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2024 Virtual Return to Work Conference, May 14-16

Episode 205: Relaunching in Data Analytics & the Special Project She Did for iRelaunch, with Cynthia Corby

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Episode Description

Cynthia Corby was a financial analyst on Wall Street before taking a 17 year career break. She decided to pursue a masters degree in business analytics as a way to relaunch her career. We met Cynthia when she reached out to us to see if she could focus her final project for her degree on data analysis that would be helpful to us. A dedicated relauncher and looking for a way to pay it forward, she produced a remarkable analysis to update our estimate of the number of relaunchers in the U.S. We discuss Cynthia’s career path, her decision to relaunch by enrolling in a graduate school program and what it was like to be in the program. We also talk about the methodologies and results of the data analysis she did for us. Shoutouts to our own Shannon Amspacher, Director of Marketing and Strategic Partnerships, at timestamp 17:40 and Katharine Bradbury, Senior Economist and Policy Advisor at the Federal Reserve Bank of Boston, at timestamp 19:40.

Read Transcript

Carol Fishman Cohen: Welcome to 3,2,1 iRelaunch, the podcast where we discuss strategies, advice, and success stories about returning to work after a career break. I'm Carol Fishman Cohen, the CEO and co-founder of iRelaunch and your host. Today, we welcome Cynthia Corby. Cynthia was a financial analyst on Wall Street before taking a 17 year career break. She decided to pursue a master's degree in business analytics as a way to relaunch her career. We met Cynthia when she reached out to us to see if she could focus her final project for her degree on data analysis that would be helpful to us. A dedicated relauncher and looking for a way to pay it forward, she produced a remarkable analysis to update our estimate of the number of relaunchers in the United States. We're going to talk about Cynthia's career path, her decision to relaunch by enrolling in this graduate school program, what it was like to be in the program and dive into what exactly is data science.

We will also talk about the methodologies and results of the data analysis she did for us.

Cynthia, welcome to 3,2,1 iRelaunch.

Cynthia Corby: Thank you, Carol. It's great to be here.

Carol Fishman Cohen: Well, we're so happy to have this opportunity to speak with you. There's so much to talk about. But before we get into the methodology, the data science and the research you use to do the actual project, I want to know if you can start with walking us through your career journey, what you did before your career break, what precipitated your career break, and then you were on a very long career break. So maybe some commentary about that.

Cynthia Corby: Sure. I have an undergraduate degree in math from the University of Virginia. And after my undergraduate years, I went to New York City and worked on Wall Street for a couple of years. I was there during September 11th, and during that very big event, a lot of us were reprioritizing. My boyfriend at the time and I decided to get married and moved back to the Charlottesville area where we decided to start a family.

And so I originally thought maybe I would try to couple grad school with new children, but that did not happen because I ended up having four children two years apart. And that was plenty. Along the way, I actually threw myself into a lot of birth and breastfeeding advocacy work and nonprofit work.

For a while I thought maybe I would end up relaunching in the nonprofit sector. But, along the way too, I also really enjoyed substitute teaching at my kids’ school. And finally I realized, that all of the things I like to do did come together with going back to school in a more technical way, so that I could bring all of the people skills that I had really been developing over the last couple of decades into purpose, with some analytical skills that I had not really been using over the last couple of decades, at least not in the same way.

Of course, being a parent is very much to me, I think, a systems engineering feat. But it was definitely time to merge all the skills together. And the master's degree was a good way to do it.

Carol Fishman Cohen: Very interesting. So when you decided you were going to go for this degree, what kind of research did you do? Were you looking at a whole range of different kinds of degrees? Were they in person? Online? Combination? And how did you figure out which one was the right one to take?

Cynthia Corby: Well, locally, there is a business school. I'm in Cincinnati, so the University of Cincinnati has a business school. And I had originally thought I was going to go back to get an MBA after my banking years.

So that's always been on my mind. Since my undergraduate years, the field of data science has really just developed and evolved. And it wasn't even offered when I was an undergrad. So yeah, it had been actually advertised on NPR that the University of Cincinnati has a program in business analytics, and it was ranked very highly.

I had also known from relocating to Cincinnati that I really needed to get to know the community better, because so much of what I'm good at is the networking piece. And I was missing having that group of people in my location who knew my skills. And so I was really looking forward to meeting more people.

