This session was recorded in NYC on October 22nd, 2019. In this session, members of the data science community discuss the role of women and the diversity within both the data science industry and H2O.ai.

 

Panelists:

Erin LeDell, Chief Machine Learning Scientist, H2O.ai; Niki Athanasiadou, Data Scientist, H2O.ai; Shar Rubio Executive Director, Head of Portfolio Management and Project Assurance; Rabobank Josie Williams Research Assistant, NYU Medical Center

 

Moderator:

Ingrid Burton, Chief Marketing Officer, H2O.ai

Read the Full Transcript

Ingrid:

Thank you everyone. Well, thank you to the panelists for joining us here today. I can tell the wine is probably starting to flow or the beer, and we’re ready for that too. So if anyone would be so kind as to bring us a bottle and a couple of glasses, we would love that, but we’re here to talk about women and inclusion and diversity in tech today, which is, a topic that I think is near and dear to all of our hearts. But I think also at H2O, where I’m from H2O.ai, Sri Ambati, our CEO who you saw earlier today, this is very important to him. So you’ll see a number of us here from H2O.ai, Erin Ledell, and Nikki there at the end, who represent us. And we have one of our great partners here, Meg from Intel. We’ve got Josie, who’s a student that I met a couple of months ago now, and Shar, who I also met a couple of months ago.

And I said, “Join me in this panel.” So what we really wanted to discuss today is first of all, I’m going to let you guys introduce yourselves and talk about what you each do. And then we’re going to talk about some of the tough questions and we want to open it up to the audience as well, to help us help you get more insights into this topic because it’s not just a #MeToo thing or we’re mad. No, we want to solve the problem. We’re in machine learning and AI. We want to solve problems. So with that again, I’m Ingrid Burton. I’m the CMO at H2O.ai. I’ve been in the tech business for decades and have been both a developer and now a marketeer. So I was a developer very early in my career, now a marketeer and do marketing around tech companies of all shapes and sizes. So H2O is my favorite so far, but I worked at Sun and Hortonworks, SAP, the list goes on. So I’m here to help have the discussion. And Shar, I’ll turn it over to you, a little bit about you.

 

Shar:

Thank you, Ingrid. First of all, thank you for H2O for inviting me to participate in this great event. So I am Shar Rubio, and I’m currently leading the portfolio management and project assurance group at Rabobank North America, which is in New York City. So Rabobank does not rob banks. I have to be very careful when I say that. It is actually a Dutch bank and it specializes in providing capital to the food, beverage and AgriFinance industries. And my awesome team and I are responsible for providing governance to all of the projects in our North America project portfolio. And we provide analytics and insight or C-suite to help facilitate decision-making. Happy to be here.

 

Ingrid:

Thank you, Josie. T.

 

Josie:

That’s a tough act to follow, but I’m Josie. I’m recent computer science graduate from NYU. And right now I’m looking for positions in researching and fellowships, but to keep my hands busy, I’m working with a team at the NYU medical center where we’re implementing machine learning into healthcare with the hope of detecting renal failure and kidney disease two to three years in advance. So that’s where I am.

 

Ingrid:

So are you looking for a job?

 

Josie:

Yes, I would like to keep… Everybody keep that in mind.

 

Ingrid:

Recent NYU grad. Erin.

 

Erin:

Hi, my name’s Erin Ledell. I’m the chief machine learning scientist at H2O.ai. I’ve been there for about four and a half years now, but I’ve known Sri since, I think, 2013. And yeah, one of the first things I noticed about him was he’s one of the few CEOs that I met that was quite aware of the diversity, let’s call it I don’t know, issue. Maybe that’s not the right-

 

Ingrid: Gap?

 

Erin:

Yeah. Gap. That’s a better word. Thank you. And I’ve always felt quite comfortable at H2O and I think it’s nice to be a part of this panel. And at H2O, what I do is I manage the H2O AutoML team. And I gave a talk about that earlier, if you want to learn more later and that’s about it.

 

Ingrid:

And you also lead our women and… Why don’t you talk a little bit about your community?

