In this episode of the McKinsey Global Institute’s Forward Thinking podcast, Janet Bush talks with British mathematician and technologist Anne‑Marie Imafidon, co-founder of Stemettes, a social initiative dedicated to inspiring and promoting the next generation of young women in STEM sectors.
Imafidon invokes the “herstory” of stellar female technologists, such as Gladys West, who contributed to the development of GPS; Hedy Lamarr, whose work on frequency-hopping spectrum technology enabled Wi-Fi and Bluetooth; and Stephanie Kwolek, who created the first ultra-strong synthetic fibers, Kevlar being the best-known. In a rallying call for inclusion in a technologically driven world, Imafidon talks about persistent bias in data collection and algorithms that are making very big decisions that affect large parts of people’s lives. The stakes are very, very high, and we need to get this right, she states.
Janet Bush (co-host): So Michael, you are deeply embedded in the world of tech, and I suspect you will be very good at answering this question—actually a lot better than a lot of people. So who are the top female tech pioneers who have changed our world?
Michael Chui (co-host): Oh wow! Well, I don’t want to disappoint you. I am a little worried because there are a lot of them. If you go back in history to the 19th century, Ada Lovelace is considered the first computer programmer. And speaking of programmers, there was that book and the movie called Hidden Figures that talked about some women—their names were Mary Jackson, Katherine Johnson, Dorothy Vaughan. Their job title was actually “computer,” but when electronic computers came to NASA, they were some of the first programmers. And speaking of NASA, Margaret Hamilton developed the on-board software for the Apollo program. Then there is the legendary Grace Hopper. She co-developed the computer language COBOL and the first compiler. And even in the present day you have computer scientists like Li Fei‑Fei, Joy Buolamwini, and Deb Raji; they are leading in research on topics like artificial intelligence. But that’s only a few. I have missed many of them, but to be honest we should have far more.
Janet Bush: And I do wonder where people are in general about the groundbreaking—actually world-changing—work that these women have done and are doing. And that’s why I am so excited to talk to one of the liveliest women in tech today in our latest podcast, and she talks a lot about the “herstory” of women in tech.
Michael Chui: I am really looking forward to this conversation. Let’s take a listen.
Janet Bush: Anne-Marie Imafidon is a mathematician, a technology whiz, an author, and an advocate. She’s often been described as a prodigy. She was only 11 when she got her A level in computing, the youngest girl ever to pass that exam in Britain. And she was only 20 when she got her master’s in maths and computer science from the University of Oxford.
She worked for a number of investment banks but then in 2013 set up Stemettes, an organization championing girls and nonbinary folk in STEM. In 2017, she was awarded an MBE (for our non-British listeners, that’s a Member of the Order of the British Empire) for services to young women and STEM sectors. In 2020, she was voted the most influential woman in tech in the UK by Computer Weekly. She has a fistful of honorary doctorates, an honorary fellowship at Keble College, Oxford, and is a visiting professor at the University of Sunderland. And she’s in her early 30s. Anne-Marie, welcome to our podcast.
Anne-Marie Imafidon: Thank you. Thank you very much, Janet. Hi, listeners.
Janet Bush: Listeners are always fascinated by how people forge the careers they do. And you tell a story in your forthcoming book, She’s In Ctrl—and by the way, “control” is like a keyboard “ctrl”—which suggests that your journey started with Little Red Riding Hood. Tell us more.
Anne-Marie Imafidon: It’s a classic story, isn’t it, Little Red Riding Hood? And for me, one of my earliest memories actually is playing on my dad’s computer—playing as much as you could have when I was four years old—in Word and wanting to type the story of Little Red Riding Hood but thinking, “Purple is a better color. I prefer purple. And I think the story should center on the color purple instead of the color red.” And typing, “Little Purple Riding Hood.”
It was probably gobbledygook. But I knew what I meant as I was typing it, saving that in the computer, having to go to bed as you do as a four-year-old, and then coming back the next day and being astounded, excited, proud, happy, all the positive emotions you can think of that my story of Little Purple Riding Hood was in that computer.
