Data Science Hangout | Emi Zambada, Nationwide Financial | Building a story for the C-suite

Transcript#

This transcript was generated automatically and may contain errors.

Happy Thursday, everybody. Welcome to the Data Science Hangout. I hope that everybody's having a great week. I'm having an awesome week. We are in Palm Springs, California for our work week. So the whole, well, most of the Posit company is all out here just kind of working together and just getting to do fun things with each other because we don't see each other all the time. But if this is your first time joining us at the Data Science Hangout, so nice to meet you. I'm Rachel. This is an open space to chat about data science leadership, questions you're facing and getting to hear about what's going on in the world of data across different industries.

And so every week we feature a different data science leader as my co-host to help lead our discussion and answer questions from you all. So together, we're all dedicated to making this a welcoming environment for everybody. And we love when we can hear from everyone, no matter your background, level of experience or area of work. It is totally okay to just listen in. But there's also three ways you can ask questions and provide your own perspective on certain topics too. So you can jump in by raising your hand on Zoom. You could also put questions in the Zoom chat and feel free to just put like a little star next to it if you wanted me to read it out loud instead. And then we also have a Slido link where you can ask questions anonymously. We do share the recordings of each session up to our Posit YouTube and our Hangout website. So you can always go back and re-watch or share with friends.

One added note I just wanted to throw out here, if you are hiring, feel free to share that in the chat. I never see that as like spammy or anything like you want to share any open positions there, feel free to put them there. But with all that, I'm so excited to be joined by my co-host, Emi Zimbada, Assistant Vice President of Annuity Distribution Intelligence at Nationwide Financial. Emi, thanks so much for joining us. I'd love to have you just start by introducing yourself, maybe a little about your role and something you like to do outside of work.

Thank you. Thanks, Rachel. And definitely Yellows because you're in Palm Springs. I'm here in freezing Columbus, Ohio with summer snow. So definitely, you know, I wish I were over there, but very happy to be here with all of you. And I appreciate the time. And as you say, Emi Zimbada, I work for Nationwide, not the property and casualty, they are insurance. We work in the financial services, specifically in annuity. I've been in the company for over four years. I've been all over the place in working for Whirlpool, Siemens. I started my career or, you know, education in accounting, but life take you to places. And now I lead the what we call the distribution intelligence team that is nothing else but using technology and data analytics to support our sales teams to be more, more effective and efficient. So again, thanks for having me here.

I like, I like reading. I like have a beer. I go with cheap beer, Bud Light. I like sports and have a little boy and spend a lot of time with my wife and the kids. So yeah, and not a lot of free time. But whenever I can, definitely, you know, like to travel, go places, visit Mexico. I'm originally from Mexico.

He said diversity is not only the right thing to do, but a good business decision.

And you need to understand those so you can tailor your products, your solutions for them. And they feel good about buying your product, right? Because they feel like you care about them. You are socially responsible and you support their groups. And there's no other way to build those products than having the people that understand those individuals. So if you are a data scientist, if you don't have, think about a group that doesn't have any minority groups, you know, white males, nothing bad, white males are amazing. But think about like there's no diversity at all. So those individuals, they're not going to know exactly how a woman thing or Latinos or black, and it's going to be very hard for them to understand nuances and the specific things that they are very important for them.

And they're not going to understand that maybe language is critical, you know, something that's funny. They're not going to know that probably data is biased or some of them, right? Like people building computer vision system that doesn't use a balanced data set and doesn't correctly train the models to identify properly black people, they're going to have a problem or people doing speech recognition, building those system to transcribe a speech into text and doing a lot of solutions based on that. If they don't understand that people might have a different accent, different, you know, way of talking, they're not going to potentially use the right data sets to train the models right and, and therefore not, not working properly for those, for those groups. And those groups are going to notice that and they're not going to be happy with that. And they're not going to buy your products.

So again, diversity helps with all of that and, and, and how you, you can help, be an ally. If you, if you, if you are a leader, hire those individuals, you are going to have more powerful, better teams. If you are an individual contributor, ask people, mentor them, and help them. And you will learn to, you will learn a lot from that, from those interactions.

Open source adoption and guardrails

Yeah, it's definitely, of course, security and data protection is important. So when we have this dedicated environment, we call it model factory, that is, is, is protected, it has all the security controls, and we have R and Python embedded into that. And every time there's a new package, it gets tested, the versions, you know, you cannot go and get the latest version or any versions that you think is best. We have an approved version for each of those tools. We, again, we test the packages, we give a very, we have very long, like a lengthy procedures of how to use it, what to do, what not to do. And we do peer reviews when people use, again, whatever, whatever package or whatever, you know, code you write, it gets reviewed by one of your peers. And then before move to production, there's another team that implement that, that validates your own incurring to any security issues, you know, like a credential are managed in a specific way, you know, you need to follow that protocol.

