Today on the FiveMinuteFriday episode of Super Data Science Podcast, we talk about the Data Science Gap – a current phenomenon that every employer and data scientist should be aware of. Make sure to take mental notes as you listen because I am also giving 6 useful tips for job seekers on how to fill the Data Science GAP!
Here’s a scenario: you’ve been on your feet looking for a job where your data science skills could be valuable. But, as soon as you run your eyes on a job post, you stop – you stop because of the ridiculous amount of years of experience they’re requiring. 10 years of expert data science experience? Even though data science hasn’t been here since a few years? What a stretch!
If you’ve been a student in one of my classes or an avid follower of the podcast, then you’ve heard of the term ‘Data Science Gap’. Well, that’s what’s happening in the scenario above.
Just to refresh, Data Science Gap is a phenomenon in the data science marketplace where, though the demands for data science skills are high, there is still a scarce supply of ‘qualified’ candidates for the job positions available.
Positions in the data science field take 5 days longer to be filled compared to positions in other field which on average only take 40 days in US. The gap is apparent. Job seekers applying are not ‘qualified’ enough because the recruiters and HR managers are not yet familiar of the ins and outs of this field.
They’re looking for the data science unicorn – a person that doesn’t really exist. It’s time for them to understand what data science skills are really needed for their industry to achieve their goals.
For job seekers out there, you’ve got to do your part also to bridge the gap. Time to show the companies that you are the right person for that position. Here are six ways to fill the data science gap:
- Understand the field. Get the domain knowledge where you’re going to. Understand the potential problems, types of customers, logistics, assets, etc. Show them you’ve got the upper hand.
- Take a course. Keep that education going. Keep learning!
- Get a mentor. There’s a big big difference that could happen in your personal, business, and career life if you have a mentor by your side. They guide you and challenge you to be the best you can be.
- Read the news. NOT EVERYTHING. Yes, only the things that could forward your career. Filter the things that could only be useful.
- Apply to the right company. A company that could see you as a valuable team player and a company that could help you catapult your career.
- Network. Statistics: 70% of jobs are filled behind the scenes – filled through connections and referrals. Instead of solely looking at job postings, go to tech events to meet new people, reconnect with colleagues, follow mentors and co-professionals in LinkedIn and contact them.
Items mentioned in this podcast:
- Artificial Intelligence A-Z™: Learn How To Build An AI
- SDS 150: Have a Mentor
- The Data Science Gap by Kirill Eremenko | Medium
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- Music Credit: Canary by Jim Yosef [NCS Release]
Kirill Eremenko: This is Five Minute Friday Episode 188, The Data Science Gap. Welcome back to the Super Data Science podcast, ladies and gentlemen. Today we got a very exciting topic to cover off a very intriguing topic which is called data science gap. This is a term that we came up with at Super Data Science for a phenomenon which we're observing in the data science marketplace. What is this all about?
If you look at the data science job market then you will find that on one hand you've got a booming requirement for jobs. The number of jobs is constantly increasing. There's a huge demand for data science skills for machine learning experts, for artificial intelligence experts. for data scientists across the board to join companies and drive their operations and their businesses in general. We have data, [power 00:01:02] of data and that is a great representation of the fact that the number of ... the amount of data is growing exponentially in the world and businesses due to competitive pressure are more and more looking into leveraging their own data, leveraging this asset that they have that they haven't been looking at, that they haven't been seeing, that they haven't been leveraging, haven't being utilizing.
Now they're looking at leveraging it to further to get that extra efficiency, to get that extra margin, to get that extra competitive edge to service their customers better and so on. That's on the side of demand. It definitely is there. On the other hand, in terms of supply what's going on? A couple of years ago it wasn't there. Data science was so new, businesses wanted data scientists but the data scientists weren't there. But now what we're seeing is that there is lots of data scientists flooding the market. There's people who've taken education in the space who would now even have experience. They're posting their profiles, they're posting their ... they're applying for these jobs so the supply is also there.
But at the same time, the data science jobs are still very hard to fill. Even though there's supply and demand, the supply it's a very booming topi. You would expect that these jobs would get filled very quickly. In reality, data science jobs are actually open for 45 days on average in the US which is five days longer than the national average of 40 days. Normally jobs are only open for about 40 days on average, in this space data science is 45 days.
Why is this happening? Why is it so hard to fill these data science jobs even though there is supply and there is demand or demand and supply? This phenomenon is what we are calling the data science gap. This gap actually comes from a number of reasons, from the fact that on one hand the supply that we have for data science, people who are still applying for these jobs, who are submitting their resumes aren't necessarily skilled enough experienced enough in the space of data science to fill those jobs.
