Mining Your Own Business Podcast

Episode 15 - Why Data Scientists, Actuaries, & Engineers Get Along at Pacific Life

Why Data Scientists, Actuaries, & Engineers Get Along at Pacific Life

On today’s episode, we welcome Rob Horrobin to the show. Rob is Pacific Life’s AVP of Data Science where he leads the Advanced Analytics Center of Enablement.

You’ll hear Rob and Evan discuss the Center of Enablement and how it supports data science efforts across the organization.

Rob describes their “Do-Partner-Support” strategy, which governs how they support internal clients while also creating possibilities for true transformation.

We’ll also hear about the intersection between actuarial science, data science, and engineering, as well as the importance of varied skill sets and backgrounds in data science professions.

Rob and Evan end by exploring the future paths of analytics capabilities.

In this episode you will learn:

  • How the Center of Enablement supports learning and transformation
  • How experience in multiple fields can build complimentary skillsets for successful data science projects
  • How to successfully partner with clients to implement a data project
  • Tips for those currently in or seeking careers in data science

Learn more about why we created the Mining Your Own Business podcast.

About This Episode's Participants

Rob Horrobin | Guest

Rob is an experienced change agent and leader of quantitative practices, specializing in the use of data and analysis to help organizations navigate periods of great change. As the leader of the Center of Enablement, Rob is focused on analytics and data strategy for the Retirement Solutions Division of Pacific Life.

Prior to joining Pacific Life, Rob oversaw the Insurance Operations Optimization & Decision Analytics practice at John Hancock. Rob started this internal consulting practice in 2015 after taking on increasing roles of responsibility in Finance, Corporate Development/Strategy, and Analytics.

Before joining John Hancock in 2008, Rob had extensive experience in the shipbuilding industry. Rob holds a Bachelor of Mechanical Engineering from the University of Delaware and both an MBA and MS Information Systems from Boston University.

Follow Rob on LinkedIn

Evan Wimpey | Host

Evan Wimpey is the Director of Analytics Strategy at Elder Research where he works with organizations to transform deficient data into tangible business value that advances their mission.

He is uniquely suited for this challenge by pairing his professional experience in management and economics at high-functioning organizations like the Marine Corps and Goldman Sachs with his technical prowess in data science. His analytics skillset was strengthened while earning his MS in Analytics from the Institute for Advanced Analytics at NC State University.

Evan almost always has a smile on his face, which is at it’s widest when he is helping organizations use data in innovative ways to solve complex problems. He is also, in a strictly technical sense, a “professional” comedian.

Follow Evan on LinkedIn


Key Moments From This Episode

00:00 Introduction
01:04 Rob’s background and how he got into data science
02:53 Rob’s role in the Center of Enablement: The Do-Partner-Support Model
05:47 Scope of the team’s work and how teams are structured
11:03 Actuaries, engineers, and data scientists: kindred spirits
15:04 Advice for those considering launching a data science effort
19:04 Advice for those interested in a career in data science
21:08 Standard certifications for data scientists in the future?
27:58 Rob’s vision: How can analytics create entirely different business models?

Show Transcript

Evan: Hello and welcome to the Mining Your Own Business podcast. I’m your host, Evan Wimpey, and today I’m super excited to introduce our guest, Rob Horrobin. Rob is the AVP of Data Science at Pacific Life where he runs the Advanced Analytics Center of Enablement. Rob, welcome to the show. Thanks so much for joining us.

Rob: I’m excited to be here, Evan. Thanks for having me.

Evan: Hey, fantastic. To get started, can you just give us a little bit about your background and how you got into data science?

Rob: Certainly. So as you mentioned I’m at Pacific Life and I run our COE in our retirement division for advanced analytics. And so in that capacity, we support our division, which focuses on people’s retirement needs. So variable fixed annuities and mutual funds as well. I’ve been doing that for about four years.

Grew that team over that time and about 14 years of experience. Financial services and insurance where I’ve done things like reinsurance, finance, distress program, turnaround strategy. And then I found my way into data science about 10 years ago. And so from there, I really found my niche, if you will, everything was prolonged to that point, and was able to leverage both my undergrad and mechanical engineering as well as my master’s in Information Systems and my MBA. I actually was a career switcher, I was in pre-business school where I actually did mechanical engineering in the shipbuilding industry. So although I’ve been doing financial services and, and really found my space there for 14 years now, I still sometimes think in six sigma concepts and you know, welding and things like that so I guess those are, it’s a very formative experience when you’re younger, right outta college.