Now, there's a lot of things that I cannot share publicly, but there's a lot of controls around open source systems. So I think, again, we have a lot of, we are big fans, you know, it's cheaper, it's easier to have the talent knowing those tools, and people like to learn those tools, but definitely a lot of controls to protect the data and protect the code.

Coordinating large teams across the organization

Yeah. Again, like any other big organization, there's different groups and different part of the different organization across the companies, like our data scientists are in finance, or data people are in technology. You have the data architecture team, and the security team, and within even within IT, or within finance, you have different groups. So I'm not going to, you know, I don't want to lie, and I don't want to ignore that having different organization, different leaders creates some difficulties. And I think that's a challenge.

Difficulties, we try to build a pod, a group of people. So we want to make sure that there's leadership support for the project. You cannot start without that. There can be some tests and learn that gets, you know, done more naturally. But, you know, you have a project, you have the support, you create a new business case, you get the funding, and then you say, okay, in order to do this, I will need the data scientist, the data engineer. So a lot of this, yeah, you can, you have support, you have the funding, but also you, you know, that was a relationship, and building a network is important, because you need to go with those leaders and get buy-in, right?

One thing I like to do is, I'm not going to tell them what to do. I'm going to, you know, I have this philosophy that you need to be very tight on what, what is the outcome that you want, what is the, the objective, and what is that, and you can, it will be needed. So be very clear on that, and hopefully they truly understand that, and, and you, you describe it, but then you wait for them to describe it, and you reiterate until it kind of sounds, you know, it's clear to them. I think that's the best way, right? Repeat, repeat what we're trying to do, and if they are still loose or not very clear, you gotta keep that message until they are clear. So be tight on that, but then be loose on that, how they get there. They are way smarter than me, so I will never use this algorithm or do it this way, because you, you, that, that controlling is, is no good. It will inhibit, you know, innovation, and, and, and they are experts. So be loose on that, you know, let them define what is that, what is the way, the how. The why and the what is important to be tight, the how, you know, they'll figure out, they're smart, but then be tight on the results, right?

I hear someone or saw someone talking about KPI, so I want to increase sales. I want to, you know, reach this market share. I want to make that metric that is simple to understand for the data scientists how to get there, but simple for the business to, to understand that, you know, we're achieving what we committed to do, committed to achieve. So, you know, tie, loose, tie, give them the money, give them the resources, give them the people that is needed, let them run with that.

Building a story for the C-suite

Yeah, no, great question, and definitely, you know, storytelling is critical. I go back to, you know, the little kids, right, and you need to speak their language. They are not going to speak your language. Go back to the kids. You need to talk to the kids in their language. You talk about the Shakespearean language to a kid. They are not going to understand you, and they don't care. They are going to lose them, so listen to your leaders. Listen to the earning calls or whatever, you know, ways that they present to the organization or whomever is the leader. Try to listen to previous conversations and pay attention to the words they use. Pay attention to how they describe things. Pay attention to the things that they, the metrics that they share or the metrics that they talk about it, and use those words. Words are important, right? Like, you can say the same thing with different words, or you might have the way you describe something, use their words.

You need to speak their language. They are not going to speak your language. You talk about the Shakespearean language to a kid. They are not going to understand you, and they don't care.

So, you know, if you can talk about, like, we talk about the data foundation, but if you say data integrity, you might say, well, data integrity, data foundation is the same, but if they use the word data foundation, use the, you know, same data foundation. If they say self-intelligent, you can use another word. Use their words so they can understand you and keep it consistent. You know, if you keep changing the words or the way, you know, the views, think about, like, in the old times, or even now, Google, but think about, like, probably the newspaper or the format, any website, but think about, like, the old-fashioned newspaper. New York Times, they always look the same. They have the same structure, the same format, the articles, the layout is similar. So, every day you go and read that newspaper or, you know, your online news, it looks the same thing. Imagine if they will tweak things and move things around, you will spend a lot of energy and time trying to figure out, and that's mental thing that your brain is taking energy to figuring out the format and the structure, and you don't care about that. You want to read the news. So, New York Times, Washington Post, they keep the same structure. They keep it the same. So, all your energy is to understand the content of the news that you're trying to read. So, the same thing with the storytelling and the dashboard, find a way that the words, the structure, the metrics that they use and they're familiar with, so they don't spend time and energy trying to understand your dashboard, your story.

Don't say a lot of things. Less is better. Be clear and concise, but be very specific on the things that you really care. Repeat throughout your session and throughout your dashboard, and be very specific about the action plan, what you need about them. And silence doesn't mean agree. You know, silence can be, I acknowledge that, but it doesn't mean that they agree. So, watch them, you know, look at their expression, and next time you go, repeat.

