Transcript #42. Data jobs: Interview with data & machine learning expert Catherine Lopes PhD.

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Renata: I have just finished interviewing Catherine Lopez. Catherine Lopez is the head of AI for ME bank, a bank here in Australia. And we first met when she was head of AI for AGL. AGL being an energy company here in Australia. And she hosted an event at AGL for LeanIn Melbourne. It was my first event with LeanIn Melbourne and we were both speakers for that event. I will make a link to the video of both of us speaking so that you can have a look and listen to us talking about confidence and personal development. But today I interviewed Catherine about her profession and her involvement with data science and data scientists and all of the work that she does, mentoring and supporting that amazing community. She has worked for very large organisations. She has been an academic, done her PhD and, worked in different countries and has an amazing day to day, working not only for ME bank, but also for her three start-ups.

Renata: One of them being a social enterprise, Ada’s Tribe that she has just set up to support women that are data scientists and want to advance in their careers. So if you are one of them, please make sure that you link to Ada’s Tribe Stripe and start participating in their events wherever you are in the world. Everything is virtual these days, as you know. But Catherine also posts regularly on LinkedIn and provides a lot of great content that is quite unique and original because, as a data scientist, she's always collating data and posting about the data that she collates, and many of them are to do with careers in data science. So if this is something of interest to you, if you are a data scientist yourself, or if you have any interest in data and you might want to re-skill and understand how to work with data scientists, how to manage them, work alongside them or become one of them, I think Catherine Lopes is someone you should follow and definitely, listen to this podcast. 

Renata: And if there is anything that I can do for you, once you listen to this podcast to support you in your career transition, in your job hunting, please don't hesitate to contact me. You can follow me wherever you found this podcast. This podcast will be a video as well. So you can see us on YouTube and on my blog, or you can listen to us as you always do on iTunes, Spotify, and again on my blog. Keep in touch, follow, subscribe, give me a review. I'd love to get more reviews. It will certainly help others like you find this podcast and good luck with your job hunting, but for now here's Catherine Lopez. 

Renata: How have you been, are you working from home? 

Catherine: Oh my God. Yeah. I'm working from home. 

Renata: You don't have to go into ME bank? You can work from home?

Catherine: I have been going for once a week. Okay. Um, but this time when the second, you know, the second round they started this grid, like stage three. So from there, I didn't go, but maybe next week I go there one time. 

Renata: So nice to see you. 

Catherine: I know me too. I am so excited. And finally something for a change. Neil like, ‘Oh finally, you wash your hair.’ 

Renata: Well, I haven't washed mine. As you can tell it's in a ponytail for a reason. 

Catherine: It looks good. 

Renata: Oh thank you.

Catherine: Yeah. We haven't really tried anything, but I'm feeling exhausted even by working from home. 

Renata: Yeah, me too. I don't know why, because I'm doing less and less and I'm still tired. 

Catherine: Oh, we're doing, I mean, Leo and I were doing, I think much more than before. It's like it never stops. So from morning eight o'clock until midnight, you would just say you're working constantly. 

Renata: Yeah. You're right. I'm always thinking about work that's for sure. Yeah. 

Catherine: Yeah I think for us it’s basically, it's not really thinking about it. It is the volume of work. Yeah. 

Renata: But for the podcast, we need to talk about you and your career. 

Catherine: Oh, I read your questions to every single one is really good. And I have yeah, I have I think in a lot of topics you put it there, there's some question you asked me in the, like say what's social media and saying what we want to promote, and now I do want to promote Ada’s Tribe. So we officially launched the Ada’s Tribe now. 

Renata: What is it called, Ada?

Catherine: Ada. Ada like my daughter's name. Right. 

Renata: Ada’s Tribe, okay. 

Catherine: Yeah Ada’s Tribe. And so this is a community for supporting female in data science and analytics. 

Renata: Okay. Do you have a website? 

Catherine: So we are building a website and about, we already self-launched with these group of people. We got all the volunteers and we ran first a session already. And then the second session we're coming up as a meetup is, job interview, I was thinking about you actually, so we maybe make it as a series. So, you know, I was hoping actually I didn't contact you early on, hoping if you can come as one of our speakers. So we have three right now and I hope you can do the Y as a senior level. So yeah. So the other there, you know, different stage of their career. 