It’s one of the things that I still love about technology till today. The creativity that you can have, the things that you can evolve, you can shape, you can make that live on outside of you because of the repeatability, I guess, of that technology. That if you dug out that computer, wherever it may be these days—probably not plugged in somewhere—waited ages while you switched it on and while Word 3.0 loaded up, you’d see a glimpse of four-year-old Anne-Marie in that computer, living on through the gobbledygook version of Little Purple Riding Hood.
Janet Bush: Excellent. Tell us a bit more about how you grew up, where you grew up, and your family. Because it seems to be a very high-performing family, from what I’ve read.
Anne-Marie Imafidon: There are all kinds of labels, all kinds of titles. But I grew up in East London, and I’m very proud of that. I still live in East London now and have always lived in East London. I’m the eldest of five children born to Nigerian parents here in the capital.
For us, growing up, education is very, very important to Nigerians anyway. It’s important to a lot of people. It was very important, so Nigerians are a highly educated population. And for us, at home, we were given a really positive, really—you could call it progressive environment.
It’s one of those things. Whatever you’re going for in life, what’s your normal? Nobody really knows what normal is. Because you’re living your version, right? In our house, the norm was, we’re four girls and one boy. And so there wasn’t really anything that was for boys, because he turned up at the end.
In our house it was like, “Try whatever it is. Give whatever it is a go. As long as you’re being honest and you’re being good and you’re not being destructive for destructive purposes in themselves.”
We were really fortunate in that we were allowed to try a lot of things, allowed to do a lot of things, allowed to—famously, I say this all the time—“take apart the VCR player to understand where Timon and Pumbaa were coming from.”
Because how does it know? You put one black rectangular object into another black rectangular object, and Timon and Pumbaa show up on the third one. Where did they come from? Because I didn’t put them in at the beginning.
It meant that that exploration, that curiosity that was indulged for us at a very young age, grew into this love of learning, love of understanding. For me personally it was about just understanding how things work. And if that logically follows on from that, then that logically follows on from that.
I can learn it once and maybe apply it. But of course me being the eldest of five meant that the norms for the other children that came behind me, for my siblings, for my sisters were, “Well, Anne-Marie did that. And we’re in the same place. And that’s normal for us. So why can’t we do it? We grew up in the same place, came from the same place, have the same parents, have the same access to opportunities.”
So if I wanted to do the two GCSEs [exams taken in Britain at 16] younger, which was what my younger siblings did, some at seven, some at nine, which you’d normally do, I guess—for non-British listeners, you’d normally do at 16, I’d done at ten. Someone else was like, “They’ll do it at nine.” Someone else did it at seven. I’m pretty sure somebody did it at six. It was just that, “Well, why can’t I? I mean, if she’s doing it, what’s the difference between her and me?”
Janet Bush: It just shows you the power of a role model, and their role model was in the same family.
Anne-Marie Imafidon: I think it’s definitely that. It’s so interesting that you say that. Because I think often we think of quite a distance, I guess, between us and role models and us and mentors. And I think it’s a real good example of, role modeling can happen anywhere.
Everyone’s got that sphere of influence. You don’t have to be the big C-suite or the big exec. I know a lot of these are, but there’s a lot of role modeling that can happen that doesn’t have to be all about traditional hierarchies or status and titles that people have gained or have earned.
Janet Bush: Exactly. Your biography says that you won a scholarship to study maths at Johns Hopkins at the age of 13. Did you actually go to the US?
Anne-Marie Imafidon: I did. It was definitely a life-affirming moment. And it’s something I still yearn to be able to provide to young people that we work with now. Just have the opportunity to go somewhere different, to do something different, to learn something different from what has been predefined and what the system does and the system says.
I am a real believer that with a lot of people, it’s opportunity that they need more than motivation or any of the other kind of things that we talk about that we ascribe success to. Being in that place at that time or having those environments to go out and do a degree content early or do a cyber certification early, which is what we do at Stemettes, or Python certifications early or Agile certifications early.
Janet Bush: When you were in the States, I could only imagine that being young, gifted, Black, and a woman might have been a bit difficult with, maybe, the more unenlightened. Did you come across such attitudes? Because you’ve coined this great word, “misogynoir,” which combines misogyny—
Anne-Marie Imafidon: I didn’t coin that.