Evan: Yeah, certainly. And I think, you know, relatively new data science field, a lot of people who have in data science now leadership roles have come from another industry so it’s good to have the different backgrounds that you bring in there.
So Rob, you’re, you know, Pacific Life does a whole lot. Can you talk about your team? You’re on the I think enablement, advanced analytics, Center of enablement. So that sounds great. Can you talk about sort of what that team does and what types of things you work on there?

Rob: Certainly. So it’s a great model. And it’s also a lot of fun and, and I’ll tell you why in just a moment. So we’ve got a mix of data scientists, data engineers, visualization experts, and, you know, delivery managers. Some, you know, somewhat akin to project managers, but they have to really understand the data science concepts. First off, it’s a great team a great place to work as a company as well. So that’s, you know, just a great starting point. The work is really fun is because we really have adopted what we call a Do Partner Support model. So one is we do, so sometimes internal clients come to us and they say, Hey, I just need you to tell me if this thing worked or not with statistical significance, Okay. That’s, a Do. Or, Hey, we just need you to build a list of contacts that we can go out when we launch a new product because we want to have a real targeted focus to really make sure this product takes off in the market. Just give us a list. Okay.
We also do things, what we call a Partner where, and that’s where we tend to connect more and more with the actuarial community because they say, Hey, you know, I wanna learn to do this myself, and I’ve got some statistics, mathematics, some basic coding skills, but I can’t quite get out of the blocks. Can you help us with that? So I call it partner or first, you know, we build, you watch, then you build, we watch, and now you just own. So not unlike, you know, learning to fly an airplane or drive a car. So we really love that model cuz then it becomes really transformative.

And then Support. And so Support is where we try new concepts new technologies and then we say, Hey, is this really something that is commercial able for a, you know, monetizable within our environment? Or is it something that’s, maybe it’s just a little too far out there, we don’t have the risk appetite for this type of technology or whatever it may be. Or in some cases, This is something that we wanna scale and we wanna proliferate across the organization to give to people so that they can actually do data science in the business units at the front line, trying to solve real business problems. And we really encourage that in a risk-balanced way. So Do Partner Support. And then as a result is we really get to connect with. Business partners who are really working on thorny problems, and they bring the problems to us because from day one, we’re just being helpful and really trying to lean in where people say, Hey, I know you can do this, but I wanna learn to do this. So we really love that. And we structure our team to be able to support people’s learning to do these things because, I mean, data science is fun. Who doesn’t – it’s just one big puzzle. Yeah, that’s, it’s been such a fun journey and we’ve got great partners and great sponsorship up and down the organization. That’s why we’ve been able to really be transformative.

Evan: Yeah. That’s great. And you say you’ve got business partners. Does that touch all of the functions of Pacific Life? I think like the actual finance and insurance teams and, and products, but then also like traditional business functions, your sales and marketing and HR, and everything else.

Rob: That’s a great question, Evan. So we work with – if I think about it, there’s, it’s probably easier to say which teams we don’t work with across the division. So we work a lot with our sales and marketing teams. We work a lot with finance we work with actuarial teams. We work with strategy teams. We work with technology teams cuz we’re, you know, technology adjacent if you will. And when we started, we were in the technology space before we moved over to the digital side. And so for us where the variation is actually on the types of products we’re using. And so if we think about that, do partner support model much more of our work now is partnering, which is great. We still do a lot and get a lot of the Do, Hey, can you just answer this question for us? But a lot more partnering because folks see that this is something that they want to continue to grow and really become part of everybody’s day-to-day if we can. And that’s actually part of our charter, our objective.
So some of the use cases, would be as I said, leadless, we launch a new product. Who should we start our marketing blitz with our marketing campaign and our distribution partners? Who should we start with because they, we think they’ll have a propensity to want to use those products. Other examples are forecasting of sales in by territory nationally based on trends and external factors. So increasing the precision of that type of a time series model. Other examples are volume forecasting in call centers and operations areas so that we can load balance staff, vacation times. Not just seasonality, but also is there external factors coming. And trying to find some of those deep signals so we can make sure that our customer is good, while we’re also not burning folks out, and, you know, folks can take vacation time. And customer experiences is incredibly important for us as a company and one of our key factors that we believe to be helping us meet consumer demand. So really we touch almost every group.