 

Erin:

Okay. I can… I didn’t want to monopolize the time, but I’ll mention two other things that I do related to this topic of diversity inclusion. So there’s two nonprofits that I help run. One is called women in machine learning and data science organization or WiMLDS is the acronym WiMLDS.org. And we have about almost, I think, 80 chapters worldwide that are sort of like meetup groups that form local communities to help support growing people, or specifically women’s careers in machine learning data science. And the other is called R-Ladies. So you, maybe if you’ve heard of PyLadies, it’s the, R version of that. So same idea, but just more focused on a particular programming language. My favorite of which is R so…

 

Ingrid:

Perfect.

 

Meg:

Great, thank you so much Ingrid and Sri for having us back here. I’m with Intel, I’m Meg Mude. I’ve been with Intel about four and a half years. I’m an informatician by education. So I started in genetics and computational applications for genomics. That was some time ago. My passion is around data diversity and algorithmic diversity and has been for quite some time, even before Intel. So when we’re looking at language image, other classification systems, machine learning systems, having a good representative perspective on that is important and having well engineered systems is important on that.

So my day job is to do design and design for systems for Intel’s tier one customers that use our specialized computing and machine learning platforms. But that’s what I’m passionate around. And one quick plug, I also do, like a lot of the other ladies here, volunteer work and my area of passion is economic diversity, not just ethnic or gender, but economics. So I mentor young men and women doing work giving them an engineering land. Yeah. So I spent a lot of time doing that. Just came back from Grace Hopper. So if you went to Grace Hopper and didn’t say hi, say hi.

 

Ingrid:

Fantastic. Thank you. Nikki?

 

Niki:

Hi everyone. My name is Niki Athanasiadou. I’m a data scientist at H2O.ai. I am working with customers to help work together, to solve problems. At the same time, I’m working on various aspects of different recipes. I have a passion for healthcare and the adoption of machine learning by health care. And my love affair with data science started during my PhD. Actually I have a PhD in computational molecular biology. And in the beginning I thought I loved biology then I realized I love data and information, and that’s… here we are now.

 

Ingrid:

So this is a really great panel. I mean you guys all have technical backgrounds. I believe you do… You mentioned that. Technical backgrounds and now you’re in AI and machine learning for the most part. And you’re very new in your career. So what advice, let me see, who, what can I pick on? Erin? What advice would you give Josie about how she’s going about getting to the next step in her career? She’s a recent grad, she’s at this great event. What are your breakthrough moments that you could share with someone like this?

 

Erin:

Well, I think Josie is a little ahead of the curve. She’s on a panel at a big conference and has just graduated from school. So I think, I don’t know that Josie needs to hear it, but for the rest of the audience I’ll share some tips. I think communities are important.

I mean, that’s why I work on these different types of communities. I think it’s good to have networks, especially if you’re looking for a job. I think it’s good to just put yourself out there, jump on a panel and say, “Hey, I’m looking for a job.” That’s also helpful.

 

Ingrid:

Being direct.

 

Erin:

Yeah. Being direct can be helpful.

 

Ingrid:

Something that we can work on, right?

 

Erin:

Yeah. I would say I don’t want to of go in that direction too much. Because I think women get a lot of pressure to conform to male behavior and stereotypes. So I want to… That might be helpful, but also honor the way that maybe we do things a little differently or something like that. So don’t necessarily try to be somebody that you’re not, but try to play to your strengths and stuff like that.

So maybe I’ll let somebody else have a chance to give some advice.

 

Ingrid:

Yeah. And I was going to ask Nikki just in terms of like you’re in machine learning and AI and you’re working with a lot of our customers in healthcare. And what is the importance, I mean I think I know the answer, but the diversity angle when you’re working in that kind of environment. You bring what, kind of, different perspective or is it just purely the math and the machine learning?

 

Niki:

So I think I will offer two answers because I think there are different ways to see it. One is providing answers through data and I think that’s absolute and there are methodologies and approaches and that’s genderless, raceless and something that I feel to do it… I think that to do it well, you need to love it and to have passion to do it well.