It would have been nice in person ‘cause it turned out to be a pandemic year. But, it was a way for me to get to know local professors, local professionals and industries. That really drove me to stay local. There are lots of programs in data science and business analytics that you can do online.

But I actually really just wanted to focus on the local choice for me, and it turned out to be a very good choice because it was economical, and had a very long standing ranking in the field for delivering a really good program.

Carol Fishman Cohen: And, and what did it entail? What kinds of courses do you take, and how long does it last? How does it work online?

Cynthia Corby: So my program and many of these programs require that you've already had the three semester calculus sequence and linear algebra. So having had the math major, that was checked off. There were people in my program that didn't have it all done, and they were doing the last semester of calculus before the program started.

You did not have to know any of the current hot programming languages, but if you'd had a semester of programming in your past, that was highly recommended. And, my particular program was one full year where it started in the fall and it actually is concluding in the next couple of weeks. And there are programs that are 18 months or even more condensed into two semesters.

So yeah. Look at your program and decide what fits best for you. But for me, I could be a full-time student, two semesters in theory, while my kids were in school, and then have the summer semester to do my capstone project.

Carol Fishman Cohen: While you're talking, I'm thinking about, we did a podcast with Michelle Friedman, who's an organizational strategist and executive coach, and it's on the learning curve. And we talk about this concept by Noel Burch, who created the concept in the seventies about four stages of learning. And that sometimes your learning can take a dip when all of a sudden you become aware of everything that you don't know, or you then start learning it. So I'm just wondering, you enrolled in this program, you had I'm guessing, the three course calculus and linear algebra years ago, 20 years ago. You're in the middle of these courses and they're starting to get intense and technical. Did you have any wave of intimidation? Did you have a wave of, "Oh, my gosh. I didn't realize everything that I didn't know." And if you didn't, please talk about that. And if you did, how did you emerge out of that?

Cynthia Corby: I absolutely had that wave many times. The tide would come in, the tide would go out and repeat. As I was applying to the program in January, I dusted off actual calculus books and I enrolled in an online class in Python, because I thought for sure all of these people that only had a couple of years of work experience out of undergrad were going to come in with all of this coding skill that I was not up to speed on. So I taught myself Python and I really reviewed some just very basic calculus. I still have my linear algebra book, and looked up that. I downloaded the syllabi of almost every class I could find online. And I actually walked through the syllabi with my husband who actually works in this field. So actually, very much in an itemized way, I was like, "Is this going to be too hard? Is this going to be hard? Do I know how to do this already? What does that really mean? Is that going to be...?" Because really, I think so much of my journey was about managing not only my time, but like the time of five other people in my orbit. And so I really wanted to go in being able to manage my time as best as I could.

And the wave of being overwhelmed, I really wanted to preempt that worry and it still happened anyway. One of the first classes in the program was somewhat heavy on calculus and it was a kind of probability class. But now that I've been through the program, I am here to tell people that do not let the math part worry you, because the theory behind the data science is helpful, but it doesn't prevent you from just being able to implement these tools. So, know you can understand maybe how any piece of software really works on the backend, but if you really just need to learn how to sum up a column in Excel, you don't really have to understand how that works. You can just do that thing. It's nice.

And I think that the underlying theory is very helpful and I was very happy that I had the calculus sequence done. But I over-prepared is the short answer. I was more prepared than my peer group. And I certainly want to encourage people to put themselves in that position.

It felt great actually to feel like I had all my tools ready to go, but I didn't end up needing a lot of them. Because the program was so well thought out to really help everybody from all walks start at the same place and end up in a certain place together too.

Carol Fishman Cohen: Wow. I hope everyone is listening carefully to that in the audience who might feel intimidated about jumping into a technical or quantitative based program, to hear exactly what Cynthia is talking about.

Cynthia were the classes live and virtual? Or were they recorded and virtual?

Cynthia Corby: There was an evolution because the department was so good at working with the new technology, that they actually even have a professor who specializes in online meetings. And so the first semester was almost all live and virtual.

The second semester, they did an amazing blend of pre-recorded videos. They even had, I think, a philosophy that if you're ever monologuing for 10 minutes or more, that it should be a pre-recorded lecture, so that the students could use live time for much more office hours, Q & A interactive sessions. And so the second semester was a lot more of a mix. It was very helpful to me because previously, there were some classes that were from 6:00 to 10:00 PM on a weeknight, which for someone in my shoes who has kids who can't drive yet, but have to be all over the place, was the worst time for me to be in class. So I could end up in the second semester watching that on my own time, whether during school hours, or even weekend time. They really did a great job.

