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John Howe, Manager Data Science & Analytics

30 Jun 2021

John Howe, Manager Data Science & Analytics is in conversation with Gareth Fleming, Director of Brightwater’s IT division

GF: I’m here today with John Howe. John Howe heads up Data Science & Analytics recruitment for  Brightwater Technology. John, great to have you here today. Thanks for coming along.

JH: Thanks Gareth, appreciate the invite.

GF: Super. Could you tell us a little bit about you and the vertical that you specialise in? How did your career in recruitment start and what’s your specialised vertical?

JH: Right, ok, my recruitment career started about 10 years ago. Well, actually it’s almost 10 years ago now at this stage. I started with Brightwater as well. I started off as a researcher supporting the IT division there, really covering every vertical going, predominantly software engineering and infrastructure. Over those 10 years, I graduated through the ranks, you could say, through consultant, team lead and then to manager where I am now. I specialise in data science, analytics and business intelligence and kind of everything in between there. Wherever there’s a role or a need for somebody with a background or a specialism in data, that’s where I would come in. So, that’s me in a nutshell.

GF: Ok, thanks John

GF: How has the pandemic treated data science, John? What’s happened in your area? What is the data science sector market like now?

JH: Honestly, it’s like it never happened which is a good thing for job seekers. But not such a good thing for employers and I’ll tell you why. Data science, the need for people in data science, analytics and business intelligence hasn’t changed. It’s increased, as a matter of fact, because we know that there has been a  little bit of reticence from candidates to move during a pandemic. They wanted to see how things were going to pan out. There was so much unsurety out there in every aspect of their lives that people were certainly not in a position to make a big decision like moving jobs. It’s understandable. Because of that, it created a pressure on businesses to increase the resources into hiring. The need and the requirement for people in data and data professionals actually increased over the pandemic. We saw more and more jobs come our way and fewer and fewer candidates. So it was definitely very challenging. But that’s good if you’re a candidate, if you’re a data professional because that need hasn’t slowed down.

In many ways, there was almost an artificial need for people but that hasn’t slowed down at all. More importantly, businesses still needed to grow. They needed to, they had so much information, so much data that they needed to leverage. Years ago, data science was always something that you found in Intel, Facebook or Google or a similarly large behemoth. Now you’ve got companies, I had a call with a client only 2 weeks ago, it’s a company of 5 people and they needed data science. It’s a retail company with a huge amount of information and consumer data that they needed to leverage in order to grow their business, in order to grow their profits essentially. It’s no longer an area for large multinationals and global entities. It’s now an area where local businesses can really start looking at these types of hires and making them. That’s really recognised over the last 18 months and with of course, the fact of the pandemic, it’s really pushed the market to new highs. Right now, I would argue that, speaking with my colleagues across technology and just looking at the market and the analysis and trends that we’ve made in Brightwater as well, data science is probably the most buoyant area of recruitment at the moment within technology.

GF: Yes, I think it is, I think it has been, even pre-pandemic, the last three years, it’s probably been the fastest growing vertical within technology. I think companies, both large and small as you’ve said, are making more data-driven decisions. They’ve seen how data can affect the bottom line and data will take an organisation down a very different path, maybe one they didn’t consider. I think that’s why.

GF: What do you think then, will happen moving forward? Let’s imagine, we’re out of restrictions, we’re back to normal. What are the hot areas of data? What’s going to happen in the next 12 months?

JH: Another great question and I could be sitting here for the next hour and a half talking to you about it. But I’ll try and keep it as brief as possible. Of course, you’ve got your more traditional areas of data, business intelligence, your data business analysts, working with the already existing data, the already existing platforms, the infrastructures and just pushing the business forward, leveraging that, visualising the already existing data for the decision makers within the business and the stakeholders. That’s always going to happen and that’s always going to increase, the need for that is always going to increase as data becomes more retained and used and the value of it becomes more and more known.  