And then I think what’s important is to however, allow for many voices to speak towards the truth of your model, the truth of your data, problems from your data. And that can come from, by including, by having many different backgrounds talking on the table. It’s about, I think, and as I was preparing about this panel, I was thinking, what is the most important thing that I was, that perhaps is worth mentioning here? Is about that no one is immune from prejudice. No one is immune from biases. We all do it. I challenge you to take the sexism questionnaire online. You would be surprised. I think that what is important is to recognize the limitation or for a point of view, and to allow people to fill in the gaps. And that’s where diversity, I think, is important.

 

Ingrid:

That’s very important, Megan, I’m sure you have a perspective on that too working with a lot of customers. What do you see in terms of machine learning, AI and your perspective?

 

Meg:

Yeah. Great question. I think from an AI perspective and machine learning perspective, independent of whether you’re a man or woman. I think first of all, obviously as Niki mentioned and rightly so, I always get the twirly chair for… So I think what’s really important to consider certainly is yes, in terms of demographic diversity and knowledge, diversity, neurodiversity. But I also would like to suggest that we look at methods diversity. For example, how we’re sourcing data assets or what algorithms we’re using. How we’re mapping the behavior of the model back into the production systems. And I think that portion is a little bit more nuanced and I think there’s going to be tremendous opportunity there.

And if you can bring any domain area, for instance, your passion is healthcare or your passion is environmental applications, whatever, bringing that other knowledge and perspective even of yourself and enriching that is tremendous. You know, Shar mentioned at Rabobank, they do a lot of work with food, beverage agriculture companies. That’s a specialized domain unto itself, not just with machine learning, but a whole industry and how the data itself is public. So I think there’s those two perspectives.

 

Ingrid:

And sitting at a customer site to Meg’s point, where do you see diversity helping with solving problems? Do you see that?

 

Shar:

Absolutely. It is a… You cannot not include it. I think in the marketplace, customers are very eager to see products and services delivered by companies that truly embrace diversity. Including all of their preferences right down to the UI, the way it’s delivered, et cetera. So I think if you don’t include it in your product and services strategy, there’s no way you will survive the marketplace.

 

Ingrid:

And that’s been your experience, not just at Rabobank, I’m sure sounds like, yeah.

 

Shar:

And also in our project portfolio, our banks are very aware. Now we just went through our project portfolio planning process for next year and starting to see a lot of, not just the new technologies, new projects included, but also addressing diversity topics, not just for external, but internally as well.

 

Ingrid:

Yeah, exactly. So that’s fantastic. I mean it’s good to see that one of the things that you shared with me at another discussion we had was that mentorship was really important for you in getting through your career. We have the new person in her career here, looking for mentorship, but tell me a little bit more about, tell us a little bit more about your mentorship journey being mentored. No, no. Sure.

 

Shar:

So mentorship works when you are in that role. I think it’s very important when I’m mentoring or being mentored to be able to have a platform to speak to someone who’s more knowledgeable and experienced in the industry to give me advice and also to speak without fear of judgment. But I’ll add a twist to that. So in the mentorship for it to work, there should clear expectations between the mentor and the mentee, what they want to get out of it.

 

Ingrid:

That makes sense.

 

Shar:

And also to add another level to that, it’s also important that not just with mentoring, but you also add sponsorship to it. I think that’s very important, especially as women that beyond the mentorship, you’re also participating in sponsorship activities. So if you see someone who’s got the talent-

 

Ingrid:

So tell me what the two differences are.

 

Shar:

So mentorship exists when you’re in the role and then sponsorship is outside of that relationship. If you think of someone who might be good for a role and you want to make sure you bring up that person. That’s our distinction.

 

Ingrid:

I think that’s clarifying. So Josie, in terms of what you’re doing and studying right now, you’re working on research projects at NYU, right? And you talked a little bit about it’s in the healthcare space. What brought you from computer science to data science? Tell us a little bit more about that because I think that’s where I talk to a lot of young people, men and women, and they’re like, they don’t know how to get into data science. How did you do it?

 

Josie:

So, that mentorship it’s my mentor-

 

Ingrid:

I did not know that was the answer.