Renata: Of course I would say yes, you know that. 

Catherine: Yes. And we got really good feedback, you know, so we self-launched about trendy people together, then immediately we got so many people reached out to say, well, how are we going to join and say, okay, let's roll it out. 

Renata: Yeah, of course. No, I'm happy to do it. Just send me the date. Now talking about leaders, let's talk about you. How did you get where you are now as head of AI of a major bank in Australia with two, start-ups going plus a what, Ada’s Tribe is a social enterprise going, you do so much. Tell the listeners of this podcast, how you got to the stage where you are now. 

Catherine: Well I think for me, I do think where I am now is through a combination of, first off for hard working. So that's work ethic and that's happened, I think this is from I have been taught by my parents and also people who I grew up, or the society, the community I grew up with, and a hardworking work ethics. But that alone won’t lead me to where I am, it’s what you love, you find something you love to do. At the beginning perhaps you do not necessary have to know exactly where you want to go, but something will click during your study and during your work experience. 

Catherine: So then for me is like from beginning, and I have been working in the area where I'm in the data where I found really interesting. This is a combination, many skills, and kept me learning. So this is a part where you need the gas, but you also need the motivation. So you need gasoline to keep you going, and you need a motivation to keep you going further and further, right? So I think this is the part where, I went through the journey from starting that data, then working with data, and also work in different domains and a different environment, or should I say different industries. So you will find somewhere you like. And I think it, you make a milestone of goal of you challenge yourself as well. So like, I like to challenge myself and we're basically you set a goal what you have done and what you want to do and you achieve it then is that enough? And for me, it's not enough. 

Catherine: So that's where from academia, then I move to industry, then juggle between these two. Then I was formerly settled down in industry and I see myself in the same pattern and I went to consulting and I went to, the software industry and I went to in house of building business and using data. So I think that they see a pattern of what are reserved for myself or I'm doing. So then after the 20 years and then say, I find well, entrepreneurship is something I find it is in me, how I do things. Some people call agile way, but as for me, I haven't really engaged into the start-up, and I was always curious about it. So that's where actually I challenge myself one more step and don't go out my zone. And that's where it started these two. 

Renata: What I think is super impressive about you and I’d love to hear how you do it. A lot of people wonder how others do what they do, but you are so hardworking and you've managed to pack so much. How do you actually manage your time and decide what you're going to spend your energy on and what you're not going to spend your energy on. And I think this is important for this reason. I see clients of mine and people in my community or followers, focusing 150% on their jobs and giving 150% on their jobs, but they never seem to be able to step away to focus on their careers, step away to focus on applying for a promotion or applying for another job. So you seem to be very good at identifying where to spend your energies to advance and it comes naturally to you. So how do you do that? 

Catherine: Actually I won't take that as come naturally to me because I think I learned. I was definitely one of the kind of person you were talking about. So I give more than a hundred percent that's for sure and if I could. So, I remember when I was doing my PhD at that time, and then other people call me 7-11, you know, so they never see me. I always before seven and after 11. And I think they see about you accumulate enough quantity that you'll make the change. So I was one of these people, actually myself perhaps is now just to be more conscious, and  compared to before is I can do things and I would just keep my head down and constantly and do the same batch don't think step a few steps ahead. 

Catherine: And so later, so there is a combination. One of my kind of mentor, I got into trouble and I had a frustration and I say, what am I doing wrong? Or, what I want, where do I want to go? And one thing my mentor told me, say, you have to, you work hard, but you have to keep your, not always keep your heads down and you have to keep your heads up as well. So look around where you are now and think, you know, stop for a moment. Stop for a moment. I think for me, what's really pause and stop for a moment think, where they were working on, is it on the direction towards your goal. So that's is, I think this is really helped me.

Catherine: Not in terms of manage the time, because time management is a different set of skill. Strategically, I had never actually think about it as a strategy, but now I'm thinking more and more, I think, many of your listeners, and many people there, maybe you should think about, you know, where do you want to go? And, what are you doing? Is this what you're doing will contribute or make any step towards where, you know, what your goal is. Of course, the situation could change dramatically. Just like the COVID-19 no matter how hard you're working or you design a really good strategy, things happen and you’ve got to be flexible. So sometimes I think is you drive, sometimes the environment drives you. 