But the areas that I see the most growth, certainly within Artificial Intelligence, specifically areas of AI such as machine learning, natural language processing, computer vision as well. All of those sub-fields are becoming more and more interesting to employers. They’re moving away from traditional data analysis and business intelligence and towards data science. That’s where we’ve seen a large increase in requirements from our clients for people specifically with those backgrounds. They want to ultimately automate the process of data in all of its forms essentially. That’s what we’re going to see, well, we’ve already seen that in the last 18 months but in the next 12 months or so, it’s going to be all about those traditional data science fields.

GF: If we bring that back to the market now, I think everyone watching this will agree, it’s very much candidate driven. There’s a lot of jobs out there, there’s a massive shortage of candidates across most areas now in technology and data science in particular. Over the last  18 months to 2 years, we’ve seen salaries increase, we’ve seen almost full employment in technology, we’ve a lack of people coming into the country because of the restrictions. How has the pandemic affected salaries in your area? Again, what do you think will happen moving forward?  How has the pandemic affected salaries in data science?

JH: The pandemic again, for reasons I’ve explained earlier, in terms of, there has been a reluctance in candidates to move, just because of the pandemic. Like a lot of different domains, there is still a retained need for those people from employers. For that reason, salaries have increased quite steadily with definitely a little bit of a spike towards the second half of last year (2020) as it became clear that this isn’t a 6 month pandemic, this is a year to a year and a half pandemic. That realisation hit everybody but including employers and everybody went after the few, the little talent that was in the market at the time. In order to attract those people, it came down to money like it always does. There are a lot of other factors involved of course but ultimately it does come down to money at the end of the day. They (salaries) have definitel y risen even taking into account the inflation as well, I’d say between 10 and 15% over the last 18 months, across all areas of data, especially data science. And it’s not slowing down. We’re seeing candidates being offered 20% sometimes 25% on their base salary, not taking into account any of the other benefits or bonuses etc. That’s not unusual, it is definitely the exception to the rule but it’s not unusual. It’s becoming increasingly more expected out there.

Salary is always going to be a major factor for any job seeker and it always will be. There are definitely other factors as well. Interesting projects, being probably top of the line. Location used to be number one even above salary but that’s a whole different conversation, I think. So more interesting projects, you’re dealing with people who are naturally analytical, like to be challenged, don’t want to be sitting around at a computer doing the same thing day in, day out. They want variability and variety so those projects are going to be absolutely key. If you’re a company or an employer who has really interesting projects, leveraging the newest technologies, the newest tools, the newest whatever it may be, that’s going to be a real unique selling point as well. So all of those things, whilst they are important, salary still remains top.

GF: And have we seen like in other sectors that difference that used to be there between Dublin and regional salaries disappear? Is that a thing of the past now? Is there still a difference between Dublin and regional salaries?

JH: No, I think it’s still too soon to be saying it’s a thing of the past. I do think it’s something on everybody’s mind at the moment. There’s plenty of people that I’ve spoken to, even only yesterday  I spoke to a client and both of them, 2 people, one was a Product Manager and one was an Engineering Manager in software engineering. They had both worked out of their Dublin office, their Dublin city centre office, where they lived not too far away and they’re both living in remote villages in Longford and Mayo now. Why not? Rent in Dublin is extortionate. We are seeing that question now, that difference between regional and Dublin salaries. We are seeing that blur and it’s going to get to the point where employers are going to look at that. They already are in the US. Companies like Facebook, Google and other companies, specifically Facebook who were in the news earlier in the week, or last week, where they allow their employees, their full-time permanent employees to work in any location within the country for tax regions but they will pay them accordingly based on where they’re living. Now that’s a Pandora’s Box of ethics and conversations that I think is going to take quite a while to iron out.

With all of that, as people realise they can work from home full time or 1 /2 days a week, they’re going to move somewhere that makes more commercial sense for them to move. I think everybody is potentially thinking about that. So, I think over time, it will become more blurred and Dublin and regional salaries will, definitely, it will be less of a question of the difference between Dublin and regional and more of a question of “how much are you paying if I live in Dublin and how much are you paying if I live in Leinster? Or how much are you paying if I live in Munster or Connacht?” There’s a lot more conversations to be had around that.