Now in the finance and actuarial space, they’ve got a ton of statistics and knowledge and experience. So where we tend to focus with them is how do we help very robust approved processes run more smoothly, and that’s, you know, data transfer, potentially making it easier to visualize the outputs, things like that.

Evan: Okay. Yeah. And it sounds like you, you certainly touch every, every piece there. So certainly has the transformative possibilities. You, I really do like the Do Partner Support. Sounds absolutely like an internal consulting arm. On the partner side, as your business partners grow capability and want to be able to do some of these things on their own, are there data science or data engineering roles that are sort of dispersed across the business as well? Or do those roles, do those titles sit just on your team?

Rob: Most of it is within our space. You know, from a data engineering perspective, there’s quite a bit in technology. And so we have data engineering. When we have data engineers, we’re very keenly focused on supporting analytical use cases. And data engineers have a much broader mandate in general around running a business. So we do have folks do it, but they do it in a very specific area of domain. And then we connect with our broader technology teammates. But there are data science or more data analyst-type roles that are out there in the organization. And we encourage that because we want folks to be able to do some of this type of work.
There are data science teams in other parts of the company as well, and so we really interact with them as well. The majority, that center of gravity for data sciences with us. But we’ve actually had great success too, as some folks come to us as data scientists and say, Hey, I love running through these models, but I’ve really found that I, you know, I really, really gravitate towards you know, marketing or sales or finance. And then they end up getting a sales or a finance role. So now they’re, you know, a financial analyst or marketing analyst, but they actually have that data science, you know, the data science stripes. And we encourage that because now it’s another person that we can say, Hey, can you help us understand the business context as well? But yeah, the center of gravity’s with us.

Evan: Very good. Yeah. And it’s great to have sort of, those champions that can help translate that are out in the business function and easy to, easy to talk back to your data science team.

Rob, you mentioned when we first started the show that your background in academics is in engineering. You’ve studied, you were in finance for a while. I think for a lot of data leaders, they didn’t come out of school and immediately jump into data science. A lot of folks in statistics or computer science, economics, like myself, engineering, like yourself, I don’t know much about actuarial sciences, but in my head that maps pretty closely to a lot of the statistical rigor that’s required in data science. So you’ve touched on that a little, about some overlap there.  I’m curious if you’ve noticed a huge overlap and how much transferability there is between the actuarial piece and the data science generalist piece?

Rob: Absolutely. So, you know, one thing that makes it easy is if engineer jokes, you know, with engineers, we love engineer jokes. They transfer right to actuarial, right to data science, you know, very similar, birds of a feather, very similar schemas. So we do work so closely with actuaries and you know, I’ve seen actuaries actually – we’ve interviewed some of ’em at times for data science positions. We’ve seen actuaries I know in the industry have become great data scientists. Where I see some of the difference is, you know, being an actuary is such a rigorous – in some cases, decade-long process to get certified where you really dive deep and learn a lot. Not just about the statistics component, but also about the understanding of regulatory components and how you know, asset liability matching – it really becomes very, very vigorous around how to, not just the statistics, but also the inner workings of how a company manages, an insurance company and assesses risk and also the financial aspect as well. So, you know, hats off to folks who do that because it is such a challenging rigorous marathon to do that.

Where I think I see the overlap is, and you know why I’ve always found actuaries to be kindred spirits as a mechanical engineer, and then as a data scientist, is the grounding and statistics and critical thinking and quantitative reasoning. So the way I always think about it you know, if an actuary’s a quarterback, you know, a data scientist is a running back and an engineer could be a tight end, you know, very complimentary skills, but not a one-to-one. And so where I get really excited is when an actuary and a data scientist and a data engineer, they rally around a problem and say, how do we kind of make this thing work better? And because data scientists, they’ve learned a lot about the coding aspect, which really isn’t, is increasingly, but not necessarily as pervasive in the actuarial curriculum. So they understand the coding and the statistics, the actuaries understand the statistics and the business context, and then the data engineers under the platform understand the platform and you know, ETL transfer and storage. That becomes such a powerful combination that really, you know, if you could get one person who could do all of that, that person is a unicorn and they should demand sky-high hourly rates. I could tell you that much. But yeah, so we really, really appreciate working with our actuarial partners and the lines get very blurry and increasingly are getting blurry. And I, as in speaking with actuarial folks that I’m close with, increasingly the Society of Actuaries, as I understand it, is making coding much more common in the actuarial testing, which as a data scientist I fully support cuz that’s just more folks that are making great decisions using this type of power.

