MEL #047 | From Curious Tinkerer to Enterprise Tech Leader through Applied Scientific Thinking with Sears Merritt

MERRITT (00:00)

What’s one thing we know how to do really well? 

Maybe better than many others in the business world that don’t have a kind of formal engineering or scientific training. And so run the scientific method. Ultimately, what are you doing when you practice that? You’re starting with a hypothesis. So you’re not saying you know the answer. You’re saying you think you know what might happen. And then you’re systematically progressing through a series of steps to validate whether or not what you think you know is actually correct.

ADAMS (00:53)

In this episode, I speak with Sears Merritt, Head of Enterprise Technology and Experience at MassMutual, where he is responsible for the company’s technology, cybersecurity, data, and artificial intelligence strategy and vision. Sears started as a curious kid who loved building radios, Lego creations, and computers, which led him to study electrical engineering. His early career moved from telecom networks to healthcare IT, then into data science and AI. Along the way, he continued his education, earning a master’s degree in telecommunications and a PhD in computer science from the University of Colorado at Boulder, and an MBA from MIT Sloan. In our leadership segment, Sears talks about how one of his biggest leadership challenges was bringing data science to life inside a large insurance company at a time when few people understood what it was or why it mattered. He had to educate stakeholders, separate hype from reality, and lead a broad change effort across the policyholder value chain.

That work required constant communication, storytelling, and influence to turn abstract models into tangible business impact. Sears encourages engineers who want to lead to embrace ambiguity rather than chase perfect static answers. He reframes leadership as applied scientific method in a changing world where hypotheses, experiments, and learning loops are more valuable than certainty. His core message is to develop agility, comfort with change, and a mindset that treats leadership decisions like iterative experiments rather than one-time bets. 

Explore the full episode summary, including guest bio, key takeaways, transcript, and recommended resources in the show notes at https://drangeliqueadams.com/podcast/. 

Without further delay, here’s my conversation with Sears Merritt.

ADAMS (02:31)

Hi, Sears. Welcome to Mastering Engineering Leadership.

MERRITT (02:34)

Thank you so much for having me. I’m really excited and looking forward to our conversation this afternoon.

ADAMS (02:39)

Yeah, me too. Can you start by telling us how you got into engineering as a career path?

MERRITT (02:44)

Yeah, you know, all started, I mean, jeez, looking all the way back, you could say I got started in engineering at a very young age. I always enjoyed problem solving, building things, whether that was toy radios or Legos or building computers at one point. But I’ve always had a passion for building tools that people like to use and ultimately that make an impact in people’s lives.

⁓ so I, I took, that desire and I guess you could call it curiosity and certainly, you know, went to college, did electrical engineering as an undergrad really, really enjoyed it. ⁓ the, the, things I found most interesting and fascinating. This is kind of back right as the internet was starting to take off. so I got really interested in networks, telecommunications, information theory, signal processing, and things of that nature. So really spent, ⁓

spent a lot of the free electives that I could choose focused on that. And then got out of school and pursued a technical career path for a while. So started off in telecom, worked back then for a company called Level 3, which is since…

been bought and sold and turned into a lot of different flavors, but ultimately, right there, a big global network provider. At the time, they had one of the largest fiber, I think, MPLS networks around the world. This was kind of early 2000s, but worked for them for a while and then transitioned into healthcare. And at that point, I would say veered away from kind of traditional.

engineering as I’m sure many listeners would think about it and started to get into more information technology like engineering. applying a lot of the network and.

and infrastructure skills that I picked up in school and earlier in earlier jobs and applied that and again, more of a information technology setting. So I computers, systems and designing those systems to make sure that we could provide availability and performance to the users of those things. again, one thing has led to another and had a really, really interesting career that’s taken a few turns, went back to school and

focused on computer science, was really passionate about getting back to what I really enjoyed as an undergrad, which is just creating knowledge and looking for fascinating problems to solve. So did that during my PhD and then went back out into industry. And this is kind of the period where we were all kind of going through big data.

machine learning and of course AI is everywhere at this point, but we were really at the beginning of what that big trend was. So came out of school and joined MassMutual where I’m very proud to be today and worked with the company to help build their data capability and now oversee the technology for the company.