GF: So we know it’s an in-demand area. We know there’s a big candidate shortage. And we know that there’s more and more people going into technology driven courses in college. What does the landscape look like at the moment in your area for graduates? What does the data science jobs market look like for graduates?

JH: Again, it depends in which area. I focus on data roles. That could be your traditional analysts, business intelligence and technical BAs. Those types of roles have been around for quite a while. Those types of roles, you can come from any type of under graduate degree, you can come from marketing, physics of course, you can come from software engineering, various different types of arts degrees, it doesn’t really matter. You can come from many different areas because the tools that you use are probably like SQL, or VBA, Excel, visualisation tools like Tableau, PowerBI. You don’t need a software engineering degree or a quantitative degree to be able to learn those pretty quick on the job. Those jobs have been around quite a while and there’s always going to be availability for any sort of graduate to move into those jobs. Where it really matters right now and in terms of people who really want to move into data science degrees, there’s very few undergraduate or Masters degrees in Data Science at the moment, very few of them. A lot of the people who move into those areas tend to have very quantitative undergraduate degrees and most like quantitative post-graduate degrees, sometimes even PhD’s depending on the work that they’re doing. They’re usually coming from physics, mathematics to a lesser extent and computer science degrees predominantly and to an even lesser extent, engineering as well. So the areas that they move into, they’re quite new. Artificial Intelligence is quite new in the general scheme of things if you think about it. AI has been talked about for 20 years now, 30, has it been longer? But really, has it been worked on as a job? Relatively new, maybe 10 years properly so graduates being able to go into those areas is even a newer thing.

Because it’s only recently that universities have caught up with the requirements of the market and they’re actually teaching algorithms around NLP or ML or CV and all of these types of areas. It’s only recently that graduates were really tooled with the ability to move into these areas. So to answer your question, I do see that people coming out with quantitative undergraduate degrees and specialised post graduate degrees in data analytics utilising programming tools like Python and what I’ve learned about the concept of Machine Learning and neural networks etc, they’re going to walk into jobs. Absolutely, we’re already seeing it and the salaries are pretty commensurate with it compared to other tech graduate or entry level jobs. You’re looking at starting off at €40k. So if you’ve got the right educational background and you’ve done the right degree (or degrees) you’re going to walk into a job pretty quickly in Ireland.

GF: yes, I think for any graduate from that type of background, it’s a great place to be because it’s so applicable across some many different backgrounds, from hedge funds through the banks, the pharma, manufacturing and supply chain. It’s all encompassing now as to where you can end up, a bit like software engineering. Thanks for that, John. 

GF: The skills that are in demand, if you look at the areas, what are the most busy areas that you have right now, what are the skills that everyone really wants right now in data science?

JH: Let’s just talk about data science because it’s quite broad, it’s quite variable once you start talking about analysis and BI. So just on data science, everyone uses Python, even if it’s not part of the tech stack, you’ll be using Python in data science, it’s kind of a shell level scripting language, you need it at the fundamental level. You don’t really need any of the object orientated compiler languages at all. Java is gone, C is gone so really it’s just Python. Because it’s a data science role, highly quantitative, almost everybody who’s currently either a leader or the decision maker or a senior person within data science has come from a very academic background or quantitative background, they would have used R, and because of that, they’ve brought that experience with them into the market. The mature market now, most companies use R for statistical analysis so there’s definitely that as well. And of course, you can’t avoid VBA, Excel is not going anywhere anytime soon. Microsoft saw to that. Then of course, you’ve got SQL as well on a simple kind of database level. So they’re the kind of core programming components. Then of course, you need to understand the concepts around Machine Learning, Neural Networks as well, Natural Language Processing (NLP), linking into computer vision is an area that’s really big in demand as well.