 

Josie:

Yeah it just segued right into it. But specifically, I was very interested in bias and algorithmic bias and how we can minimize that in programs, especially in real world fields like healthcare where they’re really impacting the prediction, like how well we can predict diseases and how well we can treat those diseases that we then predict. It was something that I was very passionate about. And my mentor came across this lovely program and I applied and I got in, and now here I am doing the research with the NYU medical center. But I wasn’t directly geared towards healthcare. I was very centered around the seed of algorithmic bias and how can I contribute in a way that would be meaningful to me? And that’s when-

 

Ingrid:

But you started in computer science, you said, right? You have a degree in computer science and it’s not necessarily a one for one map.

 

Josie:

Yeah. I just-

 

Ingrid:

So then I wanted to understand that transition.

 

Josie:

From computer science to-

 

Ingrid:

Yeah. A data science.

 

Josie:

It was always like when I was sitting in my classes while we’re talking about these very abstract or very, not very personal entities, it’s like this plus this, this mod this is like, you’re not really interacting with human or data or any real world records. And so I always wondered about that and what it would mean to really get my hands dirty with this data. And I was always just kind of interested. I would ask my professors if there were any extra readings I could do with the data in terms of like how this would apply in my real world outside the academic realm. So from computer science and studying in that academic realm to this real world researching, it was just more of a passion, like a seed where I was like, I know this can be implemented somewhere in a more concrete way.

 

Ingrid:

Right. So I think that’s a really interesting approach. It was something you were passionate about and you decided that’s the way you’re going to get into it. And so your future plans are to work for… Who’s your ideal employer?

 

Josie:

Myself, but that’s my ideal employer.

 

Ingrid:

Oh, you want to be an entrepreneur?

 

Josie:

I would like to open my own lab and just do what I want, but until then, I’m open to any opportunities.

 

Ingrid:

No, we want to hear your ideas, but we’ll talk about that. It’s in healthcare?

 

Josie:

No, actually it’s not in healthcare. It’s actually in more like social sciences and social justice, but-

 

Ingrid:

And using data science to help with that?

 

Josie:

Absolutely. Yeah. [crosstalk] I mean, data science is massive. It’s a powerful tool. And I feel like it’s not getting utilized or leveraged in ways that could really help people who aren’t like, for example, risk assessment scores in the justice systems. These are types of implementations and programs that are being used in real time that could really make a difference in impact in vulnerable communities.

 

Ingrid:

Individuals lives. And then the whole community. Right.

 

Josie:

It’s very important that we can get people who are listening on the ground.

 

Ingrid:

Right. That’s great. I mean, I’m thrilled. I want to help mentor you through that if possible.

 

Josie:

Yeah I love that.

 

Ingrid:

Any one of us would. So Niki, so back to you, what was your degree in?

 

Niki:

My original degree?

 

Ingrid:

Yeah you’re university.

 

Niki:

I’m a biologist.

 

Ingrid:

Oh you were the biologist. So I want people to understand the diversity comes not just from who you’re looking at, but our backgrounds, right? We have very different backgrounds. And I know everybody in the audience does as well. Our CTO is a physicist, right. And you just kind of go, “Oh, how did you turn into this?” I think it’s just interesting to hear some of these journeys. So how did you get into data science?

 

Niki:

So when I was doing my PhD, there was no term data science. So what I was doing, I had millions of observations of individual molecule behaviors and I was trying to model them. So in some sense, you can say, I was doing data science for molecular biology in order to understand the population behaviors, the consensus, the average, right? So this is what it all started, as it happened I wanted to pursue this project on epigenetics with these modifications on the DNA that are not encoded and they don’t pass from generation to generation. And I wanted to use this method. My supervisor was like, “Yes, of course you can, but you will have to see it through. I’m not going to hire a computer scientist.” And I was happy to do it. I was happy to do it. So actually R was the language that facilitated me, being able to analyze the data and with talking with colleagues, that was the first step.

And then of course I have seven years of experience as a post doc after I finished my PhD. Actually working on building bioinformatics pipelines and analyzing these data in large scale and with reproducibility. So that’s how it all clicked together for me, I have ,full disclosure, I was never shy of complex systems. So I think I’m seeking complexity. So it was a little bit unavoidable. And when I decided that perhaps biology is a little limiting on the type of data I can get my hands on, I did a data science bootcamp and for three months, and that was it.

