Is it too late for me to move into Data Science?
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By Jose Quesada
Hi, this is Jose Quesada for Data Science Retreat. I want to cover a topic today that I find very interesting. Many people come to DSR, thinking that:
They are too late…
They could have started earlier, and now they are late in their careers…
How are they going to make this happen…
They really want to be data scientists, but they think that they are maybe late at their late 30s or maybe even 40s..
So, I’m here to tell you that no, I don’t think it’s too late for you. I’m going to give you some reasons and maybe examples too. I have made a video on this topic for those of you who prefer watching a video than reading, see below.
So, the first thing that you need to understand is that data science is totally a lateral move for everybody. It’s not like there is a career path that you go to university and get some kind of certificate (certificates in general of even master programs, I don’t see them working in the market).
I’ve interviewed many people with master’s in data science no, they didn’t pass the interview, and looking at the curriculums in their programs, they’re not going to do any better on their own, so that’s good news for you. That’s a level playing field.
If you’re coming from a different industry, so what? Everybody is doing the same. So that’s an advantage, and that comes with the territory when you are experienced. You have already produced something in your life that is valuable to somebody.
So good news for you! You’re an experienced person you may have a leg up just because of your experience, so you may say yeah that is true, I agree but other people who are younger than me have advantages over me.
Let me tell you one more thing that I think could benefit you, which is your network. What happens when you are new in any task, you don’t know people in that particular domain then everything becomes much harder. But when you’ve been around in one industry, it doesn’t have to be data science you magically have other people that you can ask things, and every person in your network is
a potential source of problems, which is very important. Everything in the life of a data scientist is to find a good problem, where you can provide a solution with this new technology, which is data science.
How do you do that? It’s much harder with your experience when you haven’t seen much in the industry.
I’m sure that I cannot produce problems to say the golf industry because I’ve never played golf. But if you have 30 years of golf experience, then I’m sure you exactly know what a good solution for a problem that exists in golf is.
Let’s say that you look at style transfer or keypoint detection like the skeletons for your pose like dance pose, and you say wow, this would be fantastic to learn and doing something with golf. You come up with that idea, and it may be a great idea, I cannot because Idon’t have any experience there and I don’t have a network either.
So the same moment you have the idea you can call three people and tell them the idea and people will be like ‘Oh my god I will definitely use that if you create a system so I can learn the x,x,y golf moves they do because I don’t even know the names.’
They are going to love you, and you can test that because you have a network. More so, you can go and sell your things to that network before you build them, which is the best validation possible. If this idea resonates with people and they tell you Yes, I would definitely use it and you tell them okay how about 30
bucks per month, and they say No. Then maybe the idea is not that valuable, maybe it’s not so powerful for them to solve this problem.
But you have a network, and you can use it to validate and fine-tune problems. This network is also going to be valuable to the company that hires you as a data scientist. You may not expect that, but imagine that you’re a medical doctor, and you are actually learning machine learning on the side, and you are
becoming good at this. So, you apply for a job at a company that is doing something with x-rays, and you are 40. Do you think that they’re going to look at you and say well this guy is 40, We don’t want this type of profiles? No, they’re going to look at the medical doctors, and they are in the domain of x-rays, so you are going to see things that they don’t see because they don’t have domainknowledge, but you do.
This is fantastic; you have domain knowledge, it’s going to open doors for you. So, make sure that both your network and your domain knowledge is valuable to the company that you’re joining, and you may say but Jose all companies that I’ve worked for in the past they were boring, and I didn’t learn anything there.
That’s not true you probably know more things than anybody else in a room that is trying to hire and this is a really good match because vertical AI companies are really really valuable when they have domain expertise domain knowledge that nobody else has, and it’s the mixture is really hard to find then so much more valuable the companies
So remember network domain expertise, and you must have some management experience because if you’ve been around for a while, you may go well, I actually have only three people in my team, but I was the most senior of them so they looked up to me when we had to make decisions or maybe I could interface better with the decision-makers because I was kind of in the same age range or I knew them for a long time. This is valuable definitely play that up in your LinkedIn in your applications and describe how you solve problems because you had management experience. When you are interviewed, remember not all the interviews are super deeply technical some are really like oh this is not going to fit with your culture.
Also, you must have some experience period, not just management experience, but you must have done something that the left trace does.
So let’s say that you are an athlete, you were an athlete and you were swimming competitively for many years or even something more radical you were a martial artist and you may say well that experience is completely worthless to anybody trying to hire me as a data scientist. Well, No, you have experience of how it is to really try hard at getting something and failing multiple times. You have the experience of training and improving and measuring your performance. You have experience of trying different things and finding one thing that works better than the others and doubling down that. This is totally transferable to the things that you want to do as a data scientist. You may go in a direction that is something related to AB testing or bandits. That’s exactly the same use case that I have described.
But there are disadvantages to being experienced or being older. What are those?
You are more expensive than others. There’s no way around. You’re not going to get paid the same as a fresh out of university person. You may not be able to want to pull long week hours like 60 or 80 or
typical startup business hours. That is personal, but maybe you can do as much with fewer hours. That’s the issue of culture, so if anybody in the company is really of a particular mindset, being the elder outsider may not help you, and maybe this is the biggest showstopper.
You cannot really do much about that if you have been trained if you’ve been working for years in big corporations, maybe that you don’t fit straight in at a sloppy culture where things happen very fast, and people make jokes about things that you may not even understand. That is a problem, and there is no way around it unless you spend months really trying to enter that culture. I particularly love that to find a new culture and see how you can understand it better that is not only countries or big cultures like that, but it could be just the culture of people who love a board game that you’ve never played. I don’t play both games; I don’t know anything about this. They have a culture, right?
Can I understand their culture can I understand why they like what they like? So, this is a wonderful thing for me to do and to learn;it’s valuable anywhere you go. All this is fine, but there’s one more thing that makes a big difference, and that is “Business Value.” This is super important, and there is no way that you can tell me, ‘oh I’m more serious, I’m older now, and I don’t know how to evaluate business value better than when I was younger’ I don’t believe you. If anything, now that you’ve been around, you’ve seen projects go up and down. You’ve seen companies going to market and failing and coming back. You’ve seen that this is very hard to communicate.
We try to teach this at Data Science Retreat. It’s not easy to get people to produce projects that have business value or value for any definition of value. This is a huge added advantage that you have; you can see it when it pops in front of you, you say ‘yep this is business value.’ This happens only when you have the experience; when you have been around the block. I think this is a very important skill that it’s totally underestimated. There are too many people doing things in data science that don’t necessarily translate to value, and the market is taking notice. So, we don’t want to be known with Data scientists as the people who never care about value and produce technically sophisticated projects but don’t add business value.
That’s it for this session, see you in the next topic where we talk about creating or finding value as a Data Scientist.