On the other hand, what we are seeing is actually most of the reason for this gap is actually coming from the demand side, from the fact that recruiters and HR managers are not yet used to this whole new topic of data science. They're not very familiar with this playing field and when they come up with these job descriptions sometimes they're using templates from other areas, other kind of similar areas which don't really work for data science. For instance, a job description which requires eight years of experience in data science is quite absurd given that the field has barely been around for that long, not even that long. How can somebody have 8 or 10 or 15 years of experience in data science if the field hasn't been around long enough for that to happen.
Examples like that or when you see a job description where somebody is required to have Python, [inaudible 00:04:18], in Java and Math lab and all these different skills and all these different industry knowledge and experience, all these different industry and all the experience, again, some of these job descriptions are just very forced and looking for extremely rare qualities in candidates or essentially that's where the term unicorns. Data science unicorns comes from that some of the job description are looking for people which cannot physically exist because the requirements are so stringent.
Where they all comes from is the fact that the field is so young, the field is so ... even while yet it's so powerful, it is so young that recruiters and HR managers just don't know how to describe what exactly they need. They cannot ... it's really hard for them to come up with the description or like overview of what exactly are they looking for. Many just don't even understand the field well enough to understand who they are looking. That is the data science gap.
One of our missions at Super Data Science is to bridge that gap, to help on one hand organizations create better job descriptions and understand better who exactly do they need. They don't really need a unicorn, they maybe need three of these types of data scientists. Or maybe they need one data scientist with this experience, this knowledge and maybe four of these data scientists and how to build that team and things. On the other hand, we want to help you, our listeners, our students, people who we want to ultimately take care of, we want to help you bridge that gap on your side to better your show companies that you are the right fit for their role.
Today we're going to go through six tips that we've come up with that will help you bridge the gap from your side. Here we go, let's dive straight into them. Tip number one is understand the field. Try to really get the domain knowledge of the company that you are applying for, for that business. Maybe you're not applying for a specific country, you might be interested in a certain industry, or might be healthcare, or might be a logistics or transportation, or it might be mining, it might be finance and banking. Understand the specifics of that industry and this is what we call domain knowledge. Understand potential problems, challenges, types of customers, partners, resources, logistics, assets, liabilities, all these different things that go into business operations and how businesses work in that space. Understand the field really, really well. Understand what are the competitors of the company that you're interested in, how are they doing this, what's the differences.
Once you have this knowledge, ideally you even maybe do some projects, practical assignments or get some practical hands-on experience through case studies, for instance. For example at Super Data Science in the platform, we actually have case studies which we run, or in some of our courses we have case studies where we talk about industry-specific case to help you not only understand the algorithms and techniques that you can use, but also get some of that hands-on domain knowledge and get the practical skills in that space. That's number one, and because once you have that, all of a sudden you are much more lucrative or much more interesting candidate for the recruiters and HR managers because you already know their field, you've already demonstrated the interest and capacity to understand their field.
Number two, take a course. Of course if you're listening to this podcast, chances are that you've already taken one or even several of our courses, keep that education going, keep learning, keep looking and because the data science field moves very quickly, you want to be on top of things. At Super Data Science, for instance, we're releasing courses as these technologies come up. For example, in February this year a new artificial intelligence technology came out, augmented random search or ARS. By I think it was March or April, we already released the course on ARS because we want you to be on the cutting edge of technology, we want you to be knowledgeable about all of these things that are happening because who's going to ultimately get hired? The people who stand out and the people who stand out are for instance those people who are always learning the very musings, the most advanced techniques that are going to help businesses. Ultimately, you don't have to be implementing them right away in a business but even knowing them shows that you are a person who is interested and was vested into this field.
In that ocean of supply of data scientists that have flooded the market in recently, which isn't bad, which is a great thing, but you want to stand out. One of the ways to stand out is to know the most recent skill or recent tools or deepen your knowledge in the tools that you're really passionate about, that you really want to produce.
Number three, get a mentor. I cannot stress enough how much of a difference in my life mentors have made. I've had mentors in the space of data science, in space of business. For my personal life, I've had mentors for understanding how to bring efficiency and optimize the way I structure my day and things. It's extremely important to have a mentor who will guide you in some of these career choices or challenge you on some of these things that you might be delving into a certain field or some industry or certain topic, and they might challenge you why are you doing that? Is this most efficient way to go about your career? What else can you do?