Renata: Yes. What I like about the way that you position yourself is even though you're doing all these different things, they're all very consistent, they're all aligned. And they're adding to your brand as a specialist and AI data analytics data. So every time that you participate in something new, it's still very much around your expertise. And when I see your LinkedIn, all your posts are also aligned with your expertise. And I think that, it makes it easier for people to know how to connect with you and what to contact you for. If they need help, they know that they can come to you for your expertise. So why don't you explain to the listeners your expertise and how important that is in this world post covid? 

Catherine: Well, I think let's put it that way, if you read, there a lot of articles talking about the companies now trying to build their employees into a T shape. So I think that's is I learned the T-shaped early on. Not to say early on in my career, but to meet off my career, I understood that's is the way I want to build my career which is along where I love the most and where I can contribute the most. But in the meantime, of course, you have to build a horizontal bar where it give you the exposure, right exposure. However, you do need to have a T, which is the depths. When you have your tabs, where there's a, for me is called, I am an end to end data and analytics professional. And so I went through from the end of understand the business problem, relate it, and translate it to the data problem. 

Catherine: And you can process the data. Then you can engineer the data, you can push it out. So that's a T where everywhere. The bigger data, this space is really broad. There covers a lot of touch on the technology, touch on the business. And the last 10 years I have focused on my building the T, which is the after I find that where is most of the entire spectrum in data space. The fascinating topic for me is still in the data science. Data science itself is already very, very broad, and you can apply any different angle of course this is a multidisciplinary field. So that is the same which enable me stay in one field, but I still have the flexibility to do many different things. 

Renata: Yes. You're not going to get bored anytime soon. 

Catherine: No, no, no, that's for sure. 

Renata: And every time I open LinkedIn or any newspapers today, it's all about how governments and organisations are planning to re-skill their workforce for, to be able to work with data, to understand data and to be able to be data scientists or to work with data science. So what do you see is, exaggerated in that and that those statements and what is true? What is it that people that are in the workforce, in the corporate sector or not for profit or public sector, what do they need to know about how their work is going to change in the next, let's say three to five years that they would need to adjust their skills for? 

Catherine: I think this is two things, right? And so, prior, although we are still in the COVID-19 pandemic, the data science and emission learning and the AI went through a hype in the past five years. So that's you seeing a lot of the gadgets and a lot of fancy things came out and you see on the YouTube and you on anywhere, but there is not, not many of them actually being successfully productionized and be used broadly. And also the value for a broader sense of business have been realised. So this is a part where I say, you know, people don't really understand the depth, or there is only the part. So I got questions saying, what do you see the data science emission or your AI for this, for my company, that's a question quite often do. And I think if people were to think about it, or I would say, you know, this as a hype, and a more and more organisation have realised that data is really, really the key for them to make the business moving forward. 

Catherine: So, because that data actually, so I can tell you that some, even during the covid time, there a lot of historical patterns and what you understood, it doesn't exist anymore. So then, and the people are making guess and based on the experience. Same problem apply to the machine learning models they built because machine learning models a build based on historical data and a lot of people, a lot of people who work in the industry, in the company, they had their knowledge before the differentiate situation. So now they actually asking for say, we want that's our strategy. However, we need to have the data to analyse what is happening before and what is happening now to work out a strategy is data driven strategy. So that's just the part where, I’m think you know, it is true. So now I am seeing [inaudible]

Catherine: Their companies realised and asking for data and also they are not being so many, a good, a change I can see is before they have their mindset or hypothesis, see can you validate this? They're just using the data to validate their hypothesis or what they want to do. They'll say, okay, I did this. Can you validate and prove I’m right? And there is a shift. So there's is a, there is a shift of people where come to you with the open questions. So here's my problem, here's my problem statement. Are they anything you can see from the data and help us to give us some insight and we're working through to build a strategy and the depth quickly. Yeah. So this is a part where I think, you know, I certainly see in the last few couple of months. 