ADAMS (05:35)

Yeah, that’s an amazing career trajectory. I want to go back a couple of steps. I’m curious about what drew you away from the networking side and more to the data and information side. Were there specific experiences that you had either in school or in your early career or other insights that you gathered? Can you talk a little bit about that transition?

MERRITT (05:55)

Yeah. So that transition came, that came during grad school. And so I originally went back to grad school thinking I was going to pursue a purely academic career. I also thought at the time I was going to pursue an academic career that was focused on those initial passions to think networks, information stuff, and things of that nature. And as I got involved and started to

basically meet more professors, explore more folks across the department, I realized there was so much more to learn and there was so many ways you can apply an engineering mindset to lots of different problems. And that led me as I would say I would probably halfway through, maybe a quarter of the way through.

my PhD to recognize I really want to take all of those engineering skills and those science skills and apply them really in a different context. I did a dissertation that was much more focused on networks, but think of those as mathematical quantitative networks, social networks, and things of that nature. Got a lot of fascinating datasets about people.

groups of people and how those interactions actually come together. And you can express a lot of those things as a network. And then just spent a lot of time, again, kind of applying a lot of the things I had learned earlier in my academic career to those particular areas of study. And that really got me excited about, I would just say expanding what I wanted to work on and explore. And yeah, that’s kind of where it all started.

ADAMS (07:28)

you said you also, you were thinking you were going to go into academia and then you didn’t. Can you talk a little bit about how that transition happened?

MERRITT (07:36)

Yeah, so, you know, I don’t know if it came through in some of my opening remarks, but I’ve always been really curious. I love to learn. I love to do science. I love to create knowledge, but I also like to build stuff that ultimately people use. And what I discovered as I went back to grad school was that

it was highly likely, certainly not a zero, but it was highly likely that I was going to spend the majority of my time much more focused purely on knowledge creation. So come up with a hypothesis, pursue a big research program, answer the question that’s embedded in that program, and then probably move on to the next thing. And

⁓ As I was doing that through my dissertation, I realized I actually wanted to take what I had discovered and turn that into something people can use. And so that led me to think I was going to be much more satisfied finding a role, finding a company and a set of things to work on that was going to balance.

my desire to do science, but then also take what comes out of science and turn it into something that impacts people’s lives. So that was kind of the big aha moment for me that said, I’m more likely to be able to pursue a career like that if I do it in industry, as opposed to trying to do it within a department at a university.

ADAMS (08:58)

Yeah, and I think, of course, that makes total sense. we’re starting to see a trend in more students that are pursuing graduate degrees really wanting to go into industry. So I think that a lot more students are starting to have that realization that you had, which is to say, I also want to actually put these things into practice in a much more tangible way. There’s obviously still a huge place for tenure track academia, and that’s hugely needed.

Did you find that it was an easy transition into industry or were you seen as sort of like, you’ve got a PhD, what are you doing here? Can you just

MERRITT (09:33)

Yeah, that’s a really good question. think, I think given how I kind of have progressed or how I was progressing at that time, I found the transition to be easy for me personally. But I attribute that largely to the fact that there was a period of time in between when I came out of undergrad and I spent five or six years working. So I had gotten

experience knowing what it means to be part of a company and working with customers and helping grow a business and think about strategy and those kinds of things. So when I came back out, there wasn’t a real big shock and awe moment of like, wow, you got to really think about things a little bit differently than you would if you were just doing pure research. But on the other hand, I will say as we grew,

the data science department at MassMutual, we did very deliberately go out and hire a large number of PhDs. And almost all of them, not all, but almost all of them we pulled straight out of the lab. And so for them actually, there was some learning and some adjustment that we had to provide for them. I think what I always say is,

⁓ The big pitch I would give folks is we’ve taken the best of academia and left the rest in the lab. Right? So what does that mean? You don’t have to write grants. You don’t have to, you know, kind of worry about where the next project is going to come from. We’ve got a large list of those things, and all of them are going to be focused on making an impact in people’s lives or for the company. And the best part of that is you have free reign.

of how to pursue solutions to those problems or answers to those questions. And we’re going to create an environment where you can do that, that looks very much like a lab. So everything that we would do would always start with a hypothesis. would start with a formal, here’s what we think is going to happen. Here’s how we think we’re going to measure the outcome. Here’s the type of information and data we might need to go collect. And here’s some experiments that we are going to run to actually validate what we did.

