Help with your skills rather than with your money

Interview with Karrie Liu

There’s a lot of community charity work we can all do but there is not a lot of work where we can maximise our impact by using our specific skills as programmers, as statisticians, and as data scientists.

Stay tuned as we talk about these helpful points:

  • How Karrie got into helping charities with data science
  • What typical challenges charities have
  • What are the differences of other organisations which provide help to charities?
  • What should a statistician/programmer or data scientist do to help you move forward?

Listen to this episode and share this with your friends and colleagues!

Karrie Liu

Freelance Mathematician at Hypatia Analytics

Karrie Liu is the founder of Hypatia Analytics Ltd and dedicated analytics consultant with 10+ years of experience in UK healthcare and life sciences, focused on providing additional value to clients through driving end-to-end consulting engagements to enable business transformation through impactful and actionable insights.

Karrie sets up Hypatia Analytics in 2019, in hope of to get the balance between working as data scientist in corporate assignments and spending time into pro bono projects and charities. She believes data are at the heart of better decision-making and is important to apply these insights into their planning.

Karrie was also speakers at women in Data event and the winner of the top 100 women in tech 2019, she wishes to show everyone that in spite of one’s culture’s prejudices or biases, it is still possible to build a network of support and to campaign the matter and start to make a positive impact from senior management.

More about Karie:


Alexander: You’re listening to the Effective Statistician Podcast, the weekly podcast with Alexander Schacht and Benjamin Piske, designed to help you reach your potential, lead great sciences and serve patients without becoming overwhelmed by work. Today, I’m speaking about how you can help with your technical skills rather than with your money. This is an interview with Karrie Liu. Stay tuned.

There’s a lot of community, charity work that we can all do. But there is not a lot of work where we can actually maximize our impact by using our specific skills as programmers, as statisticians, as data scientists. And this is what this episode is about, so stay tuned. This is a really, really cool one that I think many of all find really helpful. Speaking about being helpful, this year’s PSI conference in Gothenburg, Sweden is again, a face-to-face conference. Crossing fingers that everything will work out fine. But there’s a lot of great content coming. There are lots of sessions from different special interest groups. I’m really looking forward to the one for example from the visualization realization special interest group, from the real world evidence special interest group, from the newly created launch and lifecycle special interest group, with lots of lots of great stuff together with awesome networking. I’m really looking forward to this. I’ll definitely be there. So see you in June, in Gothenburg for the PSI conference. Just head over to to learn more about this conference and see you there.

Welcome to another episode of The Effective Statistician. Today, I have a special guest and that is Karrie. How are you doing?

Karrie: I’m really good and hope everyone has a lovely Christmas and stuffing a new year. It must be hot in January, when there is so cold, and we are starting to work and yeah, but we are all good. So yeah, I’m fine. And you?

Alexander: Very good. Yeah, it’s kind of in the middle of January as we are recording this and starting in the New Year. Karrie has some great plans in the new year. But before we talk a little bit about the topic of the episode, maybe you can quickly introduce yourself and your background and how you got into data science and statistics.

Karrie: Hi everyone. So, my name is Karrie, obviously. And I am an analytic consultant from a company called Hypatia Ltd. I set up this company in 2019. The reason is I would like to do some project with a corporate client. But at the same time I would like to spend more time with pro bono or charity projects. So that is the set up of the company. And as for myself, my background is mathematician. So I do have two master degrees in statistics. But I don’t call myself  a statistician because I think that is more than just statistics and logic and everything else that I do as well. So that is the reason why I prefer to be a mathematician, to be a scientist as well. And career-wise, I have been working in the UK for the last 15 years. So 10 of them are in the NHS and then the other five are in the pharmaceutical company and also insurance as well. So here I am.

Alexander: Very, very good. So data science and charities. That’s a really, really interesting thing, you know. When I think, you know, there are lots of companies that have these kinds of projects, initiatives about giving back to the community. And I’ve participated in lots of these, we’re kind of, maybe you visit a hospital and help there or you do something about the green environment, all kinds of different things you can do. And, you know, my wife also once told me, you know, if you would just spend what you would have earned that day, you would probably have had a bigger impact. And when we are just kind of cleaning up the streets, it’s nice, but we are not really leveraging our expertise or capabilities. So when you informed me about helping charities with data science, I was really intrigued. So, how did  this idea come about?