Evan: Yeah. That’s great. And it sounds like that’s where your team can really support in the do support. Yeah. I think you painted a really great picture of the complementary skillsets and how they can work together. I will say, I was looking for the punchline. When it’s “An actuary and a data scientist and a data engineer, you walk into a room –“

Rob: And don’t talk to anybody, right? No. I have met some actuaries who are actually very extroverted, but yeah, much like engineers, tend to be on the quieter side.

Evan: Sure. Yeah, you’re right. You can recycle the engineering jokes there for sure. Yeah, and I think, you know, what jumped to my mind when you talk about the rigor with becoming an actuary versus the rigor, you know, I think it’s a much broader spectrum on becoming a data scientist. You can get PhD in fields that are related to this. You can also take a six-week bootcamp course and learn enough to be dangerous or enough to be really useful in, you know, certain subsets and enough to get your foot in the door.

So you’ve grown a team that has a lot of data science capabilities. What would you suggest to people who are considering a career or considering, you know, at least a part of a career or embarking on some data science effort and some data science initiative?

Rob: So I hear two parts there. One is around growing a data science capability within a company. And I’ll share some thoughts there. And then the second around the individual, and actually I’d love to share perspective there, but also I’d love to hear your perspective actually on both of these.
So, you know, one, when I think about growing from an internal company, and you know, there’s a couple things I think of one is, you know, strategic alignment. You know, we’ve got great sponsorship from day one, there was no selling and this is a useful capability, it is what is it gonna look like and how are we going to do it? So first and foremost is what’s your strategic priorities? How do you take those priorities to specific use cases that you can then go after to show some progress? Second is building a core team that really loves to solve problems. And what I’ve seen work is, you know, data scientists, data engineer – a data scientist, a data engineer, a delivery project manager, and a business translator, omeone who understands the business so they can make sure they take it from, “Hey, I think I’ve got an idea” with a business partner all the way through to actually building a product that’s gonna get consumed. Third piece to that is starting with the end in mind. So it’s great to build a model, but you have to also make sure that it’s fit for purpose and in the end, how are the folks gonna consume it? Is it going to be embedded in a workflow system? Is it going to be an Excel spreadsheet that they get emailed once a day? What is that gonna look like? How’s it gonna get consumed? And then that ties into the next point, is really get in the shoes of the folks who are gonna use the product. And so, you know, I’ve sat on sales desks and actually listened into calls. You know, I’ve gone on ride-alongs with wholesalers. I’ve sat in on actuarial risk meetings to really try and understand. And one thing is talk about talented professionals. You know, it’s, they’re just unbelievably talented folks that do this type of work, and I do not have that skill. That’s lesson one. But it was just wonderful to really start to say, okay, I see the process they’re going through. And then show them work in progress so they feel like they’ve got a say and they see their feedback turned around pretty quickly. So then when they get the product, they’ve already said, okay, now I know how I’m gonna use it in my day-to-day.

So those are some of the key things I’d think about. So you’re relying on strategy, the day-to-day, making it easier for somebody. You’re thinking about how they’re gonna consume it and everyone’s agreement that’s gonna be useful for the business. Any reactions there? You see so many different clients. I’d love to hear your thoughts.

Evan: Yeah, I, you’re spot on, Rob. I completely agree with everything. So I’m in consulting, external consulting, so certainly we jump into to a number of different organizations yet, and I think I really, I don’t have anything to add. I think you nailed it. Well, okay. I don’t have anything to add in here. I’m gonna add this. I love the ride along, the sit beside somebody who’s an end user. Somebody who you’re trying to improve their decision-making ability. With analytics, it’s so easy to look at the data that they generate and, and assume a lot about the process, but to sit with somebody, and walk through it, it’s easy to take for granted. Oh, this is, this is pretty simple. I’ve reduced it down to, you know, a matrix that’s exactly the size and I can model it. But sitting with them and learning the decisions that they have to make and the inputs that they’re taking in to do that, I think, is really helpful when you’re designing something for an end use for an output to use. So, I just, I guess I wanna second that. But I think that was a great.

Rob: Great. Sounds like we’re on the right track.

Evan: I’ll, yes. All right. Or so you’re the baseline. We’re the ones who are on the right track then. And then I guess, I guess to the second part of that, to speak to an individual who’s now considering, who had just heard that and said, wow, data science sounds awesome. I wanna get into data science. What, what kind of advice would you offer? Would you offer that?