⁓ actually works or again, answers a particular question. And folks just love that. And then as we would go and do that work, right, much like we would write papers and submit those to conferences or journals for peer review and publication, we would replicate that same type of culture within the department. So when a project was finished, we’d memorialize that work with a white paper. We would do peer reviews and have folks present the work and critique it.

in positive ways, right, to get to the right answer or get to a higher quality answer. And that was really, as I look back, that was one of the kind of the key ingredients to building that part of our technology department. And it’s something we still do and feel really proud about doing today.

ADAMS (12:23)

Yeah, I mean, think that’s great. It sounds like you all were able to create somewhat of a soft landing for a group of people that really had the skills and expertise that you wanted, but maybe were not used to the environment and the needs and maybe even the pacing of how to solve these problems and how to actually, put things into practice in a way. And so it sounds like you really created a great environment for folks to be able to do that.

And so continuing on, as you said, you love to learn. And so I know that you also went back to school again to get an MBA, which is where you and I met. were both at, ⁓ had got our executive MBA at the Sloan School. Can you talk a little bit about your decision to do that and what you were hoping to get out of an MBA?

MERRITT (13:04)

Yeah, so like you said, and I know I mentioned this a couple of times, love to learn. And I’ve always had a desire to be an entrepreneur, to be involved in business. ⁓

And as I had progressed in my career at MassMutual, it just became clear that there was a real opportunity for me to just, you know, selfishly go have an opportunity to learn more about business. And at the same time, take what I was going to learn and give it back to the company in the form of leading, you know, a good chunk of the organization, helping contribute to strategy and help others understand.

what we were doing and why, and an MBA really helped me do that. I don’t know about you, but some of my favorite classes were the macroeconomics courses that we took. I know that winter semester.

⁓ We took a really fascinating course in finance, options trading and currencies and all sorts of interesting things there. Our strategy courses were really fascinating. So it was just a terrific experience. And it really allowed me, I think, to recognize that not everything’s black and white. As an engineer, we are thought to think about problems mathematically.

And more often than not, right, there’s either a spec, there’s some thresholds, there’s things that look and feel much more black and white, right? An answer can more often than not be right or wrong. A solution will work or it won’t. And in business, that’s not the case.

ADAMS (14:32)

Yeah, I agree. it’s interesting that you mentioned that in particular, the not black or white. It’s something that I bring to my students in my engineering leadership course where I talk a lot about engineers are often seen as not good with ambiguity because we do tend to think of things as right or wrong.

MERRITT (14:49)

Many things are gray. And I think that’s one of the big things I took away from spending time at Sloan and learning from others and certainly you in our time together there.

ADAMS (15:11)

Sears, can you give us an example of how you use the leadership skills in your work?

MERRITT (15:15)

Yeah, know, these days I think I use them every minute of every day, honestly. A lot of my job is focused on working with others, influencing others, helping others understand what problem it is we’re trying to solve and encouraging them to think about how we need to approach solutions to those problems. So every day.

is a day that I am practicing leadership again, either with my team or certainly with others across the organization. I think one of the first times as I kind of reflect back on my career so far at MassMutual, one of the first times I really had to practice leadership.

was in actually bringing data science to life at the company. this is, you rewind the clock about 10 years, nobody really knew what data science was, right? We were reading about that in headlines and hearing about things in publications, right? You’d hear about it at startups and things. And MassMutual had recognized that that was a real opportunity.

for them as a big insurance company. And surprisingly, right, if there’s anyone that’s more well-suited to use something like data science or AI, it is an insurance company. Kind of the core of how that business operates is really predicated on collecting a lot of information and using it to forecast what’s going to happen six months, 12 months, and in our particular case, decades to the future. So it’s a really well-positioned to use something like that to optimize their business. But nobody really knew how.