Renata: Yes. I find it fascinating. And when I think of data like that, I don't see it so much as a, stem only issue. I see it as something that could really include professionals from other areas, like from sociology, anthropology, psychology, education, to help businesses and R and D organisations develop better ways to collect and analyse data. Right. I don't see that discussion happening a lot in Australia at the moment I see it overseas. And I see it being action, not just discussed but actioned overseas, but not here. What do you think about that? And the recent announcement, for example, that some of the humanities courses would have a bigger, a hex fees, than the courses that are STEM related, and it just feels like there's this disconnect between that need to have a more holistic approach to how did data science as a, programme of, I think that needs to be developed more holistically will have to be done in Australia. Am I right? Sorry. I'm rambling a bit but. 

Catherine: No, no, that's fine. So I can break this down. So I see that’s big topic. There's a few things you've covered. So I, let’s talk about the one you mentioned about the government. I think that's the, you know, the changing phase structure and in order to attract students into university and to try to balance it out. Right. So that's is one thing I think I won't, I think this is a temporary looks like a, some tactical thing government proposed and into, by addressing these days. It's true. Any actions they, they are doing. I think this, especially now during the COVID-19, I don't know, what's the policy is, you know, how they plan in the long term, the, the intention is to try to balance and to bring more people who work, STEM related. 

Catherine: So I think, I will say, intention is good but how they are going to roll out and how the, you know, the impact will, you know, they'll take four years for the graduate and how the job market led. And it will take a longer time for ours to realise. However, so the other topic you were talking about, which is definitely true, I think that data and also data science is not only a STEM limited. So, for example, the STEM limited and, for example, the ethics, right. That data ethics, and I almost, I attended a few discussion, panel discussion talk about how shall we teach the programmers, the ethics. And I said, yes. And I always say, I will promote that for, not only the governance is ethics because you are not a robot, right? 

Catherine: So this is, so I think in one way, you have to get people who are working on the data science to know more because awareness of you, which the data science people are working on, and what's a model you'll produce could have downstream impact in the society. Which is if you are not aware, if you cannot work in, isolated, like a real robot, right? So, you tell me, and then to write A, I I'll write A, although the A, if I write A in my code and I'm going to have a consequences is a punish somebody else, which is unfair. So that is, we need to, I think, infuse, I think that they see we really need to bring that the other disciplines into the people who are working in data science. In the meantime, the data science, or maybe let's talk about the data is a broader, is much broader because I think many of the STEM disciplines, and then they have really being technical and you have to use you, you have to where you're using data and you have to collect the data and you prove it. 

Catherine: Right. So there is many, a lot of humanity and social science and also the philosophical questions. It is not something which is you, you're using data to prove a theory. It is different to the discipline is different. However, the data, what you are collecting and, like the election, you know, in the social science. So they're very data driven. They are very data driven, you know, where to actually to promote. And then they are doing the exercise in driving the social movement, even with where you, if you have your data and you have insights and you can be more target oriented. 

Renata: Yes. Yes. It's fascinating, so basically we now live in a world where data is King. Data is going to be used to help us live better. And also it could steer us in different directions. And the idea of having data scientists trained in ethics is a good one because it allows the issues to be picked up at that ground level, right? How much, you know, from a leadership position and that level of seniority and where the strategies are being made, so how much is that part of the discussion at that level as well?

Catherine: I think this is again is a mixed bag. And is quite often is by industry as well. Right? So some of industry, they do have a lot of them. I think where, financial industry, especially in Australia is quite ahead of many other. We're not talking about the tech industry. The reason is they have a lot of regulations and, they do have a lot of tough, very strict rules apply, right? But in many other market where the other industry, perhaps it's not. So the USA, at least in my observation, the other major, financial industry, the players, and then they are in Australia, they are ahead of a curve compared to other industry, realise that data is really important. And I'm not saying that they will, so that's, for example, in Zed CDO just got promoted to the executive level, which is report to the CEO. So you can say that's is the, the importance of them to realise this role or this function is really key for the organisation to move forward. 