Janet Bush: —and racism.
Anne-Marie Imafidon: No.
Janet Bush: You didn’t. But I love it. I love it. But how much of that did you experience in the States? Or if not in the States, subsequently? Because I imagine that this has spurred you on.
Anne-Marie Imafidon: There’s quite a lot of things in my experience that I can’t say, “Oh, when I wasn’t Black, young, and female, that happened. But now that I am Black, young, and female, here’s how it’s played out.” There would have been lots of people who would have not believed I could do things, would have gotten in the way.
Sometimes they might have ended up literally saying no to my face or staring in disbelief that I just solved this database problem that they’d been trying to work on for months and feeling like they’re on a hidden camera show and having words with my manager. “What do you think you’re playing at?” “Why did you leave her in the room?” “She made me look this way.” Dadadadada.
And I think it’s one of those things that it’s only when I’m then able to almost compare notes with other people that have gone through very similar experiences. And you almost need the bigger sample size than one to be able to call something misogynoir, or to be able to note that something was based in racism or note that this is something that’s happening systemically and isn’t just happening to me.
I think it’s something that almost is—you can call it a luxury or not. But often it’s kind of like, OK, cool, I can look back and say, “Huh, was that because I was Black or because I was young or because I was a woman or because I was from East London or because, because, because?”
Or these are all things I cannot change or will not change. I can’t change my race. I’m not changing my gender. My age is completely out of my control. What I actually have to do is say, “OK, cool, if this person, for whatever reason, that it’s more about them than it is about me, doesn’t believe that this is an opportunity for me or doesn’t want to follow along or doesn’t whatever it is, I’m working hard enough that I can go to the places where I can be celebrated. And I can be heard rather than just ending up clinging to places where I’m going to be tolerated.
Janet Bush: I want to talk about Stemettes. But before I do, I want to talk to you about Countdown. And it’s a very big deal in the UK. For our non‑UK listeners, this is the longest-running quiz show. And it was such a high-profile thing for Anne-Marie to do. The mathematician or the arithmetician on the program becomes a celebrity. And so Anne-Marie has attracted a lot of attention for this stint [as the math expert] on Countdown. But on Twitter, there were people saying, “Oh, she only got that job because she’s Black.” And I just wondered how uncomfortable that was for you. And maybe it was something new for you.
Anne-Marie Imafidon: No, no, no. Gosh, no. I mean, I’ve been Black for more than 30 years. So no, not new. And I think it’s one of those things. Sometimes you do look back and you unpick. And you’re like, “Yeah, there’s a little girl that turned up. And they really weren’t expecting that.”
It’s been really interesting to kind of sit at the center of this and be like, “Yeah, you all, I knew it was going to happen. I’m kind of not surprised it’s going to happen. It normally happens in public—in private, sorry, rather than happening in public.”
I do a lot of things. And I know what I do is important. And I know that it’s making a change. But to have it presented almost in argumentative fashion—how dare you? “She did this and she did that.” And “she’s overqua—,” “you know, she should be prime min—” and all this kind of overwhelming avalanche of support which, when you reflect on the flip side in private, that’s something that may have happened to me.
But I’m not the only person that’s Black and female trying to get on in professional spaces and in the media and all these other spaces. And not everybody gets those flowers. Not everybody gets that as a benefit, which is why I talk a lot about the privilege that I do have.
Janet Bush: It was very absurd.
Anne-Marie Imafidon: Absurd. But it is one of those things that, sadly, is a norm. It’s definitely a part of our current norm.
Janet Bush: I think we’ve got a flavor of you as a person. But now I really want to talk about Stemettes. This is the organization you set up some years ago. Just tell us what Stemettes is about.
Anne-Marie Imafidon: I set up Stemettes in 2013. It’s an organization, a kind of mission. The way we describe it is, we’re set up to engage, inform, and connect girls, young women, and nonbinary young people with the STEM and the STEAM fields. STEM is science, technology, engineering, maths.
STEAM is the truer name, actually, the proper evolution of that term, which is science, technology, engineering, arts, and maths. And we’re doing that by showcasing a diverse set of role models, which we’ve already kind of touched on a little bit.
