The Slow Road to Entrepreneurship: Learnings From Early PayPal

We all know the stories: Zuckerberg, Jobs, and Gates dropped out of school and founded three of the companies that define our world. They went from college students to entrepreneurs, no transition required. But that’s the exception; to generalize Dave McClure’s instant classic, those of us in Silicon Valley’s 99.9% have to content ourselves with being relatively late bloomers.

That’s reflective of a larger narrative that dominates much of the talk around entrepreneurship: the narrative implies that one is either 0% entrepreneur or 100% entrepreneur. Such a binary classification implies that entrepreneurs have to go from 0 to 100 in one step. While there’s some truth in that — you won’t completely understand the thrills, stresses, and demands of starting a company unless you do it yourself — you can certainly prepare yourself for future entrepreneurship by being a part of early stage companies with people who can help you learn quickly.

My own story is certainly reflective of that: I wasn’t ready for entrepreneurship at age 21 or 22. By my late 20′s, I was aching and ready to start and grow a company. Here’s how that change happened.

My first substantial job experience after college (following eight months as an engineer at a failed startup) was at early-ish PayPal. I was part of the company’s growth from small in 2000 (a very unprofitable hundred person startup) to huge in 2004 (massive subsidiary of massive Ebay).

As a new employee, I knew nothing about fraud detection and received minimal guidance. Somewhat lost, I added very little value to the company my first few months. Three months after joining PayPal, Sports Illustrated wrote an article about my side project, Team Rankings. I sensed some irritation from my boss Max: I was doing only so-so work at PayPal, but was getting significant publicity for a side project. That irritation seemed unfair to me at the time; having now been a founder myself, I can completely relate. Founders want people on their team executing at the highest level; seeing someone perform better on a side project than in the office doesn’t send that signal.

Fortunately, I soon found my way, and (with some help) figured out how to turn massive amounts of data into statistical models that could accurately predict fraud. I felt like I was living on the edge because I eschewed the “easy” off the shelf enterprise tools others were using. Some 23-year-olds rebel by using mind-altering drugs or traveling the world; for me rebellion was writing software from scratch to build statistical models. And this defined me: any job other than predictive modeling struck me as superficial, scientifically empty, and not worth doing.

My job at PayPal focused and insulated me. In 3.5 years at the company, I did one thing: build technology to predict fraud. Most of what was going on at the company — operations, usage, product, competition, finance — were of no concern whatsoever. I became very skilled in a few very specific areas, but knew very little about the goings on across PayPal.

If I had a publicist, I’m sure he or she would tell me to broadcast that I magically learned how to build a business while I was at PayPal: surely that magic would cement my place as part of an all-knowing PayPal Mafia. Alas, I don’t have a publicist, so the truth will have to do: PayPal taught me very little about how to build a successful startup.

But the PayPal experience was formative in two key ways. First, it showed me that talented, driven, resourceful people with virtually no knowledge of an industry could become skilled in areas they’d never known about before (for me: fraud detection) and collectively build a large Internet business and change the world. Second, my colleagues at PayPal set a bar for the caliber of thought and effort that I now expect from those I work with.

After several great and educational years, by early 2004 my job at PayPal had become routine. Fifteen months after Ebay acquiring us, the company’s combative, execution-focused culture had been swallowed by Ebay’s relentless drive to maximize employee time spent in PowerPoint meetings. I didn’t have deep insight into the business of PayPal and Ebay, but I knew I didn’t want to play the big company game. Yet for financial reasons, I was motivated to stay around to vest my remaining stock options.

Researching my choices, I discovered that Ebay had a policy which allowed employees to work just 24 hours a week, while continuing to fully vest their stock. This held a lot of appeal — especially since I was interested in spending some time helping out some friends who were working on a new site called LinkedIn.

Ebay’s lenient vesting policy was likely designed for new mothers or those with health issues — not 26-year-old males interested in moonlighting at a startup. That didn’t dissuade me: I soon shifted my schedule to one where I worked 3 days a week at PayPal and spent the rest of my time at LinkedIn. And the LinkedIn days were a lot of fun, as I got to work with a small team, focus on a completely different set of data problems, and understand how a social network could rapidly grow.

At this point, in the first half of 2004, my boss Nathan at PayPal was trying (struggling) to find cool stuff to work on, so we spent some time with other groups at Ebay looking for interesting data problems to solve. But I soon realized that once I started to look down upon my employer, it would be difficult for me to do top-quality work. I was still building good fraud models, but I was no longer psyched about my job at PayPal, and the caliber of my work certainly suffered. I admire those who can be completely professional and work at full intensity for anyone at any time, but I’m not like that. When I’m excited about a project and a company, I’m hard-working, clever, and efficient. When I’m coasting, I’m none of those things.

Trying to foster more commitment, Nathan came to me in July 2004 and told me that I had to choose between working full-time at PayPal and leaving. I’m guessing he thought this would push me to increase my commitment, but it had the opposite effect: I was bored, disgruntled and antsy, and I was going to leave. I left PayPal in August and darted off to France for a month of cycling.