And no one really understood how they were gonna do it and what to expect on the other side. And so one of the big parts of my job was leading others, whether those were folks on my team or colleagues across the organization, leading them through that process and helping them understand the power.

of what you can do with a data science team, with the right types of technology, certainly the right data sources, and how to apply those in a business setting, right, to actually solve problems and improve process, improve customer experiences and things. And that took a lot of influence, going back to some of the things we had mentioned around ambiguity. It really took a lot of time and…

I would say communication of explaining the why and the how and ultimately what the impact was going to be if we were successful in working together to achieve a particular goal. So I’d say that was my first kind of foray into real, maybe you could think of it as enterprise leadership. And I really enjoyed it. I particularly enjoyed it when it led to impact.

where a problem that we solved actually made its way out into production and impacted policyholders, impacted employees, made our financial advisors lives easier. That was kind of the big outcome and what was most satisfying to me.

ADAMS (18:07)

Yeah, absolutely. I’m wondering, can we get a little tactical Because I can envision that as you were bringing the data science and you understood the power of it and you’re in a context where maybe they don’t fully understand the power of it, there’s so much information out there about how great it is, how impactful this can be.

or maybe it doesn’t work. mean, so you’re both internally trying to make a change and create a vision and help people understand how this can work and dealing with influencing people to make a change in an organization, Any change, but you’re in a field where there’s a lot of headlines about these sorts of topics and there’s a lot of consultants will give you white papers on these sorts of topics. And so you have to try to manage.

normal internal change with external macro information. I’m just curious, how you thought about your communication plans and your strategies for navigating kind of both sides of that coin

MERRITT (19:04)

Yeah, maybe another way to say it is helping educate people on how to separate the signal from the noise. Right. A ⁓ lot of what you hear can mean a lot of different things to a lot of different people. we would always start in the most concrete of terms when we, let’s see, let’s back up. So the way that we approached all of this type of work was by looking across.

ADAMS (19:11)

Exactly.

MERRITT (19:27)

what we call the policyholder lifecycle. Others might call it a value chain. But basically, right from the time you want to think about acquiring a policyholder or a customer all the way through to the end where that customer either chooses to move on or the service or the product actually that the lifetime of that thing comes to an end. So we mapped all of that out.

And then we just systematically went through each phase and worked with leaders across our business to ask very, very specific questions, right? So if that was at the kind of the beginning of the chain and we’re talking about customer acquisition, we would say, how do we more efficiently acquire customers? How do we more efficiently match customers ⁓ with a particular type of product or a service? And we would sit down

⁓ with those particular groups. And in this case, right, you could think about that as like a marketing department. And we would map out how do you run a marketing campaign? How do you choose who you wanna communicate to and what do you wanna communicate? What type of information do you use to generate the communication and to identify those target audiences? And then maybe most importantly, how do you define success? What’s the measure that says,

the communication that you shared with a set of consumers ultimately delivered the impact you were hoping, right? And ultimately that’s typically conversion rates and things like that. If you’re talking about a marketing context, particularly digital. So we would sit down and.

with a marketing team map that journey out, right? So from the time we identify there’s a campaign that wants to be run all the way to campaign execution and then performance evaluation. Then once we’ve mapped that process out and we spent a lot of time defining in quantitative terms what success looks like, we then said, what information do we need to make that process more efficient?

and make the outcome more impactful. So think again, in a marketing context, conversion rates for a particular campaign. We would then go and collect information, internal.

and external, and then apply machine learning, artificial intelligence. Many methodologies quite frankly that exist in engineering. We call these things special names, but more often than not, you can actually find those same types of methods being applied in an engineering context to solve a problem, right? Radar, great example, Kalman filters and things of that nature. But at any rate, we would identify those methods and those data.

apply those two things together, ⁓ estimate what we think is gonna happen in terms of an efficiency or impact, and then actually test it and start to run campaigns and measure improvements, and then talk about those improvements with the business partners we were working with so that they could actually see it come to life.