Renata: Yeah. Do you find sometimes people in organisations don't see a problem as it being a data driven problem? I'll give you an example. I also work as a consultant and I was once called upon a large organisation in Australia to come in and help them deal with a cultural I'm doing, um, this to show that it wasn't cultural. A cultural issue where there was a lot of duplication of work being done by different departments. And they wanted me to come in to facilitate some discussions between those individuals that had similar jobs in different departments, and they were duplicate, they were duplicating work for the organisation. And I said, that's great. I'd love to come in. Can I bring my colleague? So I, in the consultancy, I have Diego who is an expert in robotic process automation. Can I bring an RPA consultant who is a colleague of mine just to observe and see if there's any opportunity for us to discuss that? 

Renata: And they said, Oh, no, no, no, this is a cultural issue for sure. It's not, it's not it. We don't need an RPA expert. And they surely did. They did. It was completely an issue that the organisation was so big and diverse and having some sort of cheap, you know, bot system working there would be much easier than trying to convey to all of these different individuals that they were duplicating work. But I think people have this misunderstanding that something like RPA would just replace everyone's jobs. When in fact, none of those individuals that were quite senior in their roles had that specific task as part of their job description. So they weren't actually doing what they were supposed to do, which was a much more creative relationship management type role in bringing in new business and doing business development, because they were bogged down into, you know, doing all of this paperwork in the background. And they were all doing very similar things across different departments. And I thought that was really a fascinating business example of a misunderstanding between what's a cultural problem, and what's potentially just an efficiency data solution that could step in and resolve that.

Catherine: I think this kind of a situation happens not only on the level you described, it happens everywhere. I can tell you this happens everywhere. And, this is a few things I always say, if you, I think I’m referring to the other, I'll bring back to the data science is a team sport. So what's the situation you've described is also a, you have to put out a holistic way of looking at people and a process and a monitor monitoring and validation. So that's, you have to bring them together. So just like you say, if it is culture problem, because they haven't been, perhaps I'm just guessing they haven't been really exposed, or natural fear is if you bring something else or a new idea, a new technology or new data in, and I'll be replaced. 

Catherine: So, but that's natural to people, you know, people will always get scared of anything new or they don't know. And if they say okay into the frightening mood, right. So in a frightened mood they will be resistant. So that's where they say that this is, I think the more you are actually, it's funny in a way. When I do the data science or I do data strategy and people asked me, what's the challenge, I say change management. Bring people on a journey to have the mindset change, or open mindset, be ready, also not be afraid, and we're not threatening anyone. We're just to bring everyone on the journey and we are going to work toward the same goal more efficiently. That's the part where I always say, you know, that's quite often happen, it happens a lot. 

Catherine: And you can see this is the same situation for data and analytics. They have be in the middle between business and technology, right? Yeah. So it's like, I would say sometimes I joke with these people saying we are purple people. Because there is blue group and there is red group. So we, sometimes were sitting with the blue group, sometimes with technology, but we need to deliver the value which is a business side. So I say, you know, there isn't something where you say, wow, you know, my team quite often asked me and say, ‘Hey, Catherine, we’re in Dumont, but we just have too many things pulling us apart. We don't really have the marriage. You know, they need a good marriage to put two in one picture. I think we're just, I think it is a fascinating for me. I love data analytics because we get a chance to work in both sides. 

Renata: And that's great, that's a great segue to this next question, which is this, if you have lost your job in 2020, and you were thinking and reading about, data science, data analytics, the importance of this for the corporate sector, and you're thinking about reskilling, is there still time for somebody mid-career to make a shift and pivot to, join, you know, the team sport that you talk about or is it too late? Is it something that you have to do very early on as soon as you get out of high school and do your undergrad and then start early? I don't know, there's this, there used to be a time when, you know, only very young people were sort of considered for roles. I don't know if it's still the same. 

Catherine: I think, the answer to your question is yes and no. So this is what the community that I'm, you know, I worked with many volunteers and finally we actually just self-launched our community over many years we have been working only once a year, but now we have a community for supporting women in data science and analytics. And that is a part where we want to change career. And, they always ask me they say, we're not the school students anymore. And we are either taking some courses online, and is it too late? My answer is no, never too late. So that's, but I asked them, why do you want to change? Okay. So it is never too late. And also this is individual situation's very different. So if you are coming from art and you say, well, you know, I want to change to data science, and I will say go for it, but it will be harder now. Yeah. Right. 