 

Ingrid:

That’s fantastic. It’s fantastic. Hey, so I wanted to open it up for questions in the audience. If there are any. Yes. [inaudible] Do we have a microphone that we’re going to run up here? I don’t know if we have them.

 

Speaker 7:

Thank you. So many people talk about diversity and programs at a very top level, but my question is, and anyone on the panel can answer it, What are you, anybody on the panel doing to eliminate or to make middle managers aware of subconscious biases? Like ancestral energies, like the biases of maybe someone of another race or that is subconscious because sometimes we may not even be aware of that because we inherent, we inherit these biases on a subconscious level from our ancestors. So whether it be race, whether it be social economic, whether it be a certain behavior, what are people and organizations actually doing to eliminate these subconscious biases and make middle managers aware of it? Because diversity is a great, when you talk about it, but what are-

 

Ingrid:

Go for it.

 

Shar:

Okay.

 

Ingrid:

And I think Erin has an opinion.

 

Shar:

I’ll go for it. That’s a great question.

So two things, I would say for me in practice is whenever, or when I was a more hands on leading projects, I lay down the rules of engagement in my team. So on the get go, that will not be tolerated and there will be consequences. And if you do not agree with that, then maybe you shouldn’t play in my team, but it’s not that harsh.

But I think laying down the law, be very clear about the rules of engagement in our team helps to set the tone of how the team will operate. And also as a manager, part of our training, there’s a lot of the unconscious bias training that we’re all undergoing now. And I mean, it’s just a daily, everything that you, every day thing that you just need to be very aware of. Because I don’t want to alienate everybody and very conscious about people are coming to my team, coming from different backgrounds and also with their biases, but have something to contribute. And my job is to lead them and extract the very best out of each and every one and make sure that they are aligned with the mission.

Because otherwise if I cannot manage that and lead them, we will not achieve our targets.

 

Ingrid:

Excellent. Erin, I think-

 

Erin:

You saw me thinking?

 

Ingrid:

I’m putting you on the spot because I know you have an opinion on this. What did you do at H2O?

 

Erin:

So I think the thing about unconscious biases is that you have to not shame people about it. Because I think when people, you sort of poke at people and say, “Oh, you said that it was sexist,” or something like that. You know, people, a lot of the times don’t really realize it. And by people I mostly mean men, but women also-

 

Ingrid:

But women do it too.

 

Erin:

Have a lot of unconscious biases as well. Like it’s maybe a little bit more unconscious, let’s say. So I think one thing is just to not introduce shame into the equation. And I think if you’re someone who notices these things, I think as women, we notice them, but there’s a lot of male allies who will notice things as well.

So it’s important just to point them out in a non confrontational way and as Nikki said earlier, everybody has gaps that need to be filled in and that’s just, let’s not necessarily make anybody feel bad about it, it’s just, we all have gaps and they need to be essentially filled in. And I think that’s by education and whether or not that’s some formal, unconscious bias training. I think it’s just a constant education process that we have to do with our peers and that we’ll probably always be doing to some degree-

 

Ingrid:

We’re going to be always doing it.

 

Erin:

But we’ll… maybe it gets more and more, less of a burden over time.

 

Ingrid:

Meg you seem… jump in.

 

Meg:

Sure. Yeah. I think the panel here might be, actually itself an over fitted model of representativeness thinking for two seconds, but I think-

 

Ingrid:

Well we have one outlier here.

 

Meg:

From an epistemological perspective, meaning how we look at how society conveys information inside itself. And specifically when we look at AI system and how we’re sourcing data for AI systems, and you’re looking at the research methods and I’m going to put the philosophical and sociological to the side and just look at the systems that we have. So the kind of information that people are signaling, whether it’s companies, whether it’s boardrooms, whether it’s engineered products, whether it’s applications, whether it’s UX, whether it’s even the generation of data, all of these systems are in fact, I think going through a re platforming exercise.

So the awareness itself is much higher. So I think the noise is being cleaned. Will that be flushed out sociologically? That’s a different conversation, but I think at the way the economics of today and the world we’re in today is it’s already doing that. And we can go into the myriad of reasons for why. The second thing is in terms of, to your point about middle management, I think never before in my entire 20 years, since I began in this profession. It started in my lab having to get access to a mainframe to build the models because nobody would share with me until 2:00 AM, until today, things have evolved quite a bit in terms of equity and what the experience of equity is like. I’ll give you an example if there’s me in a queue and there’s my mentee who might present as a young white man or young Asian man, but he or she, they come from a different background.