And so for us it’s Stemettes. We are giving those opportunities to those young people to make an informed decision on their own relationship with the field of technology. In the broadest sense, whether it is engineering, whether it is mathematics, whether it is computer science, whether it is any of the sciences, there is a lot of innovation at the moment. Most innovation, you know, was driven by STEAM and that idea of mixing the science and the technology and evolving things and getting very creative and solving problems using it.
It’s something that when we look at media representations, when we look at the history of STEM, that misses quite a lot of the herstory of STEM. That actually is pretty tough, if you’re a girl, a young woman, a nonbinary person, to look back on. No offense at all to them, the dead white male scientists, the dead white male technologists. We overwhelmingly sold that story, sold this history, sold this narrative that if you’re not dead, white, male with a beard, you can’t contribute to these things. And it’s quite frustrating for the girls and the young women.
And for those who maybe will end up being dead white males with beards, having to then understand that actually this isn’t a space that’s solely and purely about you. You’re not the one definition of success. You’re not the only type of value that we can have in this STEM and STEAM field.
Stemettes is about doing what we can while they’re young, to help form their ideas and give them a little bit of staying power and slightly different motivators to enter into the field and stay in the field. I think it’s something where we have to lift up and have a bit more perspective and say, “This next generation is not involved if they’re not making technical decisions, if they’re not considered, if they’re not pushing to be there.” And if they don’t have this wider sense of the legacy we have, that we need to all have.
We need to embrace this technology while it’s here so we don’t end up with Terminator 2, Black Mirror, any of these dystopian views that we have of robots marrying our grandchildren or killing us by accident. There’s all of these things that could happen, which come from science fiction.
But they’re not inevitable. And actually, if you’re not able to stay because of the experiences you have, there’s a new generation that can come up, that can come together. So we work a lot on cohorts of young women coming through together. And when they face those issues and face those problems, they’re built differently.
It’s something that we see so much with our Stemettes and our alums who will come through programs. They’ll be on a mentoring program. They’re getting new certifications. They’ll come along to the panel. They’ll go and work at such-and-such company.
They’ll drop me a note, and they’ll be like, “Hey, Anne-Marie, so I’ve stopped working at that company. I’ve quit being at a company. But I’ve started my own company. And this is the funding we’re going to do. And I just wanted your advice on, should we focus on this or focus on that?”
And I was like, “Hang on a second. You’ve left? Let’s rewind.” And they’re like, “Yeah, yeah. But that doesn’t matter.” And it’s one of those things that I feel I genuinely have not seen a generation ago. It would have been, “No. I left because that wasn’t working for me. It wasn’t serving me.” There was no alternative. Or it didn’t seem like there were alternatives. Where this new generation know they have the value. They know they can have alternatives. And if not, they can create those alternatives.
“This is a tool I can use. And so whether the industry likes it or not, I’m going to come here. I’m going to thrive. And I’m going to use this STEM and this STEAM as tools to do what the adults didn’t do.” That’s what I love so much about what we’re doing at Stemettes, is building that network, building that community, empowering them with opportunities and really shifting their norms in a way that you know that is the countdown to the Stemettes legacy.
It’s the countdown to them being the CEOs, being the COOs, having alternate industries that they kind of spin up out of thin air with the tech industry having to grapple to keep up with these Stemettes who are no longer Stemettes at this point (they’re women in STEM) coming through and really changing things up and taking control, which is the title of the book.
Janet Bush: And if not now, when? McKinsey has done a lot of work on the future of work, on automation, and also on the very unequal prospects that that entails for women. Those on low incomes, anybody with low skills. So this is the time. If you’re going to empower women and young girls to thrive in this new world, this is the time to do it.
Anne-Marie Imafidon: One hundred percent. I’m a trustee at the Institute for the Future of Work. And I know there are definitely big overlaps in bits that we’ve all been working on in this space. I think there’s been so much more that’s been accelerated during the pandemic that actually this was always going to be the time.
It’s partly why I’m involved with the institute, where when we talk about the future of work, these are the people that will be in that future. And so I’m really, really conscious that they will need the skills.
But the industry and the workplace also need to evolve so that then it can hold them, it can carry them, it can satisfy them. It can actually have people in that workplace. I think there’s a lot of trends that we did have and things that if we’re not careful will actually roll out of the pandemic.