Renata: Well, it would make more sense. What would make more sense? What professions are sort of an easier transition into data science?

Catherine: I think the transition in data science from data analytics, you know, so I'm talking about now COVID-19, because you frame the question is in 2020, right. We're in this reality, a lot of people, and I think, maybe the number of Victoria only prominent was 7.5 and went up ever record high. So this is unprecedented in error. And we all here in the very, very uncertain environment. So I think in this uncertainty we have, and my recent advice to people say, don't make dramatic change because you are on the market where too many people are competing for the limited job. And so that's, I can say from many, it doesn't mean that you cannot change, right? So then back to your question is which are the professions and the easy to move into the data science. 

Catherine: So data science is a broader case in where you can be the data analyst. So from the business but you have been doing a lot of data, touching on data. So that's, it depends on where do you want to work in your data science? What part of the data science you want to work for? And so if it's a very theoretical part of model building, then perhaps you need to take longer, you know, it will take longer, you have to understand the algorithm. And statistically It's not just by, I do an online course in 20 days and it will happen. So that will be hard. But if you are coming through a team where the engineers, a lot of data engineer, or maybe people who are working on the technology and they already play around with the data, then they can actually start tapping into the data engineer. Now from data engineer, you work with data science project, then you can get in, right. So that's easy. You already started building the, so from business side and from the technical side. So that's the part I would say, there's a few field directions you can approach. 

Renata: Yes. So I would summarise as this, so if you come from a background that has data already, it's a matter of just progressing into that. But if you're coming from something outside, it's a much bigger transition that would require a much bigger career transition, possibly a master's or even, you know, a longer time for you to transition into data analytics, but it would be an investment for, let's say, 10 years, right. It's something that you could do for a year study. And then move in to that profession with a longer term goal, not something that you can do right now for 2020.

Catherine: Yes. And so often with my mentees I ask them, say, you know, go to do a gap analysis. Now I'm very well known for the gap analysis now. So I'll ask them, actually I learned from one of my ex colleagues as well to be specific and that is a very systematic way to think through. So I say you do a gap analysis of what you are good at, right. What skill do you have and what are the skills they listed on the job description? And also what part of the gap and how much effort you need to put in. So then you find somebody who are in that job and talk to them, you invest a lot. Then you get into the job and they say, no that’s not what I want. So that’s not, you won't feel happy. Yeah. So that's the part where people really need to do a lot of homework it's at the moment. 

Renata: Yes. Yes. I agree. And you know, you do a lot of mentoring and you have just set up Ada’s Tribe as you mentioned before. And I have invited you on this podcast mainly because of all of the amazing posts that you do on LinkedIn, with graphs that do explain a lot of statistics about the profession and you do some analysis of gender and by country and by profession and by age about how men and women progress in careers in data science. So what started that? Are those graphs yours? Did you, do you put them together? 

Catherine: So, yes. And so I worked with one of my mentee and co-author. And so we scrapped this data, its a public data and I always have been advocate for, I want to say, look I am a female data scientist when I came back to Australia four years ago. I know we were talking about, okay how many female in your industry and quite often they ask us, how hard to get a job and where you are and all this background. So that's the part when I started working with one of my co-author and, so I say, okay, let's pull out this data. We are data professionals. And I love visualisation because as much as I love the analytical part where the, you know, all the algorithms to be honest with you and that is hidden, but eventually what you produce, you need to tell a story. 

Catherine: So unless you tell the story, it's hard to get into people's head. And I'll have a very, when you talk and when you are putting the right articles. But, if you visualise it, a lot of people, we are just very visual. So if you put the visualisation there and people get it straight away, they get to the concept. So yeah, and so we use a table to build that. And then they do all the data manipulation, then frames etc. It’s a lot actually, it’s a part time project. It took a long time for us to pull out together. 

Renata: I can imagine. And of all of the data that you've analysed so far comparing professions by gender and age in each country, what were the most surprising to you? What did you find out by manipulating and using that data that you didn't know before? 

Catherine: Oh, well I think one thing I didn't know before is about the career, how active female data scientists compared to male data scientists on the job market, and across industry and their activities. Yeah. So that's the part, well I found it, this is very interesting in a way, if you look at the technical skills, that's technical skills from the data, we use a public survey data or from 2015 to 2019, which is across different countries. And so female and the people who actually put data scientists as their profession, overall they have a higher educational backgrounds than male. And overall, they have more technical skills in terms of programming.