Carol Fishman Cohen: Yeah. That's super interesting. Let me just ask you a very basic question. Can you define for us, what is data science?

Cynthia Corby: I think at its core, it's using a library of tools to make sense out of all the information that you might have at hand.

And so, when you start hearing words like algorithms and procedures and things like that, it's really, you have a toolkit. And just like if you were pursuing chemistry science, and you have all these ways of approaching a chemistry experiment, you have ways to approach the data to help make sense of it and to really figure out a story that the data is holding that needs to be uncovered.

Carol Fishman Cohen: And the tool kits are ways to control the data and to make it usable for yourself? But do those data sets already exist somewhere, or do you actually create them?

Cynthia Corby: That's part of it. So you might have very structured data where, in your mind, it's like an Excel spreadsheet, where there is a row and you can aggregate things. But you could also be collecting data, say, from scraping the internet. And that could be very unstructured. And so there's going to be a whole realm of tools to help you navigate, pulling that data in, organizing it, cleaning it, making some sense of it.

So much of it is even understanding if the data is correct. There's a lot of acknowledgement of garbage in, garbage out. So understanding your source and knowing if the data really means what it's supposed to mean. We did hear a lot of stories along the way of what happens when the units haven't been correct in previous disasters in history, or what happens when marketing companies can use data in a way that may or may not be ethical to be sharing it in a way that maybe the public doesn't understand yet. So there's a lot of nuance to using all of this information. So it's not all just a coding game. It's also very much a conceptual game.

Carol Fishman Cohen: Interesting. And that was actually going to be my next question. So you mentioned Python and some of these other programming languages. What is the role, when you learn those, how does that fit into the picture of being a data scientist?

Cynthia Corby: Absolutely. So for those of us that are familiar with Excel, my first job out of undergrad was very Excel heavy. One thing that Python and R, and really companies have their own versions of these things too, is they are ways of pulling in the data without changing the data. And then, you can do some functions with the data, you can visualize the data. So anytime you see something being done with it, that would count as a data science act.

Some people, and I've spent a lot of time, even myself, trying to understand the difference between a data scientist and data analyst. But there's a lot of crossover and being able to use the data is why you need R and Python. And really they're just very user-friendly, I think that's why they're so popular. You hear about them now because people have been working with giant data sets for a long time, and they've just been using other things like, we had to learn SAS and that one is still very much used today in a lot of industries.

There's plenty of other mathematical software tools. But R and Python are the hot ones because they are free, and they're really easy to teach yourself. Their online community is really supportive and fabulous. I encourage more people from underrepresented communities to dive in. Because that community is trying really hard to help people learn this tool, to help really bring all these voices and perceptions of information to the greater good.

Carol Fishman Cohen: All right. That's very helpful to me to understand this a little better. Let's get into the project that you came to us about. Can you walk us through, I guess this was some sort of a capstone project you got assigned and how you thought about what you wanted to do and approaching us?

Cynthia Corby: Sure. So part of my master's program is that each student does an individual project, called the capstone, where they are leveraging some or all, or maybe just a few tools that they've learned along the way. And the students are encouraged to go out and find a company that could use some data science assistance. Maybe a project that's been put on the back burner for a while.

And, if not, they can actually work with the professor. But I immediately thought, I have always wanted to have a good excuse to meet you, Carol, but also just to work with people that are relaunching just like me. And I thought, "Well, maybe I can be helpful, because this is a domain, I have domain knowledge of this field of being a relauncher. I certainly have been trying to relaunch in the last, over the last three or four years before my graduate program, without the degrees. So I have a sense of what that community looks like and feels like. And, maybe I could lend that kind of business subject matter expertise to the data that you might have that's maybe not being put to work right now." And so when I reached out to you, I was so happy that you were eager to pursue this project. You referred me to a colleague who absolutely had a perfect data science project that was waiting for me. And so I dove in head first. I even joked, it was my "dessert project" because I had some other schoolwork that had to be done too.

And so I reward myself by really playing with the relaunch data, that I got to learn much more about. And now I can share with other people because the data source that you end up sharing with me is public and it's available to everyone.