Karrie:  So, let’s start with data scientists first. Like you say, I think being a data scientist or you’re a statistician or mathematician or you know, data engineer, doesn’t matter what angle you’re in. But we go through a lot of training to get to where we are. And this is the training that could be quite formal and informal but it’s still our experience and knowledge. So I do think that, you know, that in the business world we are driven more to data, driven in strategy, and also culture. Why did the charity as well? And the other thing is sometimes we all want to get back to society. No matter how you want to do it, like you say, but I do think that being a data scientist is what you need in the world. Why can’t we give him back our skills out to the community that we love or the course that we love rather than just going to do a better day. I’m not disrespectful with people like doing big a day or not, you know, we know that, it’s not like that. But I just thought, instead of just giving money to the charity. We can build a community using data science that can help other people and charity as well. And they all have a saying that instead of teaching someone how to, you know, give them a fish to eat. You need to teach them how to, you know, catch the fish, cook the fish and then eat it.

Alexander: Yeah.

Karrie: So you need to make sure the system is, you know, cycling and moving forward. So that is the reason why I think I want to use data science to help other people, who want to be a data scientist, or they want to use data to build a better world. So that is how I see data science with the relationship with charity.

Alexander: Yeah and there’s a point also kind of lots of us, just love what we do. Yeah, we love programming. We love data. We love kind of, you know, making the best of it. And so I know that, lots of people just love doing this and then kind of doing this and at the same time, giving back to the community. I think it’s a wonderful combination. So yeah, I completely agree.

Karrie: Yeah, that’s for myself, if you allow me, let me go a little bit deeper, what is the reason why I’m going into charity and data science as I am from three different points of view. So I’m an Asian girl, I’m a Chinese girl. And to be honest in our culture being a girl is not as important. Let’s put it this way. And my father on my graduation day said to me that he wished that I’m a boy.

Alexander: Oh no.

Karrie: Yeah and on the day that I win the Top 100 women in Technology 2019. On the day that I won it, he said to me, “Oh, there is no money in it, why do you want to win it?” So it’s really all negative. And I want to tell my father that, well, ‘I’m okay. I’m actually really good at what I’m doing’. So I want to show other girls or other peers that well, if you have a passion, you think that you can help people, you can do it. So, that is the first reason why I’m doing charity work. Second, I immigrated to the UK. I’ve been here for 20 years, a very long time. But on my journey in the UK, I got a lot of help from the community. Different types of help are like, sometimes, it’s experience and it’s not the money. It’s not just money that they help you. It’s about giving you security. They give you the help on how to build a community and how to integrate into it. So I wish that, you know, they help me to settle into the community. Why didn’t I use my expertise to help other people to settle into the community as well? So that is the angle of it. The last angle is like I said, I worked in the NHS for a long time. So the NHS is the National Health Service, which is the government. So in the UK, healthcare services are from the government. So when I work with that, as you know, money will be tight because it’s the government, you know, all the government side, ‘we don’t have money, we don’t have the funds to cut everything. So working there, people normally are not money driven. It’s more like I want to make the show society better. I want to make the patient feel better. But having said that, because of no money in it, you will lose a lot of people who are really skilled. I mean, we all need to survive. So financially, some of them, you know, some may need more than the other. But at the end of the day, we only financially support ourselves before we can get other things.

Alexander: Yeah.

Karrie: So that is why I thought, you know, when in the NHS is what is so difficult to get skillful staff, let alone the charity because they are long profit. They are helping other people. So that is the reason why If I can help, why not?

Alexander: Yeah, I think also for the non for profit for the charity organizations. Yeah, if they get money, yeah, I think they predominantly want to spend it on the projects they are funded for, not for internal bureaucracy and these kinds of things. Yeah, and so if we say can as data  scientists reduce internal bureaucracy can, you know, improve the way decisions are made on data, you know, the whole information and data flow, make it, you know, faster, easier and more automated. It’s a huge benefit for the charities because it helps them run a charity more effectively with less overhead.