Catherine: Yeah, overall they have more technical skills and then they have higher education background qualification, but they get less paid compared to their male peers. And also they're not active in terms of job hunting. If you look at one of the graph I put in there for male data scientists and across different industries, it's just like a spider web and like snakes and are all over. They tried different things. 

Renata: They are all over the place looking for work. And the women aren't. 

Catherine: No women are not, the women are stable. And there are only a few streams of this, you know, approach they're coming, they’re very stable.  

Renata: I remember that graph because I looked at it and I wasn't wearing glasses. And even without glasses, I understood immediately what it meant, because I was like, yes, this is exactly what I see. And I, I don't think that it is unique to data scientists. It's amazing that they don't invest in themselves that way. And don't see their careers in the same way that men do. And I wonder if it's about the family or if it's something else, what do you think based on the fact that you've been managing large teams of people for a long time? 

Catherine: Well, it is interesting to hear that you say this is not only the data science. Actually for the data science field, I would say I would expect it less than what I have seen from my chart, because for the group of people especially female to make it to data scientists field, it's not that easy. It's really, that's why I said a while, unicorns. Right. And really I think it’s not that easy to get into the data science field especially during a certain time. And once you, so my impression was, my hypothesis was, okay, so they're making it, they push so hard. I know there'll be more aggressive, but certainly not. Certainly not as aggressive as men in the same field. 

Renata: We are generalising of course, you know, I have wonderful female clients that are very proactive and very ambitious and have signed on the spot and all of that. But the majority of the time women do tend to be reluctant about investing in their careers. 

Catherine: Yeah. And I think for data science, I can say something even from my own experience and from other people who I'm coaching or, you know, not coaching, mentoring. So for data science especially, you need to invest a lot. It's not like, I'm not saying like a traditional profession. I'll give you an example, accounting. Right? So accounting, I'm not saying they don't put much effort, but I once talked to my friends and who will give up our career at the same time. And so she got a CPA and, so I didn't really get a CPA.  But it’s stable, so there are no many changes, like it's not going to today and the taxation change and that is marginal. Right? Yeah. And so the change I tell you, so well, the I.T. technology change is fast, right? 

Catherine: The business and AD is quick, however, an amplified the degree of change, if you do data science. So to cope with the change, that if you don't really, at least it depends on where you want to play, you know, say, Oh, I'm comfortable with this I know how to write these, perhaps you won't last long. And so if you, I remember one of my chart, and there was like when women and a female and a male in the data science I think around age, 25 to 30, a lot of more female than male. Right. And a much, much more. And even compared to a technology, there is a perhaps excitement and they were really driven. However, once you move to the 35 perhaps it burst, you know, children, family time and they reduced dramatically. And that is similar as others. But I wasn't surprised to say actually, and they're much higher proportion for female compared to male in data science in that age group. 

Renata: Okay. I think that to make it easier for people that are listening, I will post the illustrations on the episode show notes. Would that be okay with you? That data there so that people can look at them and see what you mean and what we're talking about. 

Catherine: Yeah. Actually I was thinking because I, you know, while I'm working on that I was working on each of dimension, each area topic, and a post it. I'm thinking I will consolidate all of them into one article. So then people get a better picture. 

Renata: Oh, that would be great. Yes. Yes. If you can do that in the next two, three weeks. That would be great. 

Catherine: Yeah, I would do that.

Renata: So that's great. The other thing that you did that was fantastic was a list of questions that people should ask employers when they go for job interviews. And I thought that was brilliant. I thought that list was so good that even if you're not a data scientist, let's say you're a lawyer. You should just change the words and, you know, ask those same questions anyway. That's a great list. How did you come up with that? 

Catherine: Oh, thank you. And actually, I think I did so many interviews. Yeah. I myself, I have done a lot of interviews myself and I interviewed so many people. 

Renata: Yeah. So you've both been a candidate and you've been a hiring manager as well. 