Carol Fishman Cohen: I just want to fill everyone in a little bit more, we are so thrilled that you reached out because the project that you ended up working on, and I'll tell everyone about that in a minute, was super interesting to us and it's been sitting there for awhile and everyone will hear in a minute. But I also want to acknowledge Shannon Amspacher from our team who heads up our marketing and is very data-driven herself, and was the one who was able to consolidate a lot of our material that was all over the place, to present to you as a starting point. So that was huge.

But let me just tell everyone about it. When Vivian Steir Rabin and I were writing Back on the Career Track, we got that book contract in 2004. So we were writing that book in 2005 and it didn't come out until 2007, ‘cause we turned it in at the end of 2005 and things took longer than expected.

We actually had a big New York publisher who published it, it was Hachette Business Plus, it was great. But anyway, we had this very intensive period where we were doing a lot of research. We were talking not only to over a hundred, and we were focusing on stay-at-home moms at that point.

Now we look at the relauncher population much more broadly, but we were talking to over a hundred stay-at-home moms who had returned to work. And we were talking to academics, work life experts, recruiters and employers. And, one of the things we wanted to do was estimate the number of relaunchers. So at that time, the way we were defining it, were essentially educated mothers of prime working age who were not in the labor force and were interested in returning to work. If you drill down, that is women between the ages of 25 and 54 with children under 18 with a bachelor's degree or higher who are not in the labor force. And then a couple of studies told us about 80% of them are interested in returning.

So we use that as a ballpark and that's how we ended up with our number. And originally, I'm going to call out Katharine Bradbury, senior economist at the Boston Fed, who was so gracious in hearing what we were interested in finding out, and helping us get this answer by searching Bureau of Labor Statistics micro data, and I'm sure a whole bunch of other data sets.

And Cynthia, you can comment on this? So she's the one who initially gave us our first numbers, and you can talk about numbers in the progression in a minute. And then around 2013, we thought the number was getting old, so we wanted to update it. And we were all so curious what the number looks like now.

We were able to do some rough estimates based on population increases, but to get deep into that data again, Cynthia presented us with that opportunity for her to work on it. So can you tell us a little bit more about your reaction to that assignment? A little bit of a history of the two different numbers that we had prior, and how you thought you would approach this?

Cynthia Corby: Sure. Well, when I first heard the project, before I received all the documentation from Shannon, it was kind of like, I don't know if I will have time to do this in the allotted semester, because I didn't know how much I would be able to replicate or how much digging I would have to do.

There are so many ways to go about a number like this. But because Shannon did put together such a good, essentially like a small booklet, and I bound it in a little binder. The detail that I was left with was so helpful that I could go directly to a data site that had already been curated over years and years, it was trusted.

And so that piece of it was just very quickly taken off of me, because that to me is one of the hardest parts with a lot of these data projects, is making sure you trust your data. So knowing that we had this IPUMS online data repository, which is curated by the Minnesota Population Center, they have curated this data over months and years, and they have it harmonized so that as things change over time, you can still aggregate it and get a meaningful number.

And the information that Katharine Bradbury had left us was not only that data site, but what exact variables she had selected. And just learning how to use that data set took some time. My suggestion to people is that if you are interested in anything related to Bureau of Labor Statistics data, that this data set is so clean and so ready to use, but just learning how to use it is an investment in your own ability to perform some really interesting analysis later. I definitely applaud the Minnesota Population Center for keeping that data and making it accessible to all of us. So, after I figured out how to collect that data, and then you actually extract it.

You have to wait a little bit to get an email that says your extraction is ready. Then you have a choice of how you would like to download your data. And one of the choices is something I had never heard of, and one of the choices was SAS and one of the choices was R. And so I was like, "Oh, okay, R is my really good friend," after a year of being in that. And so I was so happy that, not only do they have some choices within R, there's also an R package for those people who are R users. There's an R package that makes it really easy to not only download the data, but also the kind of information, a data set that helps you interpret the data.

And so with that R package and with IPUMS being trustworthy, I could then get right into what a lot of data scientists feel is like the most fun part, which is treasure hunting and looking for the story. And my first challenge was, can I match that number? And it didn't take me as long as I thought to get there because of Katharine’s very detailed list of her variables.