Karrie: Indeed like you say when they got money. So, basically, even if you start up a company like that, they get money or when they get funded and then they focus on the things that they know first before data, some of the biggest, not everyone comes from data background or scientific background. They won’t think about how important data is and can help them. So, that is another thing, that I think the typical challenges, the charity will be like you say, when they get money, they put it to the project that they need immediately. They aren’t really able to see the bigger picture. So that is the one thing. Second thing is they don’t understand how to create a data set or what is useful, or how can I collect the data in a way that they can use it in the future. So I think being scientists like you, I mean, you worked in business before that. We all know that data is money too, right? It’s just thinking Pharm. So it’s valuable. So, how can we make sure they help them to keep hold of their access, right? To make it more interesting or just just knowing what they are not doing themselves, that is already a big help. And sometimes when I asked the charity, for example, ‘oh, you got volunteer, right’. Yes, we got a database for, You know, got 200 volunteers in front of us. Right? I also asked. ‘This morning, how many active volunteers helped you at the moment they were sent to you?’ No, I didn’t know the answer but when you know an answer like that, you can help them to build a rotor for example, and understand what kind of stuff they need or what is the missing point? So you can, you know, when we could have a mixed group of volunteers. You already know that when people ask me a question like, or on a typical what kind of skills that we want, normally how long did it take to volunteer every week?

Alexander: Yeah.

Karrie: So, you ought to build a bigger picture. So, yeah, that is the reason why I think it’s important.

Alexander: So that’s a really good use case. Yes, if you want to predict how many volunteers you need, all the volunteer data itself in terms of, you know, phone numbers whatsoever. That’s you need to maneuver and these kinds of things, what are case studies where data scientists can help?

Karrie: So for me, it is at the moment. I want to build a framework to run it to see whether, you know, in a typical lifestyle of all local charities, what they need from the very beginning. If you start from the beginning, right? I would like to have someone with a data engineering background to find. Tell me if I want to build a local data set, how easy or how difficult it is, to start. Second is what kind of open resources are out there that we can use? And what kind of skills that you need. Basically, like I said before, you cannot just build something for them, but at the same time you need to teach them how to use that, right? So things like that, for example, you need already desire educational packaging for just a set of databases, right? And the next step is, how can we use the data? What kind of data do we need? So we need to understand what kind of questions the charity or business case, I mean, in the business world will be like, what question that we want to answer? What is our target for the next five years? And then after we know the five year plan, we need to chop it down to like yearly. What is the milestone in every single year? How can we measure it? How can we make sure that we got enough information for me to make my point decision?

Alexander: Yes. I think, here it is important to kind of take a step back because there’s other organizations where data scientists can actually,you know, help as data scientists, to help charities. The problem there is that charities need to, kind of, define what they want the data scientists to work on. And as we all know, people don’t really know what they need. Yeah, unless it’s a really big organization that has, you know, already an IT department whatsoever. Then of course, people know what they need. And they can, you know, work as highly skilled data scientists. I think the difference of Karrie’s organization is that it’s targeting all these huge numbers of small charities that don’t yet have an IT department that, you know, don’t know what questions to ask, and to set up a framework that can help all these different smaller charities to work more effectively. So it’s less of a bespoke approach for each of these big charities, but more kind of creating a framework, creating a kind of more modular version that can then be replicated and kind of adjusted a little bit for many of the smaller charities, because I think, lots of charities will have very, very similar, kind of data challenges, like managing their volunteer data, managing their finance data, managing the kind of things that they do in terms of outreach, in terms of promotion, in terms of, you know, getting sponsors, raising funds, all these kind of things are very, very similar for lots of the different charities. It’s just that you know one charity may help people, another charity will take care of the local forest, another charity will kind of look after the kindergarten and whatsoever. Yeah, so the content itself may vary but the fundamental underlying questions are very often similar, isn’t it?

Karrie: It is yet definitely like you say, it’s like we are trying to do the thing, but the thing we we’re trying to make sure that we give them a best practice example, how can we move forward? And like you say it is hard when you don’t know what to do. So that is why we can, as experienced data scientists, we can come and hold them to you know, guide them what is the best way to ask the question? What is the best way to use the data? So help them to save time with our experience.

Alexander: Yeah, basically, to give them some kind of common operating system that can work through and leverage that data in the best way. Yeah, and I think that, you know exactly like you said, the first step is having, you know, people that have more of a data engineering approach that know how to handle, save all the data, collect the data, extract the data, load the data. Yeah, so these kinds of things.