Catherine: Yes. And I put some, you know from both sides. And especially from the recent years, the reason for that is, I wish I could help the candidates better. And, because I know from my experience in some, I'm helping, I'm still helping a lot of my mentees to prepare for the interview and provide the reference. And it's quite often, they say, well, they don't really know what to ask. And also there is the way I thought, okay, I think this two way, don't be too eager and just jump in somewhere you don't want to stay, or you won’t stay. So it benefits both sides.

Catherine: So you're going to see the churn rate for data scientists stay in an organisation is really, really high. Yeah. It's really, really high. So data scientists jump around. And there is a reason that quite often you can see, I would say you can attract candidate, but you would be hard to retain them. And so that's the part where I thought, well, you know, this is two way, from the interview, from the company, then can use this question to frame what they were looking for and think about it. Maybe not many of them think through, but for the candidate that is a good for them to get into this field, you get what you want or less disappointment, or, you know, being more elastic, what you're going for. 

Renata: Yeah. That's great for every profession, but I can imagine that, especially for data scientists, that's really important for them, because for them more than for others, there's such high demand for them that they can pick and choose, like you said, and find the best fit for them so that they don't keep moving around as much. Yeah. Now tell me, how is working remotely going for data scientists? Is it easy to work from home when you have team spread out, working with data that sometimes sensitive working from home? Is that something that's sustainable? 

Catherine: I think, you know, I work with a broader team. So my team had, for this current team, I don't really have many data scientists with ours and we just didn't have a build it yet. So I think most of the data professionals I spoke to within the organisation or beyond my peer or my, you know, other people who I know, looks like working okay. Yeah. And as long as, you know, as long as the system connectivity for the system where they can get hold of the data, they can use the software. And also they can understand the question, you know, so I think, I haven't really heard much complaint to be honest with you from the data professionals 

Renata: And Catherine, do you have any last advice or tips or ideas that you would like to share with the listeners, people that are looking for work? They could be the data scientist, or maybe they're just interested because they might work with data scientists, they might be colleagues or bosses of people that deal with data. Do you have any advice for them? 

Catherine: I think, my advice is to be patient if you want to do data science. It’s not something like learning to write few lines of code and you can become, you know, you can pick it up straight away and then be a professional data scientist. Be patient and realistic. And then so this is a part of where they were, perhaps they will need more investment compared to other professions. So the other part I would say, don't be afraid. Don't be afraid. It doesn't matter what gender you are, you know? So I will strongly also encourage that if I can use your platform to encourage more female, they use this time to learn, to get hands on the data, you may find it that's is interesting. 

Catherine: And you don't have to have deep understanding, or you know the depth skills for data manipulation or their skills, but you still can work on this direction in data science field. So the other thing I would say is stay engaged, stay engaged to where it is. You will find some project to do. And by doing, by learning or learning by doing so, if you just learn some concept and don't do it. And I would say, the more times I burned my hands, actually, the more learning I gained. 

Renata: Yeah. I think that that application of what you're learning is so important for new concepts, isn't it? 

Catherine: Yes. Yes. 

Renata: That is good advice. I'm going to add to the episode, show notes also a post that I just thought of which you did, and it's a list of books that people can read. I'm going to add a couple of books to that. I will make a comment and add to your books as well.

Catherine: You read so much. 

Renata: No, no, I don’t read a lot about data analytics, but I love data. 

Catherine: Oh, one thing I really want to say is we did some really good work, I do think so. And then now it's a project I work with another one of my mentee and get into this space. And then she is a very well established researcher already in the computer division, but not exactly. We worked, still working on covid-19. And so we published two articles already and one is looking at Australia, Australia's covid compared to other countries. And then the second article we looked at the, I mean we published as, what kind of majors is timing really matter? So we're working on another one. So that's hopefully can help people… 

Renata: When do you sleep? I don't know. It was lovely talking to you. Thank you so much. And I will make sure all of those things are in the episode show notes. So people can link with you and join Ada’s Tribe, Ada must be so proud that there is a social enterprise now named after her. You have to name another one after your son as well. Otherwise it could be a fight there. 

Catherine: I know Leo said you cannot just do it for your daughter. 

Renata: Yes. Nice talking to you, Catherine. 

Catherine: Yes. Yeah. Really enjoyed the chat.

 

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