And I was so excited to be able to cross check that one and previous numbers with Shannon. She had some information that I hadn't had in the booklet, so we went back and forth a couple of times. And so then I felt very confident that I was delivering similar numbers, if not the same, because these data sets, they're fixed in time compared to something that's always evolving, billions and billions of rows of data that's always evolving. This stuff is fixed. So if you want it to go get the same 2012 number of relaunchers with these parameters, it should be the exact same.

Carol Fishman Cohen: Can you talk about the three numbers? At the beginning, I think we started with 2.4 million, and then we went to 2.7 million. And I just want to underscore that the women who take career breaks for childcare reasons remain the largest subset of the relauncher population.

But by no means are we capturing the entire size of the population. But because we started with this number, we were very interested in it, and we had the number from 2005, and then I should say, Katharine Bradbury and team were very gracious in updating it in 2013. So that was the initial number we were focusing on and we wanted to keep consistent. And so that's where we went from there.

Cynthia Corby: So the 2013 number was the first number I tried to match. And I had received a number of 2.7 million relaunchers. When I went back and researched this, I came up with 2,652,838 female relaunchers. And then, with the software or with this coding language, you can really easily just include the men.

And so I was able to find out that in 2013, there were 430,000 thereabouts men having the actual 2013 total be slightly over 3 million relaunchers.

Carol Fishman Cohen: So wait, Cynthia, let me just clarify this. So you are saying the 2013 number, we had originally 2.7 million, and you ended up with 2.65 million women when you tried to match it. And then also about 400,000 men. Are you saying men between the ages of 25 and 54 with children under 18 and a bachelor's degree? So the same demographic except male? Essentially a stay at home dad.

Cynthia Corby: Exactly. That total was about 3.1 million, even in 2013. And, I was very pleasantly surprised because I think the kind of cultural language around whether or not there would be that many male relaunchers, it was very much like they are out there and it's not negligible.

In fact, I did a rough calculation over the last five years, and almost always between 13 to 17% of the total relaunch population is men.

Carol Fishman Cohen: And I just want to clarify for our listening audience that, so that 2.7 million number in 2013 was the raw number of people in that demographic, not in the labor force.

Remember, studies show about 80% of them are interested in returning. Not a hundred percent. So if you take 2.7 million and you multiply it by 0.8, you get about 2.2 million. And that was the number we were using for a long time of how many relaunchers were out there. We weren't at the time including the men, but it's so interesting that we should have been, and that was a sizable number.

Anyway, just want to point out that nuance to our audience who might be paying attention and wondering, what if there was a little discrepancy? There's no discrepancy. We were taking 80% of that.

Cynthia Corby: Exactly. And since iRelaunch is being much more inclusive in their definition of relauncher, I want to make sure I did both the female number and the male number. And a side note, this is why many perspectives are needed because as we know that gender enumeration is probably going to change over time. And so that's going to be another thing with data is how you define a lot of these demographics.

Carol Fishman Cohen: Yeah. It's important. And I'll just make an additional note, that does not include the relaunchers who take eldercare career breaks, or have a personal health issue, or an expat experience, or military spouses necessarily, although some of them could be in that number. And then people who may be in the military who then take a break after military service.

So there are a lot of other categories that we're not capturing here, but we are focusing on this big sub set.

Cynthia Corby: Exactly. And what was really nice to see is that because you're downloading the monthly data from this IPUMS set, you can actually see the number of relaunchers, as reported anyway, by the Bureau of Labor Statistics micro data, how it changed month by month, and how it really did peak very much in April of 2020. At one point we had about 4.6 million relaunchers out there. It's definitely creeping back down as companies are learning how to help parents work from home and things like that. And all sorts of people navigate the balance between work and private life.

So after being able to match the 2013 and the 2006 numbers, I just made sure I could report actually what the official relauncher number was every year since then.

And right now, 2020 ended with the average number of relaunchers being 3.7 million. And then the year to date average, which was through May of 2021, was 3.4 million. So it is creeping back down, but it is still, the total relauncher number is still above 3 million people.

Carol Fishman Cohen: And can you break that down into female and male? The 3.4 million?

Cynthia Corby: Yes. The female number is 2,895,000. And the male number is 523,000.

Carol Fishman Cohen: Okay. So just to clarify, ‘cause I don't think we ever even said what the original number was in 2005. I had said 2005, it's actually 2006, and that was about 2.4 million. That's the women only. And then 2013, remember, it was about 2.7 million. And now, Cynthia, you're saying that the year to date number is 2.9 million for women. And then of course we have about half a million men, male relaunchers in that stay at home parent category.