Karrie: Yeah, exactly. And now you say when we got the data, we can do the next time we can have a statistician to find out the modeling for doing parametric modeling. For example, create algorithms, like, AI to find out that well, which location could lead them for  help, for example. And afterward we can also have people doing data visualization for example, that is from the moth design background to, you know, marketing that better story out. So like I say, it’s not just one type of person that you need when we’re working on a project. We need different types of expertise to make the work to the best. It doesn’t make sense?

Alexander: Yeah, and the idea is to have to know each project. Yeah, basically, build new modules that can be reused again and again and again. So over time we get more and more modules that we can, you know, just stick together like legal parts.

Karrie: Yeah. Just like our programming, right? We got a library and then whenever we need any packaging out mix and match to do our project, right? So I’m trying to do and also looking to talk to different people about the different expertise as well. But talking to you is great because you have a lot of data scientist people that are around you, but the one that I need help the most is about I know a lot because local small charities are very difficult to find because they have no money or know people to do marketing. So another thing I would love to help is that I would like to talk to people that come from a local charity that has gone as well to find out what is the best way to move forward? And also, like I said, we as a data scientist, we are working in data that is great, but we don’t understand the charity sector so it would be great as well that you have them on board to tell us that, ‘Well, from our point of view, how local charity work as well’. It will be really beneficial.

Alexander: Yeah. So if you as a listener, engaged in such a community, if you’re working with a charity, reach out to Karrie, connect with her, you’ll find everything on our homepage, The Effective Statistician and send this link to her LinkedIn page and her company. And so you can, you know, contact her to see whether she can help your community or whether you want to help with moving forward. This charity was supporting work. I’m really, really excited about this because this is a fantastic opportunity to give back in a very very impactful way. So that, you know, all of our charities around the world can work much more effectively. Thanks so much for that. If someone, kind of, comes to know you, how do they best do that? Should they kind of send you a CV? Or what would you need?

Karrie: I think at the moment, I was like if you drop me an email and then we can discuss what is the best way to move forward. Because like I say at the moment, I do have some projects on the go that I need help. But the thing is, it is easier to talk, let’s say, not one-to-one but easier to talk to in the email to find out that. If you can email me, tell me what kind of expectation you got, what kind of tool kit that you use or what like, another major thing for me, everyone has a different priority on charity. So some like a demo[2] , some like children, some like, you know, elderly people. So if you can, also let me know what type of charity that you want to have, that would be great. Then we can do some, you know, mix and match, you say that, well, if I know that you are interested in children, that is like a children project that I’m doing. I would definitely, you know, contact you and say, ‘hey, there is some help for children if you, you know, are closer to your heart’.

Alexander: Yeah, I think that’s the other thing, you know, you’re building kind of a network of people that help with that. So even if you know, maybe at the moment you don’t have time for it, but you’d love to work on this in the future, yeah? Still drop Karrie an email and connect with her so that she can reach out to you in the future when she is available. Yeah, because you know, these projects come and go over time and maybe there’s something in the future coming up. And of course, kind of, if you’re working with a charity, same way. Reach out to her.

Karrie: Yeah, definitely. Thank you, exactly.  Not just today, but he’s talking about the future as well. So it would be great to know you now rather than later.

Alexander: Yeah. Yeah, very good. Thanks so much Karrie. It is really awesome that you’re spending time on such an initiative. I found it absolutely wonderful. And as we talked I’ll continue to support you with my knowledge, which is maybe more on the marketing and external communication side as well as kind of, how to move over all these businesses forward from a strategic perspective. And I’m so glad that we got connected through a common colleague.

Karrie:  Yeah, that is amazing and really cool. Sometimes, it’s like you say, we don’t know, right? And by talking to different people, we know what we can act on and make sure that the course will be getting better. It’s  really cool. Thank you so much for having me today.

Alexander: Thanks so much. Karrie. All the best for this year. I hope that you build an amazing company around this goal. And yeah, maybe we can check in next year to see where you are going and what are the success stories.

Karrie: Yeah cool, that would be great, yeah. Hopefully, I got some case studies that we can talk about.

Alexander: Yeah. Thanks so much. This show was created in association with PSI. Thanks to Reine and her team, who help  the show in the background, and thank you for listening. See you in Gothenburg. I’m really looking forward to the PSI conference. So reach your potential, lead great science and serve patients. Just be an Effective Statistician.

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