When we had a handful of those, so this is all the way at the beginning, when we had a handful of those that were successful and some that were not, then what we actually ended up doing was asking the folks that worked with us to share those stories with us. So it wasn’t the, what I always say, it wasn’t the technicians, right, their skills. It was the people that benefited from using those skills, telling others about how it changed.

their part of the company for the better. So we would facilitate lunch and learns. ⁓ We would organize quarterly meetings or we would invite anybody that wanted to join across the whole company. And then we would put folks that partnered with us up on a podium.

and kind of let them tell the story. And that led to just a tremendous amount of influence and impact in terms of educating people on what this stuff is and what benefits it can provide. And that kind of just created the momentum we needed to grow the team and ultimately make more impact.

ADAMS (23:18)

All right, Sears, as we wrap up, what advice do you have for engineers who want to pursue leadership roles?

MERRITT (23:23)

Yeah, know, the advice I would give is actually a little bit of what we talked about earlier in our conversation. So as engineers, right, we’re quantitative in nature, we’re problem solvers, and we tend to view the world quantitatively, and there’s usually a right answer and a wrong answer. And in leadership, more often than not,

there’s multiple ways to get to an answer. And you might not know that it’s right or wrong, right? Everything is very ambiguous. And that’s largely because the world’s not stationary. Things are changing underneath you by the minute, whether that’s the market changing, whether that’s kind of social forces and consumer preferences and employee behavior, competitive dynamics between businesses, the world is always changing around you.

And so again, more often than not, you’re gonna have to think very dynamically and get comfortable with a very high degree of ambiguity and helping others work through that. So creating an environment where you embrace change and maybe even encouraging folks to manage to change is actually a great set of skills to learn.

And I’d also say when you’re first starting out, you may be focused on solving a particular problem. And maybe you’re going to get frustrated when you think you know the answer, but something around you has changed and that answer no longer holds. So doing your best to bring a mindset that’s focused on change, being agile.

being willing to change your mind, being willing to account for new information as you come in and again get to work solving problems or building systems is I think a great set of skills that will carry really any role that you’re going to pursue, but particularly roles that require leadership.

ADAMS (25:12)

And you mentioned that, in your role now, part of what you have to do is to help others learn how to adapt to change, learn how to manage to change. Are there any strategies that you use now as a leader and maybe even a coach that you implement to help others start to become more comfortable with change?

MERRITT (25:32)

Yeah, know, in the corporate world, you’ll hear lots of folks talk about being agile, being resilient, failing fast, not being afraid to fail. Those types of phrases are really important. Going back to kind of where we started, right, we’re engineers, we’re scientists. What’s one thing we know how to do really well?

maybe better than many, many others in the business world that don’t have a kind of formal engineering or scientific training. And so run the scientific method. Ultimately, what are you doing when you practice that? You’re starting with a hypothesis. So you’re not saying you know the answer. You’re saying you think you know what might happen. And then you’re systematically progressing through a series of steps to validate whether or not what you think you know is actually correct.

That type of mindset, right? You’ll hear about it. Folks will call it experimentation. Folks will call it continuous improvement, but it’s ultimately about making systematic changes or improvements to a particular problem or a situation, observing the results, and then updating your beliefs on what you think is gonna happen next. That’s effectively managing through change. And if you get really good at doing that well,

and executing that process as quickly as is appropriate, you’ve learned how to manage through change, or at least I would argue you’ve learned how to manage through change. And you can contrast that with a mindset that I would say is much more deterministic and a mindset that is maybe more focused on the world of static and things don’t change. And so you’ll make series of decisions as though you know the answer.

And if those decisions become very big and they’re incorrect, right, you end up with not fast failures, but really expensive, large failures that take a long time to correct. So I think to the best of folks’ abilities, looking at the world through the lens of scientific method, leverage hypotheses, leverage a mindset focused on experimentation and quantitative evaluation of results.

is something I think that can serve everybody really well, particularly when you mix it together with a leadership mindset. Because right, more often than not, as a leader, you’re helping others and you’re leading teams that when you put them to work and get them focused on a goal, they’re gonna make much more impact than you could on your own.

ADAMS (27:56)

Sears, thank you so much for sharing your insights with us today.

MERRITT (27:58)

Thank you. This was a great conversation.