Rob: Yeah, so I think that one thing I love about data science is I’ve seen a ton of profiles be successful. I’ve worked with folks who had a PhD in operatic theater who were tremendous. One who was a trained guitar player from the Berkeley College of Music. I’ve seen folks who are chemical engineers, electrical engineers, business majors and almost every, any profession. And I think that the one is you have to have the passion to really learn the skills is, one. Two, is you just look at, you know, what is the stack that you’re going to be working with. So you have to be able to be comfortable working with relational databases, SQL as an example, being comfortable querying and building tables that you can then consume into your standard tools. You have to have grounding in Python, R, or something similar. Understand as well your probability and statistics. A visualization layer is really helpful because then once you come up with a concept, you have to be able to make it consumable for senior leadership. So there’s a variety of visualization tools out there. And then last is really understanding the end-to-end data science process. You know, whether it be crisp DM or, you know, agile methodology, whatever it may be. And I think if you can, if you can put those on there and then get some. Especially if you’re in an existing role where you can say, Hey, I’m a financial analyst and I keep running into errors with my financial forecast. What’s going on with that? You can actually come up with a better answer using these tools. Then you can actually start to build almost a portfolio of work to show that you’re serious about this and you’ve got these certifications, but you’ve applied it in a context, and then you start to build that portfolio of work and then you get an opportunity to move into the data science space.

And one question I have for you, in addition to any reactions you have there is, we talked about certification. Is there any word that you’re seeing about a standard and certification for data science that’s coming? Much like we had for engineers or actuaries or CFAs? Any traction there? You think we’re gonna see that in data science?

Evan: That’s a good question. There are a lot of disparate certifications. Most of the certifications that we see now are tool specific, so you can get a certain cloud service or a certain programmatic language certification. I don’t know. As aside, you know, maybe from some of the online trainings that you can do and get a certificate or like a bootcamp certificate. It’s just very disparate. It’s just very broad. I don’t think there’s a single or even a handful of things that are gonna be, these are the key certificates to have. And I think that’s the nature of data science is so dispersed. There are so many players in technology providers and solution providers that there’s not gonna be like a one size fits all, and I suspect that a lot of ’em are very helpful, but I don’t think there’s going to be a standard. I don’t know. Do you, what do you think Rob?

Rob: No, no clear answer either because I’ve seen them pop up and I’ve always been kind of curious. But if I think about, you know, being an engineer and by the skin of my teeth, getting through the first certification exam before you become a professional engineer. I forget what it was even called now, but I think Fundamentals of Engineering and then if you do that, you can practice under a PE and then after you do that for a couple years, you can apply for your state license to be a PE. A lot of the reasoning for that is – you look like you were gonna add something Evan.

Evan: Well, I’ll probably bite my tongue here in a second, but I don’t suspect that, I don’t suspect that data science will go that route where there’s, you know, here’s the certification. Be practicing data science. And if you don’t get that, you’re not gonna go to a big firm or a big, you know, either consulting firm or a big organization that has a large data science team. It just seems too broad and too disparate of a field for that. But I’m ready to eat my words.

Rob: Well, this is a theory that I’ve been thinking about is the reason a lot of that was done, and we learned this in engineering, is if you make wrong estimates, or if you are doing incorrect calculations or you don’t follow a standard grievous, bodily harm can be incurred. Bridge collapses, things like that. And much like the actuaries very similarly is, if the process is not followed and vetted and independent review and doesn’t align to best practices, there can be significant financial losses or even in worse cases, promises that were made to customers for their beneficiaries or their retirement aren’t gonna be true and folks have, you know, significant financial harm. So I think about that in the context of data science where, you know, you start to think about interpretability and as we see AI and machine learning really start to get traction in a lot more spaces, really automating some of these decisions. The data engineers outside the data scientists that, that run these processes and build these models start to have significant societal impact. So I wonder if there’ll be a movement at some point to say, maybe it’s, I think you’re right, it’s very diverse, the use cases. But maybe they say, okay, if you’re training a model for automated cars, or you’re training a model that’s going to make standard of care decisions, or you’re training a model that’s going to make a decision around medical treatment, triage. Is there gonna be a need to be a certified data scientist science for some of those? Not sure.

Evan: Yeah, I, you raise a good point, Rob. I think that’s a closer point. Or at least, at least a more specific use case. And I can see. Maybe, maybe a responsible or ethical data scientist. And I don’t know that there’s ever going to be like some strict standard where it, that’s, you know, that’s translatable from engineering or from actuarial sciences.