Where not having flexibility or not being able to take into account the social context in which technologies are applied, or even with the institute, looking at this notion, because there’s so much work. This is almost in its infancy, this whole field of us really concentrating on what this looks like and the future of work.
At the moment, we’re looking at the individual level. But above that, there’s what that means for the company and the way the company can operate, the way the company can evolve, the way the company can serve its clients and work with its supply chain and the rest of it.
But even more so, no company is an island. You’re also part of the society. You’re part of the economy. You’re part of something wider—and even if just the idea that the gig economy has come out of certain companies operating in a particular way, it doesn’t have to be negative.
There are other things that we can do that we can evolve out of, that consideration for the individual, consideration at the firm level, and then consideration at the societal level to say, “OK, cool, the future of work is so linked to the future of life, the future of socializing, the future of spending, the future of retail, the future of all these other spin out things.”
We need to have different types of people feeding into this, making these decisions with the checks and balances, with the audits, with the norms that we’re setting, so that we don’t multiply up the poor decisions that are made. That’s basically what technology is good at doing. It’s good at repeating, which is, again, Little Purple Riding Hood. It would have been really good at distributing Little Purple Riding Hood.
There’s actually poor decisions we make in hiring, poor decisions we make with recruiting, poor decisions we make with workforce management. Do we really want to multiply those up and create that Terminator 2 scenario inadvertently? Or do we want to be measured? Do we want to be honest with ourselves as technologists? I don’t think enough technologists are honest or are humble enough to say, “Yes, this is a powerful tool. But just because we understand the tool does not mean we understand its applications or the implications of it in its entirety.”
I think that’s the power of something like STEAM. We’re able to value different skill sets, different disciplines, different perspectives, and then build something that then is additive and does better, rather than something that basically repeats the past. And let’s be honest, for some people the past was great. For a lot of people the past was not great. So actually that’s not really where we want to start with in building the future.
Janet Bush: In your book, you have some really good illustrations of the bias in algorithms. And I just wondered if you could share a couple of those, because they were very arresting.
Anne-Marie Imafidon: Quite a few of these—I think the ones you’re referring to—quite a few of them have come from the work that we’ve done at the Institute for the Future of Work. Whether it’s biases in job adverts and often, when we talk about bias, there’s almost two levels of bias.
There’s the technical bias that comes in because of, what is a fact? What is a statistic? This is a thing, dumb statistics. There’s bias that comes in because when that statistic was collected, often it wasn’t with your algorithm or your technology in mind that that statistic was collected.
There’s bias that comes in from just data collection and statistics and all of that in itself. But also bias in terms of the why you’re doing something and the context. And who has asked for it to happen? And so there’s going to be bias if it’s the police that’s asking for it, versus, I don’t know, different actors in a group that’s asked for this particular piece of technology.
In the book and from the Institute for the Future of Work, our machine learning case studies or papers that we’ve worked on, there’s three examples that I pull out. One is around job adverts. And this is this notion, this idea that when you’re advertising for a job, especially days like this, when you’ve got a slightly better understanding of the wording in the job advert.
For example, there’s a big lead on who applies or where it gets shown is also very important on who applies. There was one algorithm—well, social media, actually, an advertising algorithm that was used by a company who know that they couldn’t contravene the Equalities Act, and so couldn’t choose gender and ethnicity and all these other things as part of the criteria.
But they could choose (and this is prepandemic days, of course) location radius and other factors of success that they wanted to put on this job advert. They then put this job advert out via the social media network which used its own algorithm to decide which users to show this job to.
What the social media platform had as success, and what the potential employer had as success, didn’t quite fully match up, which meant that the job ended up being shown to a particular gender, a particular age, all the things that would been the protected characteristics, but then also ran counter to what they were trying to do with that job, with getting the most people as possible to be able to see it so they can get different types of candidates, and hopefully the best candidates across any kind of demographic to then apply for the job and the role.
So that’s one example. Second example is around hiring. And this is another one where it’s the statistics and it’s the intent and it’s the purpose and the bias that you have in implementing these technologies.