Cynthia Corby: You got it.

Carol Fishman Cohen: Interesting. So of course this is a number that we're endlessly fascinated by, and to see just the trajectory in itself is interesting.

And so again, that's a hundred percent of the people who are not in the labor force in that category. if you're taking 80% of 2.9, you get about 2.3.

Cynthia Corby: It's a lot of talented people.

Carol Fishman Cohen: Yes. All right. Interesting. Any other commentary, Cynthia, about the process or were there any surprises or any other comments on the project?

Cynthia Corby: Oh, I think there's so much more to learn. And I guess my major comment is, "I've just scratched the surface." So I'm so excited for iRelaunch, and some of the information treasures that are awaiting you.

Carol Fishman Cohen: Thank you. And I should say that Cynthia presented to our team and underscored that this is the tip of the iceberg in terms of the data that's available, and the type of analysis that can be done. So that's something that we're going to be taking a look at.

So Cynthia, we're at the end of our podcast time right now, that went very quickly. And I want to finish up by asking you the question that we ask all of our podcast guests. And that is what is your best piece of advice for our relauncher audience, even if it's something that we've already talked about today?

Cynthia Corby: I suggest if you are considering going back to school as a way of relaunching that you not doubt yourself. And that my biggest piece of advice, is to make sure you have a support person or team very much like we've learned along the way, that it takes a village.

I think you need a person that has done it before. So if you can find someone that has done that graduate program before, help you stay confident that you can do it and you're going to get through it. And then also people that just know that you're a talented, smart person and that you've got this.

And so I would just surround yourself with one or more people, on those days where you're like, "What am I doing?" "Who do I think I am?" That person is like, "Are you kidding? You are a rock star." And you are, and that's the thing. You are giving so much to your cohort that they are happy to have. So, know that you are just what your graduate program needs too.

Carol Fishman Cohen: That's excellent advice. And also it's making me remember something I wanted to comment on earlier was, that you had a course, a degree that required a capstone project. And we talk about how capstone projects as part of degree programs, certificate programs, even course itself are so important, because you not only have the course, but you have the capstone project experience, which is sometimes even more interesting to talk about than the course itself when you're having talking with professionals in the field or in an interview.

Cynthia Corby: Absolutely.

Carol Fishman Cohen: Did you find that at all?

Cynthia Corby: I did. I've had a lot of fun talking about my project with all of you, which is perfect, which is exactly what I wanted. Because it was another excuse for me to make sure people know about relaunchers, and to make sure people know that this is a wonderful resource for them.

I can now share it with my employers, which to be honest, almost all my interviews were with men. And I could say, this is a company that's out there that's helping people like me find great positions. So it's so easy to talk about something that I'm interested in. And yet I did use all these tools that my graduate program taught me how to use. So absolutely.

Carol Fishman Cohen: Well, we are so fortunate that you reached out to us, Cynthia! And we love that we have you in our orbit now. And we're so excited about your relaunch, and we're also so grateful for you for speaking with us today.

Cynthia Corby: I am so happy to be able to share. And please let folks know they can find me, because if they need me to be that person that's been through the program before, I'm absolutely happy to say, "You've got this and have no fear." I'm here to cheer you on.

Carol Fishman Cohen: Cynthia is very generous, but you can reach out to her on LinkedIn. She suggested that to us earlier, to me earlier. So it's Cynthia Corby?

Cynthia Corby: Yes.

Carol Fishman Cohen: C Y N T H I A. C O R B Y.

Cynthia Corby: Yep.

Carol Fishman Cohen: Okay, excellent.

Cynthia Corby: I'm the UVA undergrad, University of Cincinnati Business Analytics master's student who will be posting that she officially has a master's degree in about a week or two.

Carol Fishman Cohen: Woo! So excited. I'm glad we got to end on that note. That's even better. All right. Thanks for joining us today, Cynthia.

Cynthia Corby: Thank you, Carol. Have a great one.

Carol Fishman Cohen: Yes, you too.

And thanks for listening to 3,2,1 iRelaunch, the podcast where we discuss strategies, advice, and success stories about returning to work after a career break.

I'm Carol Fishman Cohen, the CEO and co-founder of iRelaunch, and your host. For more information on iRelaunch conferences and events, to sign up for our job board and access articles, work tools and resources, go to

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