Again this is on the automating stuff, no-one wants to be sitting there running tasks and running scripts in order to get really the best use out of data. They want to automate it, look at things and extract information and useful information from data on an automated level. That’s why you need to learn all of those things. Just elsewhere, Pandas are really important. Pretty much every company who’s recruiting for data science at the moment requires somebody who has got some sort of insight into Pandas – Intenso Flow is the big one, they’re actually doing certifications at the moment which are online and free. I’ve found that the response from a lot of the hiring managers who are recruiting in the data science space, really warm to candidates who have bothered to do that free online Intenso Flow certification. It’s a weekend thing. Even at graduate level, those candidates stand apart. So this is a tip for any graduates who are looking to get into any data science type role. Then PyCharm as well is another one, cloud platforms like AWS of course, less so Azure. They’re the kind of predominant tech. There’s going to be lots of tech floating around that as well. That’s more on the technical side of things.

What we’ve found as well are the softer skills. With the pandemic, it’s probably no surprise that communication skills and collaboration skills are more highly valued than they might have previously been. No longer do you have your manager or a senior member of your team there to throw an eye over to make sure you’re going the right way and you’re not wasting an hour by going in the wrong direction. It’s now, unusually so, on the worker themselves to be able to effectively communicate and to update, make sure they’re managing their own work correctly because it’s all virtual now. That’s really important now for employers. We’re seeing it increasingly on top of the list of softer skills in job descriptions and it’s been articulated to us when we’re having those introductions to clients. Then of course, it’s a strong work ethic, to be able to self-motivate in a tough virtual scenario and then of course, because it’s a data role and this applies to all data professionals, a commercial mindset. To understand the value of what you’re doing to the business, that’s pretty important too.

GF: I think those softer skills, that’s been, for every client that I’ve interviewed, every person in Brightwater that I’ve talked to, that is a common theme. Those softer skills have really come to the fore now. The current batch of graduates currently have that softer better communication skillset instilled in them now because they’ve had to operate in that way. It’s the one little silver lining out of all of this is that those graduates, if they get back on campus and hopefully they do, some of them are right now, they’re going to have that skillset which will really help them when they hit the working world.

JH: Yes, that’s a really good point actually.   

GF: Last question for you. You’re a great man for sharing, I have to say, some brilliant articles on LinkedIn. I think it’s great because it shows you’ve got a real interest in what you do. It’s not just a job, you’re actually really interested in data. You’ve shared some really interesting stuff over the last few months. What’s been your favourite data story over the last six months?

JH: God, that’s a tough question because I’ve liked them all for different reasons. I posted one about using Machine Learning to ultimately teach computers how to break down and process the clicks and sounds that whales make which is fantastic. I’ve got a particular interest in biology so that will be interesting to see how that works out. But of course, it does have to be the Kevin De Bruyne story. I’m a huge football fan, not a Manchester City fan, I will admit but I am a huge football fan, a huge fan of Kevin De Bruyne as well. I just loved the fact that he was not just smart enough but forward thinking enough and surrounded by the right type of people to advise him, ie. his brother and his dad who identified the fact, “look, we’ve got a huge amount of data supplied by your employer and you can use that to effect the best possible decision for you, for the future of your career over the next 10 years”. That’s never been done before so I think definitely, I don’t know about the history of sports but definitely not in soccer so I found that really, really interesting. It crossed over my natural interest in data with my interest in football as well.

GF: That was your most popular article as well, I think.

JH: It was, about 300k

GF: It was and counting. It reminds me of one of my favourite films, sports films but also a brilliant data film, Moneyball.

JH: Loved it, absolutely loved it.

GF:  Great movie, John, all about data, all about data science. John, it’s been great talking to you. It’s a really busy sector. I know you’re really busy at the moment. I’d recommend any client, any contact, any candidate looking to  talk about the data science market, to talk to John. We’ll post John’s details on this recording, you can get in touch. John, thanks a million for taking the time out today.

JH: Great talking to you as always, Gareth.

GF: Thanks John.

John Howe Manager was in conversation with Gareth Fleming, Director of Brightwater’s IT division. If you wish to have a confidential conversation with John about the data science sector, please contact him on [email protected] or connect with him on LinkedIn