And the one thing that we, another mistake we make is that we go back, you know, let's say you presented something last month, and you're going this month to present the same thing. Recap, it said, last month we talked about this, now this is happening, and now I need you to know this, or do this for us, right? We need your support. And don't go there thinking that they will, they remember everything that happened last month, they know everything that is happening, you know, in your work, and assuming they know that, because they don't. Whatever happened last month, there's been so many things, like the kids, right? So many things have happened in their life that they're not going to remember, you know, what you talked about last time. Don't feel offended. It's normal. Go back to that and say, last time we spoke, we did this, now we're doing this, now let's bring it to the present.

Transitioning into data science

Yeah. Don't try to be a data scientist, you know, from scratch. It requires a lot of expertise, a lot of knowledge and experience and education. A lot of people, they go and take online course, which is great, or they build up a little toy project and they want to go into data scientists and they don't get the job and they get frustrated. So start with something, data analyst, start with something, business intelligence, you know, supporting an area with some dashboard, some analysis, even if it's in Excel, build those projects that can get you started. And then think about like how much passion or where do you want to go?

Like if I think about myself, I was passionate about the topic, I've been learning, you know, always trying to learn new things, but I knew myself that I will not be able to compete with the data scientists, people that have PhDs, they're mathematicians, they're really good at what they do. If I would have gone into that track, I would probably have failed and not be as competitive and as good as they are. But then I thought about like, well, what are the things that I think I do best? And the things that, it's like you have a new product, right? Go back to being an entrepreneur, you are your own product, you are your own brand. And think about like, you will go in the market and compete versus Apple with the iPhone, you're not going to make a good deal there. You're not going to be able to beat the Apple and be better than, you know, build something better than the iPhone. So instead of that, I say, what is the gap? What is that thing that is missing?

And then you'll say, oh, but this is not enough to do this. Then build that app that will, you know, make iPhone better. So think about that in your organization or in the industry. And what is that, you know, data scientists, yeah, we'll never have enough, but there are a lot of good people, very smart, but maybe think about maybe in my organization, there are not a lot of very good data engineers. And not like that, maybe start being a data engineer because there is the need, you know, there's the market need, again, being yourself a brand, a product, and I don't mean it in a bad way, but feel that need, understand that need and try to fulfill that need. And then continue learning and continue, you know, figuring out what is your career and where do you want to go.

Continue learning, don't get desperate, but don't try to, you know, get pushed to something that, you know, might not fit with your career progression or what you can really do in the short term and don't get frustrated that there will be someone or a position that might help you.

What to look for in data science hires

I think, you know, there's definitely the math, the technical expertise, the coding, but being, you know, being very analytical, like asking questions, be someone that cares about the business, someone that is passionate about that, that asks good questions, think through like problem solving, you know, think about the problems and what is the thing that we really need to solve. So I think it's a combination and some people are better at one thing than the other, so try to fit, you know, again, your profile to whatever whatever the spectrum that can be. If you are very, you know, into the technical thing, the math, you can do that, but if you are more on the analytical or the business translator, do that. But, you know, we are very rigorous into data scientists and we have a very strict, you know, hiring process. So that's, you know, that involves a lot of the technical expertise. And depending on, you know, like if it's an LP, computer vision, you know, whatever that might be, we have a specific process. But again, don't feel discouraged if you don't have all of that, maybe find that role that fits your needs.

Navigating the R-Python dichotomy

Yeah, our model factory environment is suited for both and other languages. So we, everything we build try to be, you know, to be flexible with whatever programming language is used, especially R and Python. The people that are experts in R or that's in Python, but a lot of that, you know, we as an insurance company, financial services, we're very heavy on R, but this transition in, you know, probably 70, 80, 20, I think about 50, 50, but moving towards Python. So people are trying to learn both and they prove it to be more successful if they know both. So not easy and difficult to have, you know, expectation to be an expert in both, but if you build expertise in one, but then know how to do at least the basic on the other language, that can be extremely powerful.

Thank you so much. I think we got to everybody's questions. I'm sorry if there was anything that we missed, but thank you all so much for joining. Thank you, Emi, for all your great insights. This was great.

Thank you all for joining and I appreciate the time and happy to connect and let me know if there's anything I can do to help. And thank you for having me. Enjoy Florida over there.

Data Science Hangout | Emi Zambada, Nationwide Financial | Building a story for the C-suite

Transcript#

People development and personal branding

ChatGPT and AI in the workflow

Bridging data science and the business

Presenting to the C-suite

Data sources and tools

Diversity and inclusion in data science

Open source adoption and guardrails

Coordinating large teams across the organization

Building a story for the C-suite

Transitioning into data science

What to look for in data science hires

Navigating the R-Python dichotomy