They might get a chance to go ahead of me because I’m in this case, the advantage. So the calculus of how these platforms, these social systems, and even the conversations we’re having is changing. And then finally at the technical level, like I am sure you can speak to it. You can speak to it. The actual algorithms, the actual products, financial companies are building for machine learning systems, whether it’s chat bots, whether it’s FICO scores, whether it’s all those kinds of credit scores, those types of systems are also evolving. And there’s understanding at a widespread level, the historic ingredients that we’ve put in a machine learning data science level and the ones that are coming out, the signal and noise hadn’t been mapped correctly. And the way we’ve processed those systems, haven’t been correct. And there’s a re-correction on that. I would say maybe I’m taking too heavily of a technical approach, but-

 

Ingrid:

You can say my answer to you would be back to Shar, actually as a combination answer. [inaudible] Yeah. So I think it starts at the top. Yeah. So I think… Yeah, I think it starts at the top. I think each company, I mean, we get to make a decision of where we want to work. Right. So it starts with your leadership team and ] you can almost tell right away, right? So for example, when I walked in to meet Sri for the first time, and I think Erin would agree, you just felt so equal, welcome. The whole company’s like that. You can feel it. It’s in the culture. It’s cultural. And I don’t think you can, you can train people, but you’ve got to kind of embrace a culture of just fairness, right. And equality. And I think that that’s what Sri has built at H2O.

And I wish we could replicate that for so many companies. Because I’ve been at other companies where it’s not like that and I felt it and you know it, and then you have let’s go try and have training classes. Let’s go teach the managers that treat people differently or respect diversity. I think you got to look at it from within. And I think it starts with that leader at the top and I’ve worked for great leaders.

I think Sri is one of them that you feel that leadership at the top where it’s like, we’re all equal. So I would encourage people to… you vote with your feet and you go to the place where you’re going to see that. And it’s doable. There are a lot of men and women out there that treat you like that. And it’s about respect. So that’s my opinion. Because I can now… I can go where I want, right? I’m the boss of me. You’re the boss of you. You’re going to go where you want next. And I would just advise if it’s not a culture that’s respecting you, leave it. It’s not going to work out. Now you can try and change it, but that’s a hard road. Just my opinion. Niki, you were about to say something.

 

Niki:

No, I… first of all, let me agree of how wonderful the culture is in H2O. And it, as you say, you vote with your feet. And then I believe if I am allowed to put a little bit of a parenthesis to that. Technology companies offer this paradox, however. You want to be to a company, in a company that is going to promote your career and is going to be one of the top companies. And perhaps that’s when the choice is limited. So I do think that at the same time as voting with our feet, there is something to be said about cultivating systemic change and panels like that is one way to do it. I’m thinking being in a culture, you mentioned culture, perhaps being in a company that cultivates… Not like openness, not necessarily diversity, not necessarily equality, but perhaps the bare minimum would be like find a company that cultivates openness and cultivates-

 

Ingrid:

Yeah. I think that’s more to the… you’re right. I would agree with that. So I’m with you.

 

Meg:

I’m going to give a plug for Intel for a second because-

 

Ingrid:

They’re a great leader in this area.

 

Meg:

We’re, I think, number one on many, many of the social responsibility and ethics factors. And for me, that’s a massive data point. I live in Silicon Valley. There’s no shortage of social media companies that reach out to folks like Erin and myself and Nikki, but we choose to be where we are because the management itself is inculcated. I personally have an amazing organization that I’m a part of. My managers are supportive all the way through. I think they’re probably somewhere here, but we also partner the ecosystems also that you foster. So all of these ladies, myself and all of us will probably have drinks and become friends. The ecosystem matters.

 

Ingrid:

All right. Another question from the audience? Yes. By the way, I don’t think there’s an easy answer or one so we’d love to chat.