They might open up doors for you, they might open up your eyes on certain things. Extremely, extremely important, find somebody. We had a podcast, we had a Five Minute Friday episode on the importance of mentors so I've shared some tips. I think I shared four or five step process on how to find a mentor and develop that mentor-mentee relationship. Check that out, we'll include the link to that episode in the show notes. Actually, I'll actually put up the right dial. Check that out because you definitely need a mentor, somebody who will guide you. It doesn't matter where you are. It was episode #150, it's called Have A Mentor. Check that out because once again a mentor will be helpful and will transform your life in ways that you can't even imagine right now.
Number four, read the news. I'm a personal ... like my personal belief is don't read the news, it's very controversial. Don't read all of the news. I don't check the news, I don't watch TV, I don't check any kind of news outlets and things because I don't want to ... I want to filter what I get, what information I get, but check relevant news. For instance, a great blog to follow, there's a couple of them. A great blog that I read at least once a week or I try to read once a week, and it actually gets delivered to my inbox it's called The Abundance Insider by Peter Diamandis. If you go to, I think it's abundanceinsider.com or if you Google abundance insider you can subscribe to that, it's absolutely free. Once a week you will get news on all the technological updates, like a very distilled newsletter like email of all of the top, top, top things. It takes me less than 10 minutes to read through them, maybe five or seven minutes and click the links that I'm interested in.
Something like that, find something where you will get your information but don't let it be information, as Tony Robbins says, don't let it be information that you click bait and information is being force on you and that's marketed to. Make it information that is actually, that you've selected by choice. Make a conscious selection of what you're going to be reading and why and what's it going to about, how is it going to help your career.
Five, apply to the right company. Make sure that you ... when you're like ... a lot of time people make this mistake, they're applying to companies that their open positions they'll apply to everything. There's a cool company and they just want to apply to that right away. You need to understand how is that going to, how is that position actually to enhance your career and what is it going to do for you. It's not like, especially when you go to an interview it's not about just them understanding if you're the right candidate for them, it's also about you understanding if they're the right company for you. This is ... at the end of the day, this is your time that you are investing, this is the most valuable resource that you have, the most valuable commodity that we all have, something that we cannot restore. You're investing your time into them or you will be investing you time into them when you're working with them so you want make sure it's the right choice. Make sure you ... like less is more.
Rather than applying to a hundred positions, pick the two or three that really matter to you, that you really believe in, and you can relate to their missions and visions and values. Do your research on those and really prove to them that you care and that you want to work there. That's the way to get a really great role in a company in data science, doing data science.
Number six, network. This is probably by far the most important tip. You probably have heard the statistic. We've mentioned or I've mentioned a couple times in the podcast that about 70% of jobs are filled behind the scenes, like 70% of the jobs are filled never advertised, it's never a job position posted online that you can apply to, none of that. You only see, all these jobs that you see that's like 30% of the job positions. The reality is that most of the jobs are filled behind the scenes through connections, through somebody knowing somebody, recommending somebody, somebody referring somebody, internally they post the job description things like that.
That is where you need to be. You need to network, you need to connect with people, you need to talk to them and be in the middle that because that's how jobs will come to you. For instance, if you've been listening to the podcast, at the very end I always ask our guests, is it okay like, how can I'll just connect with you and share a couple of ways, Twitter, Facebook and so on. I always ask them is it okay to connect on LinkedIn if they don't actually mention it themselves.
A good starting point is connect with all our guest on LinkedIn and maybe message the ones that you're actually ... you are interested in their podcast and you really listen to it and you want to learn more and you want to have something to say. That's a great way to establish these connections and build these networks. There's other ways of course, you can go to meetups, go to conferences, go to events, go to ... connect with a group of colleagues or go to meet like places where data scientists gather, hackathons or things, and really, really network and get in that space.
For instance, next week you'll hear from our guest, Randy Lau, who built a huge successful career in data science. Within a year, within one year, he built a career and through networking, he has gone in two jobs. Just over the passed year, he's gotten to ... he's got multiple job offers but now he's got two jobs that he is working in in data science just through networking, just through building connections in the space of data science and mostly this was done therapy LinkedIn. Look out for that podcast, that will be next week and you'll learn exactly the steps that you need, the exact steps that he took to build a career in one year. That is probably by far the most important step that you can take, number six, to network.
There we go. That's the data science gap, and that's what you can do about filling it in from your side and standing out from the crowd. Don't worry. The bottom line is the demand for data science is definitely there, you just need to do everything you can to stand out from the crowd and be the data scientist that everybody else wants, the company's want, and we'll do our part on helping educate businesses on how to better write those job descriptions and how to better find them. Hopefully together we can all bridge this data science gap so everybody is happy in the end. On that note, I hope you enjoyed today's podcast. I look forward to you back here next time. Until then, happy analyzing.