But if you’re maybe are aware of biases that can be introduced, biases that are in your training data that you can perpetuate in this model that’s in production. I could see, I could see a stamp, I still don’t know that there would be the one model, but I could see some leaders in data science offering something like that in an ethical, responsible, unbiased data science practitioner training. That’s a good idea. We’ll talk offline. Maybe.

Rob: I think we’ll have to, I think we’ll have to start a curriculum.

Evan: There we go. Well, I did want to touch on one thing from your, from your answer earlier about somebody embarking on a data science journey. You gave the example of the financial analyst who’s thinking maybe there’s a better way to do this and the term that comes to mind and what I think is an important skillset in addition to, you know, the litany of technical skills that you need is really intellectual curiosity – are you eager to ask questions and think about new ways to do things? And trying to understand how data comes to be and trying to understand what’s some analytic or model output is going to be. I don’t think that’s different from anything you said. It just that intellectual curiosity piece I think is probably a big one.

Rob: Absolutely because you’re gonna run into roadblocks. You’re gonna run into challenges whether your data’s not gonna be clean or the model’s not gonna have the performance you expected. And so that tenacity and really trying to understand why. You know those folks who used to take apart the clocks as a kid because they just wanted to understand why they work or, you know, the kid in the backseat, you know, just why, why, why, why, why being, just that insatiable machine, you know, that’s the sign. If you’ve got a kid who’s in the backseat asking why, why, why, why? with every scientific question, point them towards data scientists. They’re well on their way.

Evan: My kids are at that age now, and I’ve only got a limited number of why’s, but then I can still be optimistic about it before. Okay. Don’t be a data scientist, just be a, I don’t know, just be a stop sign somewhere.
Rob, I think we have time for one more question here. It’s been a super interesting conversation. You touch a lot of areas in Pacific Life. You’ve touched on a lot of them here today. You’ve got a team with a diverse set of skills. If you could align all of your business partners to the Rob Horrobin mission and the Rob Horrobin goal, and you could point your analytic effort to do anything at Pacific Life and everybody’s behind. If you’ve got full stakeholder support, what would you want your team to work on?

Rob: Oh, that’s a great question. You know, I think where I’d love up to see us go next, and we’re already starting to think about this conceptually as you, as I said, we’ve got great partners. I’m willing to try new things. It’s just been a tremendous journey oftentimes, and I am guilty of this, the engineer, he likes to say, you know, “here’s a problem, here’s a solution”. Analytics is often viewed, and we operate this way quite often as, “here’s a strategic problem we’re trying to solve. We wanna enter this market, or we wanna optimize geographies, or we want to, here’s a business problem we wanna solve. Can analytics help us get there faster or more effectively? So it’s almost a supercharged, better screwdriver, but it’s still solving a predefined problem.
Where I’d love for analytics to go and, and I, in speaking with peers in the industry and you know, at conferences, I think everybody’s trying to think through this, is How can analytics actually create an entirely different business models? How can they create entirely different products or entirely business entirely different business models or even businesses if you will. So that’s where I’d love for that to be almost this greenfield thinking on what may be entirely different markets, business models, ways of doing business, clients that we can target, that maybe we can’t now with our existing model, but that actually because of the power of analytics, it becomes either more cost-effective or we can reach people where they are, or we can, you know, open our, open up that territory, that geography that previously would not have been available. So that’s where I’d love to go next.

And, I know we’re not alone in thinking about this because I think everyone’s thinking about, okay, we’ve tackled some of these problems. How do we actually transform our business? You know, there’s transformation is the word, and I think we’re on the cusp, not just within Pacific Life or our industry, but I think, you know, across society and the use of data science to really start to see these become more and more in the day of the day.
So it’s exciting for data science, it’s exciting for this industry and in all industries and data science. And as you see, there’s a ton of open requisitions out there. So for any folks watching or listening that are thinking about getting into the space can’t suggest it enough.

Evan: Awesome. That is a really exciting answer, Rob, and I think you probably inspired a lot of people. We’ll link to anything that you’ve got available, Rob here when the show is ready to air. So make sure you check the show notes there if you’re interested, if you’re inspired by Rob’s answer in the future of analytics at Pacific Life.

Rob, thanks so much for joining us on the show today.

Rob: Evan, it’s been such a pleasure to reconnect, and I look forward to connecting again soon.

Evan: Okay, fantastic. Thanks Rob. Thanks everyone. See you next time.