I Analyzed 2,244 Data Scientist Job Postings to Find Out What Skills You REALLY Need to Get Hired

when I was first starting out in data science I had this note on my phone where I would write down all the skills I needed to learn every time I found out about some new tool or method I was working really hard to study everything on the list but the list only kept growing which made me feel super stressed out that I'd never be able to learn enough to get a job to make matters worse Looking Back Now I wasted a ton of time learning skills that haven't come up at all in the six years

that I've been working in the field so to make sure this doesn't happen to you I analyzed 2,244 data science job postings to figure out what skills employers are actually looking for in 2025 in this video we're breaking it all down first I'll reveal the core skills that come up over and over again in job postings then we'll get into how these skills shift depending on job level from internships to Executive roles and don't worry we're going to tackle the big question that's on everyone's mind right now do you actually need generative AI skills or

is it all just hype by the end of this video you'll know exactly where to focus your energy to land a job in data science whether you're just starting out or looking to level up let's get into it before we dive into the findings let me tell you a little bit about the data we're working with I scraped 2,24 four data science job postings from LinkedIn in December of 2024 and then I used an llm to extract the primary skills from the postings the job span 1,89 different companies across 495 unique English-speaking locations worldwide but

they're mostly in North America and Europe in terms of seniority we're looking at jobs across the entire career Spectrum from internships to Executive positions so we can understand how skill requirements evolve as you progress in your career let's start with the big picture across all levels in geographies what skills do employers actually want first up python it's not even close python appeared in 1,920 job postings which is over 85% of all the listings in my opinion this kind of settles whatever remains of the R versus python debate while 714 jobs did mention R only 12

mentioned only R and not python so unless you're going for some super Niche academic role I would just learn Python and pick up R later if you need it in your career the next most in demand skills might surprise you while machine learning comes in second at 1,527 mentions communication skills are right up there in third place with 1,435 mentions nearly tied with Statistics this completely aligns with what I'm seeing as a career coach and On hiring panels at Amazon the people who succeed have strong technical skills of course but it's almost more important that

they're strong communicators who can express themselves well and explain their work clearly to a variety of audiences the next tier of skills is interesting too we see data science itself with 1,163 mentions data analysis a little under that and data visualization a little under that all clustered very closely together this tells us something important and players are still looking for those kind of classic data science skills with more of an analytical bent so it's not all about building models moving back to the tech stack seal came in with 1,088 mentions showing up in about half

of postings this isn't surprising given how fundamental database skills are to data science work after that we see a bucket for soft skills this is where I kind of bucketed all the more specific social skills like taking initiative attention to detail empathy adaptability these show up in over 800 postings and that's not even counting the communication skills we talked about and right after that we have problem solving this all goes to show that while your technical skills are super important alone they're not enough to get you a job after these core skills we see a

mix of specific tools and applications like NLP Big Data Tableau and AWS one question I get asked relatively frequently in my coaching work is which cloud provider to focus on I've always defaulted to AWS since it's the largest in terms of market share and that looks like it's born out in the data as well AWS is mentioned in 486 postings while Azure is next at 326 and gcp is way down at 248 so if you're new and trying to learn some Cloud skills just stick with AWS for now another thing that stood out in the

data is the positioning of java while it appears in 379 postings which is a lot more than I expected it's important to note that it's almost always listed alongside python not as an alternative looking at some of the postings it seems like Java was just adding to kind of a general list of programming languages that might be relevant but it wasn't listed as the primary language that they're seeking so I think the takeaway here is not to get too hung up on it if you don't know Java already start with python and learn from there

in the future if it's needed for your role now here's something that might surprise you with all the hype around I you'd think new technologies would show up at the top of the list but General AI skills show up in just 447 postings with deep learning at 401 and llms even lower at 290 so while these are valuable and emerging areas they're not yet the primary focus for most data science roles we'll go into way more detail on this topic like which gen skills you should be prioritizing at the end of the video lastly something

that stood out in the data is the inclusion of very specific skills like Docker Jenkins and Spark though those are near the bottom of the list in terms of frequency at most they're around 10 to 15 15% of postings now when I was just starting out I got really hung up on trying to learn everything including every new tool thinking these were kind of mus haves to get a job but in reality these are either more relevant for specialized roles or something that you can learn on the job so if you're just starting out you

don't need to overwhelm Yourself by learning all of these at once instead focus on the core skills so python SQL machine learning and statistics and pick up tools like Docker or Jenkins when your role requires them let's dive into how skill requirements evolve across different career stages what's interesting is that while some core skills remain consistent there are distinct patterns at each level that can kind of help to guide your career development looking at the numbers first I analyzed 112 internships 604 entry-level positions 123 associate roles 1,56 mid- senior level 20 director roles and five