But they took their own definition of success in this organization. And they looked across the people that were already in the organization. They looked at their social media profiles. They took all this other data that they crunched. And they essentially said to the algorithm, this is what successful candidates look like.
They used customer reviews as well, words that were used in customer reviews. And they fed that into the algorithm. And this algorithm basically didn’t refer any women through for the hiring process, because the definition of success was incredibly male.
And whether that was in the customer reviews, whether that was what was in internal performance reviews, whether that was the makeup of the organization, it meant that statistically, the algorithm couldn’t tell what successful women look like. Because they didn’t see one already. That’s not part of his data sets or his data points. And so no women were hired for that.
And the final example is workplace management. And this is the one where we have to be really mindful about intentions versus the data.
This was a retail example. A company had brought in an algorithm to help them manage the workforce, pick shifts, pick promotions, pick pay. And they took this data from—they looked at workplace chat. They tracked where people were on the shop floor at what time of the day. They looked at facial recognition and the rest of it. And this algorithm ended up having these inequalities reflected again in the pay of people, the promotion of people, the way the shifts were allocated.
But when we looked a little bit closer at what had been pitched and what this software was sold in to be able to do, it was kind of like, “You know, we want to pay people as little as possible to not let them leave.” And if that’s how you’re deciding pay, that’s not going to be fair.
There’s a lot of things we have around pay, especially in the UK, we have gender pay reporting, that we know there’s a gap. And so actually that’s probably not really how you want to be optimizing your technology if that’s really something you know is now law.
It’s been the law since the ’60s. But it’s also very highly regulated, and we all now have to report it. I can’t blame the statistics on that. You can’t blame the technology really on that. You can blame your assumptions, what you wanted, your measures of success for your algorithm.
And so it is this—all these things. And that’s just those three use cases across three workplaces. When we then magnify it up with all the other things that happen around restricting, rating, recommending, allocating, all the other things that we get algorithms to do, you then end up multiplying up and seeing, “Goodness, there’s quite a lot to unpick here.”
Where when we talk about bias of algorithms, that’s a really big heading for quite a lot of things that technologists need to think about, that senior leaders need to think about, that workers need to be part of the decision, part of the process.
How do we get ourselves to that point, where we’ve got that basic digital literacy where we’re able to? Because these algorithms are making really big decisions that really affect big parts of people’s lives. The stakes are very, very high for us to get this right.
Janet Bush: I just wanted you to share some stories about your Stemettes graduates, your babies gone off into the world. Without naming names, I’d just love to hear a couple of wonderful success stories.
Anne-Marie Imafidon: We’ve got so many. One example of a young person who was on one of our programs back in 2014, actually, who now has ended up graduating from university. Did an engineering degree at Warwick. Normally it’s four years. And she’d been along to loads of events. She’d been part of our—we had a start-up incubator as well that she was part of. She’s been on stages. She speaks all over the world now as well, around engineering and the rest that she does.
She reached out to me and said, “OK, I’ve got this quandary. I’m in my third year of my engineering degree. I’ve just done my placement year. I’m supposed to go back and finish my master’s. But you brought me along to this Institute for the Future of Work workshop that was going on. Because I like to kind of connect the dots with a lot of the work that I do.”
And she said, “I met these policy people. And they’ve stayed in touch. And actually they’re offering me this job to look after science policy at think tank X, Y, and zed. What do I do?”
It’s so funny. Because yeah, actually, it’s a really big question, isn’t it? Because we talk so much about STEM. But actually STEM policy of course is part of that. And you’d think I’d say, “Go and finish your master’s. You have to finish your master’s.” My parents probably would have said, “Finish your master’s.” But actually, if you finish your master’s, you could also have this job at the end. Because you’re definitely qualified to do it now as you would. But why can’t you go ahead and do that?
So that’s what she now does. She heads up policy at this organization with her engineering degree. All the other work that she’s done with us, or the other work she’s done outside of us. And this is a real powerhouse. I always say, “One day, we’re all going to work for her.” Because we will. She will be prime minister. She will run the country. I’m sure of it.