 

Speaker 8:

Hello ladies. Great discussion. So far, I actually have a question. It was for anybody in the panel. I’ve spoken to Ingrid briefly sometime ago. For someone like myself who has been in the IT industry for 20 years, I actually have an interest in breaking into the machine learning data science space. What advice would you give me basically, I’ve been at this for the past two months I’ve just been doing my own self study online. This is my second attendance to one of these conferences. So what would you give me? What ideas, tips any resources out there. I’m giving myself six months. Is that a realistic goal?

 

Ingrid:

Coming into the field new and retraining yourself?

 

Speaker 8:

Yeah. I mean, I’ve got quite a bit of technical experience.

 

Josie:

I mean, I feel like the best way to learn anything is just diving right into it. So if there’s a small project, if there’s like, “Oh, I want to make something that predicts the weather tomorrow.” It’s like starting there. That will teach you a lot. And it will also teach you where your blind spots are. And so it’s like, “Okay, well, I don’t know this. I don’t know this. And then maybe that’s where I’ll focus harder in the next project.” But just getting in there. Because I just studying and getting those little tool kits, it’s like putting them into practice is where you really start learning stuff.

 

Speaker 8:

So does that mean I should look for like data sets out there and capture those?

 

Josie:

Yeah. Getting data sets or even well yeah. That’s a shaky road, but that’s also learning experience I feel like. But getting data sets, making your own datasets, all of those things are a part of the learning journey, I feel like. And if you really want to take leap steps and those jumping steps, you can do every single part if you want.

 

Speaker 8:

Thanks for that.

 

Shar:

I’ll make a suggestion. So I don’t know what organization that you’re in, but this is a familiar question I’m asked by a lot of technologists. So what I advise to them is if you’re in an organization, like for instance in a bank is to-

 

Speaker 8:

I am.

 

Shar:

You are! Well here you go. Here’s the advice. Keep your eyes and ears open. Not just to what’s important to the C suite, but the most important part of the C suite, which is your customer. And I would frame that as being aware of your strategy and the types of initiatives that your organization might want to undertake to volunteer that. Present it to them as a problem and an opportunity and volunteer to be part of that. Or maybe even start a movement in your own organization. Because I’m sure you’re not the only one thinking like this. So why don’t you spearhead it? And you’re in the right place. Because over year ago, I didn’t even know what AI, blockchain, IOT was, but I’m a very curious person and wanted to do something different. Stay relevant in my field. So do it, just do it.

 

Ingrid:

I think also, and we’re going to have to wrap up here in a few minutes, the next panel coming up is the Kaggle Grandmaster panel where they’re going to share how they got started and what data sets to look at. And just as Josie said, just jump right in, right? Or volunteer to do a project with somebody else in another organization in your organization. Because you have that flexibility and it, and it’s a good way to get started without having to switch jobs or go find another job or get a degree or something like that. Any other thoughts on that?

 

Niki:

I will reinforce the idea of have a pet project. It also helps with applying for jobs.

 

Meg:

And if you’re looking to make… [inaudible 00:00:37:34].

 

Ingrid:

Erin’s going to say that.

 

Erin:

You should join the meetup called Women in Machine Learning and Data Science. There’s a chapter in New York.

 

Ingrid:

It’s big.

 

Erin:

And it’s quite big. [inaudible] Yeah. Check it out.

 

Ingrid:

You should all join that chapter here.

 

Erin:

And R-Ladies or PyLadies if you like either one of those languages.

 

Meg:

I would say if you’re doing a career pivot, translating all the skills and capabilities you have for your next profession is probably the most important. So to everyone’s point, pick that project, but also don’t throw away the skills that you have. So think about who it is that cares tremendously about data engineering and the infrastructure orchestration around that. That’s going to be a tremendous area of opportunity. We’re just at the dawn of this wave of AI. So there’s a ton of opportunity coming in the next six, nine months who knows, we might all be working for you. So…

 

Ingrid:

All right, on that note, I was going to say the opportunity in AI machine learning for everyone in this room, that’s watching live on the live stream and men or women just graduating middle school, what have you, the opportunity is tremendous. So I just want to encourage everybody to embrace this and transform the world by using this new technology. And I want to thank our panelists for joining us here today. We’re going to move the program along and get over to the Kaggle Grandmaster panel. Thank you all.