executive the rest were Mark not applicable this distribution tells us something important the bulk of opportunities are at the mid senior level suggesting that while breaking in might be challenging there's significant room for growth once you're in the field let's start with internships interestingly machine learning tops the list here even ahead of python appearing in almost 90% of internship postings this is followed by data analysis and SQL suggesting that companies want interns who can handle core analytical tasks as well what's notable is that soft skills and problem solving also make the top 10 indicating that

even at the internship level technical skills alone are not enough for entry level positions we see a little shift so python takes the lead but data visualization jumps to second place this is a significant change from what we see at other levels but this makes sense because Junior data scientists often need to analyze data or communicate findings effectively before they take on more complex modeling work Java and software engineering skills also appear more prominently suggesting that many entry-level roles have a software engineering component looking at this data I wouldn't be surprised if what we're seeing

is more of a specialization split in entry-level roles with some more engineering focused and some more on the analytical side of things the associate level starts looking like your standard data science role Python and machine learning claim the top spots with Statistics and communication close behind what's notable here is that data visualization dropped significantly in priority compared to entry-level positions the focus appears to shift more towards technical work mid- senior level is about the same as associate which makes sense given that the boundaries between what makes someone associate versus mid- senior level are kind of

like fuzzy now we're looking at director level positions but keep in mind this data set is super small looking at the data the core data science skills remain but with the inclusion of leadership and more specialized technical skills like AI NLP big data and deep learning while we might expect management skills to dominate at this level the data show that technical expertise remains crucial even for higher ups in the field at the executive level we have just five postings but we see a little bit of a shift in priorities here as well AI takes Center

Stage appearing in four out of five postings Python and machine learning follow closely but interestingly we see equal emphasis on crucial leadership elements like Team Management client management mentoring and problem solving this suggests that executive rules require a balanced mix of AI strategic Vision technical literacy and Leadership capabilities but of course it's five positions so take it with a grand assault overall the key takeaways from this seniority progression are maybe a little bit different from what you might expect entry level roles heavily emphasize Python and data visualization suggesting companies want people who can start delivering

insights really quickly as you move to associate and mid- Senior levels machine learning and statistics gain prominence while visualization becomes less critical this reflects a shift towards more complex technical work at the director level surprisingly machine learning and python still lead the requirements showing that technical credibility remains crucial even in leadership positions there's also more emphasis on specialization at this level as well finally the executive level brings a clear pivot AI strategy kind of takes precedence though maintaining technical literacy remains important leadership skills also share equal footing with technical knowledge at this level this is

quite different from many traditional career paths where technical skills become less relevant as you advance in data science it seems you don't leave the technical work behind but rather folks tend to specialize and add layers of strategic and Leadership skills on top of the technical Foundation now let's talk about generative AI with all the hype around Aid driven applications and llms you might think that every data science job requires gen skills but the data tell a different story looking at our overall numbers only about a third of data science jobs mention any AI skills at

all and the numbers for specific AI skills are of course even lower General AI skills show up in about 20% of jobs llms in 12% and generative AI specifically in 13% more specialized skills like Transformers and prompt engineering are barely mentioned at all also even when companies do mention AI skills they're usually listed after traditional fundamentals like python statistics and machine learning this suggests that while gen is becoming important it's seen as an additional skill rather than a core requirement so while AI skills are clearly important and growing the data suggests that you shouldn't Panic

about learning every new tool that comes out instead the job market is telling us that strong fundamentals are still what matters most that said I did see kind of an interesting Trend in job titles we're starting to see roles specifically focused on gen like gen data scientist these specialized positions might become more common in the future but for now they're still a small part of the overall Market the bottom line is that yes learning about gen is valuable and it might give you an edge in your job search but don't let hype distract you from

mastering fundamentals companies are still primarily looking for data scientists who can write good code understand statistics and build solid machine learning models with more traditional methods so what have we learned from analyzing over 2,000 data science job postings three main takeaways first fundamentals matter most despite all the new technologies and buzzwords companies are still primarily looking for people who know python statistics and machine learning these core skills remain consistent whether you're applying for an internship or director position second soft skills matter a lot communication and problem solving in particular but taking initiative collaboration and creativity

are also critical this is a reminder not to skimp on practice ractice time for Behavioral interviews third while emerging Technologies like gen are important they're not replacing traditional skills they're just adding to them so don't let hype distract you from building a solid foundation think of new technologies as tools you can add to your toolkit not complete career redirections so that's it for the skills needed to be a data scientist in 2025 my next video will be all about the skills for machine learning Engineers so make sure to subscribe if that's something you'd like to

see also if you want help breaking into data science I have a comprehensive road map on how to go from a complete beginner to your first data scientist job in one year here it's available as a free 880 page ebook at the link in the description or an hourlong video up next see you next time