It’s people like that, or whether it’s younger people or other people who have gone on, like the young person that we work with who went through one of our mentoring programs, ended up working at one of our partner employers, ended up actually saying, “Do you know what? I’m going to start my own thing. And here’s all the reasons that I’m going to start my own thing.” She is now running a VR start-up based off experience that she had with her grandfather who had, I think, dementia. There’s elements of what she’s doing that she’s taken from her own personal perspective that she’s using to work on that.
So many of them. There’s another young person. I was there the day that she decided not to—her parents had really wanted her to do medicine, as most people, right? It’s a good field. I have nothing at all against medics and doctors. Of course, it’s part of STEM. But it’s the one where we don’t quite have the underrepresentation that we do in the rest of STEM. So I was there the morning she decided, “I’m not going to go to university. I’m actually going to go into this tech thing. I don’t have to be a doctor to help people.”
She ended up doing an apprenticeship and working her way up in another tech company, working really well, building her career. And so she was like, “You know what? I really want to give back to the organization that helped me get to where I am today.”
She worked with her internal teams to get that company to come on and be a partner of ours. We then run school trips, different events with them. And she turns up. And she’s like, “Do you know what? There was a time I sat in your seat. And here I am. I’m going to speak to you for half an hour. And then I need to go off and do things. Because I’ve got real work in this company. And if I’m not there, it doesn’t happen.” That, for me, is the real example of what success and what our legacy looks like.
Like I said, these are young people. These are people. These are industry. These are professionals who are going in. And their success is not just on them and how they climb. But it’s on who they’re bringing up with them. And that’s the kind of legacy that I’m proud to be able to look back on, to be able to see and to know, year on year on year, there are young people that are coming through, that are opening opportunities.
Final example I’ll give. We’ve had a couple hundred young people since just before the pandemic. We did two pilots in the pandemic. Pandemic wasn’t going to stop us. We did it completely virtually in the summer of 2020 and again in 2021.
One of them had ended up on the program learning Agile with us. She stayed on with the qualification provider, joined an apprenticeship with them, and has ended up being the youngest Agile Scrum master in the world, working across lots of different projects, coaching teams who are much, much, much, much, much older than her.
But her, again, being able to hold her own. Because she’s had that formative experience that she knows what is possible. She knows that there’s value that she brings. And she knows her stuff. You know, let’s be honest.
Janet Bush: Just tell me what a Scrum master is.
Anne-Marie Imafidon: Sorry. A Scrum master is someone—Agile is a new system of working through technology projects. It used to be you built a tech project like you build a house, maybe. You did the plans. You did all of it. And at the end you’d look back and see what it’s like.
Whereas technology is not quite a house. It means that you build iteratively. There’s a whole framework of ways to run these projects and different elements of it. A Scrum is one of the loops that you have, every two weeks, I think it is.
You then are the Scrum master. You look after that two weeks, the organization of what’s going on. It’s almost like project management. But anyone that’s listening knows that Agilers will be wincing a little bit. It’s not project management. It’s product management. That’s what she does. She’s in control for that particular tech company in terms of how they work through and how they build out their tech products.
Janet Bush: Love that. Love that. We’re getting towards the end. I wanted to ask what’s next for you and what’s next for Stemettes. What’s the plan?
Anne-Marie Imafidon: With Stemettes, our big thing at the moment is pushing for a bigger and a higher-level societal shift in the social norm. There’s lots of programs that we run. There’s lots of work that we do with teachers, with parents, with careers leaders and the rest of it. We’re scaling that up as much as is possible.
The other big thing that we’re really, really working very hard on is—you know, the policy isn’t the answer. It’s not the silver bullet. But how do we influence and change the social norm using policy, using white papers, using all kinds of different levers for us to take the things that we know are very normal at Stemettes and allow those to be society-wide, education-wide, norm-wide things that are done?
We’ve started. We actually have just come to the end of a four-year project with the Institute of Education funded by the Wellcome Trust and the National Science Foundation looking at equity and equitable practice in STEM outreach and youth justice–oriented approaches.
And that’s kick-started a load of work for us. There are a lot of things that we do that are codified as part of the Stemettes way of working that actually could work industry-, nation-, education system–wide. Trying to pull some of those levers and leverage that influence so that we don’t have to exist.
