST APIs running in cloud Kubernetes platform. And writing ETL pipelines to cleanup and structure the messy data.

[u/r5d400 → u/wntrsux]

ah I do a bit of both. I write the models, and also deploy them, and write data pipelines, ETLs etc. but I don't build up the infrastructure from the ground up, the SWEs do that

[u/pacman47 → u/wntrsux]

SQL, Python, and Tableau for me.

[u/SnooDoodles420 → u/r5d400]

What I’m trying to major in….🙌🏻

[u/Extermos → u/SnooDoodles420]

Protip: if you want to be a data scientist, don't major in data science. All the data science majors I know work as analysts. Most of the DSes I work with have masters or (more often) PhDs in some engineering discipline, physics, economics, or math/stats. The data science and coding stuff you can just Google, it's the innate math and stats intuition that employers are looking for.

[u/MxClementina → u/Extermos]

Echoing this, I'm seven years into my Data Science career and studied economics. Anything statistics or math heavy is what will really prepare you.

[u/PM_ME_BIG_CHUNGI → u/MxClementina]

Any advice for an econ major entering their second year who might just end up in DS? I'm planning on taking lots of math courses and may just be a couple away from a second major in math. Are there some particular math courses that I should take? What can I do to develop my programming skills? (Currently they're non-existent, but we'll do a stats course in the 3rd sem which will involve R, and I'm hoping I'll pick it up from there.)

[u/SamSmitty → u/PM_ME_BIG_CHUNGI]

I major'd in Econ after realized Computer Eng/Sci was more of a hobby than a career I wanted. Just treat it like any other business related degree.

Teach yourself Microsoft Excel at an advanced level with some VBA knowledge. Know how Access works. Be able to write some SQL. Bonus if you learn at least how R and Python work, but honestly not many large corporations will use it for daily stuff.

I'm probably 95% self taught when it comes to Data Analysis, but just taking the effort to do so puts you above 90% of people you will end up working around. It's definitely a field of work smarter not harder when you are getting started from my experience in a large firm.

Still early in my career, but have already moved up 2-3 times in the 5 years I've been here (could have made more by job hopping, but love the company and my boss so that's worth it's weight in gold). Happy to answer any specifics you might have.

[u/MxClementina → u/PM_ME_BIG_CHUNGI]

Linear Algebra! Most people don't know about that one, but one of the most important math fields for modern data science. All about large multi-dimensional array math, which is exactly what big data and machine learning are. Any statistics is great, but focus on Bayesian, as it's a new world of computational power, and classical statistics is falling by the wayside.

[u/SnooDoodles420 → u/Extermos]

Thanks!! I feel wise choosing a Mathematics as my official major now.

[u/BigGayGator → u/SnooDoodles420]

Majored in math and currently interviewing for data analyst/science positions. You've made a wise decision.

[u/SnooDoodles420 → u/BigGayGator]

I wish you the best of luck!!!

[u/GodofTroy → u/r5d400]

Electrical Engineer Sr. trying to get into data science/ business intelligence space

[u/r5d400 → u/GodofTroy]

well hello me of the past lol. theoretically I wasn't quite a senior yet, but I was an EE and was supposed to get promoted soonish when I made the move.

while I have always liked EE, and still do, data science is super fun and the pay is so much better. good luck on your journey!

[u/GodofTroy → u/r5d400]

I meant “Sr.” as in my final semester but thank you!! Good luck.

[u/FckMitch → u/GodofTroy]

Why not try to get jobs in the electric cars industry?

[u/GodofTroy → u/FckMitch]

True but Why not both? There’s many data analyst/engineer roles for auto manufacturers...I work for a semiconductor company that develops eV battery/charging components now.

I do love cars though.

[u/baadakku → u/r5d400]

same here!

[u/rooster7869 → u/r5d400]

Corporate Hero!

[u/nocturnal_shit → u/r5d400]

What do you think about this in future. There are lots of data scientist jobs around here but hardly the actual work of data science. I'm thinking of switching my career towards DS/ML/AI. It is genuinely interesting but since everyone is getting into it, feels like I'm FOMOing in.

[u/r5d400 → u/nocturnal_shit]

it's hard to say, some of the really basic stuff can be automated, but at the same time, more companies are getting better at actually gathering usable data and new opportunities will appear too.

I think it's a good field that it's not going anywhere for the foreseeable future. but the days of getting hired off the street just because you could do 'import numpy' are already gone. tbh they were gone before I entered the field, because I haven't been doing this for very long. so to me that's fine. the job descriptions and reqs will keep changing over time, but that's true of any tech job

with any field, I'd say enter it if you like it and you think you could do a good job at it. we're not about to run out of DS jobs

[u/pacman47 → u/r5d400]

What up!

[u/me_and_the_devil → u/r5d400]

How did you get into this field?

[u/r5d400 → u/me_and_the_devil]

went back to school for a masters. not required for becoming a DS, but I work in research, in which case it definitely helps

[u/naiq6236 → u/r5d400]

QA Engineer doing MS in DS rt. Hoping for a decent jump in pay once done.