I think ultimately that’s what I’m aiming for. I don’t want to still be doing this in X years. I don’t want to still be doing this. I don’t want to put a number on it.
Janet Bush: You’re working for your own obsolescence.
Anne-Marie Imafidon: Exactly. That’s where we’re heading for. We want to be obsolete.
If you’re creating resources, have female role models. It shouldn’t just be dead white guys with beards, which is fine. We’ve got a lot of those resources already. But there’s so many women, whether it starts with Gladys West with the GPS; whether it’s Hedy Lamarr with frequency-hopping spectrum technology, which is why we have Wi-Fi and Bluetooth; whether it’s Stephanie Kwolek, who is the reason that we have Kevlar.
There’s such a rich herstory that if we’re able to break that into so much more—quite a lot of that confidence issue, quite a lot of the issues that we end up seeing, not knowing what success looks like, and only having one vision of success—a lot of that would disappear and would go if we would have these names on the tip—we should thank Hedy every time we’re able to connect to Wi-Fi. Imagine having that on the tip of your tongue.
No one needs to tell you that women do tech or women do STEM. Because you’re on your Wi-Fi because of one. There’s all these small, small things that when you add up make a really big change, make a really big difference. And that’s what the science in tech, that’s what the STEAM world, the STEM world really needs. And so that’s what we’re going for next. Obsolescence.
Janet Bush: We sometimes like to end these podcasts with some rapid-fire questions and quick answers. So—
Anne-Marie Imafidon: Hit me.
Janet Bush: You mentioned food. What’s your favorite food?
Anne-Marie Imafidon: Oh, gosh, there’s flips. At the moment it’s biang biang noodles, which I make. I make them from scratch. I love it. It’s one of my favorite things to eat. So there we go. That’s my fave.
Janet Bush: What’s your favorite thing to do outside of work?
Anne-Marie Imafidon: Again, this changes. I do watch a lot of television, like, people don’t believe how much TV I watch. At the moment, though, I spend a lot of time in Animal Crossing on the Nintendo, which I don’t know if that says a lot about—you’re trying to escape to an island where I can just build houses and plant things. And just look for iron and clay and build things from raw materials. That’s what I spend a lot of my time doing in the evenings.
Janet Bush: What’s on your bucket list?
Anne-Marie Imafidon: Oh, I want to learn Punjabi. Languages were such a big thing for me growing up. I learned so many—I enjoyed collecting languages and just learning grammar. And you learn so much of the history of the people as well through their language. And so Punjabi is next on my bucket list.
Janet Bush: And finally, if you had one piece of advice for listeners of this podcast, what would it be?
Anne-Marie Imafidon: I’m going to put two in one if that’s OK. One is to have a growth mindset about anything technical. I talk about this a little bit in the book as well. As much as it’s nice to say you want to be the expert in X, especially if that thing is technical, it’s going to keep evolving. And so actually you don’t want to be the expert. You just want to know more than you did yesterday, more than you did last month, and more than you did last year. So having a growth mindset is fantastic. And that’s much easier to do when you’re part of a learning tribe.
Finding a learning tribe that you can apply that growth mindset and learn from as you learn enriches your learning so much. And that’s the way we’re all going to grow and maintain this technical and this digital literacy to ensure that it’s not just the computer scientists controlling what happens next. It’s something we can all do collectively. So that’s my two-in-one, two-for-one on advice there.
Janet Bush: Democratizing technology so that none of us get left out.
Anne-Marie Imafidon: That’s exactly what it is. None of us get left out. And all of us have some sense of agency in what happens next. Because I think that’s what is missing at the moment in such a big way. It’s almost like when you see a car crash happen in slow motion. Or just before it happens, it’s like, no, before it happens I’m just going to just know. Having a computer science degree should not be a prerequisite for being one of the only people in control of what happens next.
Yes, it’s about democratizing. It’s about giving folks agency so we can make collective decisions about what happens next rather than it being that technical power race. I don’t know if we can call it race to the—I’m going to call it the bottom, race to the dystopia, yeah.
Janet Bush: Race to the dystopia. Well, on that note, Anne-Marie, it’s been a delight talking to you. Thank you very much indeed.
Anne-Marie Imafidon: Thank you. Thanks for having me.