At this point, I had aspirations of starting a company some day. I’d had some entrepreneurial experience with Team Rankings (more on that later), and had built up some skills at PayPal. At PayPal, I’d worked on some other side projects that could have turned into their own companies (none amounted to much). But I didn’t have in mind a specific company that needed to be started, nor was I compelled to start a company just to start something. And LinkedIn seemed like an attractive place to be: a strong 15-20 person team, an innovative and useful product, a really interesting data set. So I decided I’d join LinkedIn full-time.

I’d spend two and a half years at LinkedIn. I was the first analytics scientist and would lead what’s now called the Data Science team. Unlike my insulated time at PayPal, my years at LinkedIn would get me very close to the business and the product, piquing my interest in a much wider array of topics. Ultimately, that experience would nudge me to jump off the entrepreneurship cliff and start my own company. In my next post (follow me on Twitter), I’ll tell the story of those two and a half years and of the key experiences that led me to start something myself.

Data Science is the Old New Thing

In the last six months the term “Big Data” has reached the mainstream media. Companies are furiously hiring brilliant data scientists to make sense of all the data they have. Most revolutionary: I no longer need to sheepishly disclose that my college major was “Mathematical and Computational Science.”

Is this a step forward? Absolutely.

Does it mean that those same companies are doing all they can to use data in intelligent ways? In most cases, no.

The de facto current “data scientist” model is the third of four steps to data utopia. Here are the steps:

1) Null Set. You don’t collect a lot of data. You don’t know what they don’t know, and you operate solely based on intuition. No serious Internet companies are here, but some small startups are, as are most small offline businesses.

2) Collect Only. You collect data, but you don’t really know how to use them. Maybe you have non-technical marketers telling engineers to run specific queries. This rarely works, because most (not all) marketers don’t have an analytical understanding of the technology or the product, and the engineers just do what they’re told. This is still pretty common.

3) Data Economics. You collect data, but have a line in the sand between builders of products and those who use the data. Your product and engineering teams spec and build features; the Analytics/Data Science team takes the data available to them and uses them to figure out cool stuff.

4) Data Hacking. Your systems (technical and human) are structured so that data geekery is integrated with product and engineering. Your team asks not (only) what they can do with the data they have, but how they can get the data they want. This requires really smart data geeks who can understand how to design a product and can write code.

Let’s say Twitter decides they want to figure out how much coffee each of their users drinks.

If they were at Null Set, they would be completely lost.

If they were at Write Only, they might look at the frequency with which users mention “coffee”, “Starbucks”, “latte”, and maybe “caffeine” or “cafe”. Those with two or more mentions would be pegged as heavy coffee drinkers. And their accuracy would be abominable.

If they were at Data Economics, they would have a couple of smart data scientists, and they’d probably manually classify the people they knew as heavy/light/non-coffee drinkers. They’d use those manually classified accounts, plus keyword data and connection data, to build a statistical model that estimates the coffee consumption for any given user. They’d do okay — they’d uncover keywords that are more predictive than the marketers’ list from Write Only, and find one or two surprising correlations — but they still probably wouldn’t be able to build a great model because the data Twitter has are not very predictive of coffee drinking.

Were they at Data Hacking, they’d do everything in Data Economics. But then they’d also build out a dozen or so features to yield future insights. Maybe they’d suggest following Starbucks or Peet’s and use acceptance as a proxy for liking coffee. Maybe they’d create a “tweet about my coffee” iPhone/Android app. Maybe they’d ask a small percentage of people who’d just tweeted from a coffee shop what they ordered. Maybe they’d ask users who just tweeted about something coffee-related if they wanted a coupon for a free Starbucks coffee; presumably those who accept are much more likely coffee drinkers than those who aren’t.

I offer no guarantees of the effectiveness of any of those ideas. The point is that that they’re interactive, and they facilitate iteration: hack, analyze, hack, analyze. The hackers are the analyzers, and vice versa.

Want to measure reputation or influence? Algorithms on top of the existing data from Facebook/LinkedIn/Twitter aren’t enough — you need better data. Cubeduel — which allows users to express a preference between two people they’ve worked with — is an interesting product because it’s closer to “real” reputation measurement. But on the reputation front, the three big social networks haven’t built a lot of clever measurement features themselves.

And that’s not unique to them: few if any Internet companies are in the Data Hacking phase today. The good ones are doing Data Economics: they have great data scientists (or search relevance teams, etc.), but with scientists who are siloed. Here are the data available to you, they say, go figure something out.

That’s limiting, because there’s an entire class of problems that need Data Hacking brilliance to be solved. Andrew Chen’s post about Growth Hacker being the new VP Marketing is an example of companies moving to step 4 to improve their distribution: growth hackers embody the “data + dev + product” approach and do better on distribution than anyone else. In the next few years, we’ll see visionary data hackers tackle more of our deepest, most challenging problems in interactive, iterative ways.

The Visionary and The Pivoter

A tale of two startups

Last month, my startup of 4.5 years, Circle of Moms, was acquired by Sugar. I’m proud of what my team created over that time: the product behind a large and strong community of moms, a set of technologies that allowed us to move quickly and make sound data-driven decisions, and a positive team culture conducive to both good work and employee happiness.

A month out, I’ve had a little bit of time to reflect, and the lessons I’ve learned from the process are still fresh in my mind. By almost any measure, Circle of Moms was a success, but not a “rocket ship”, either of the quick (YouTube) or slow (Facebook) variety. We did lots of good things and lots of bad things along the way, and this is a great time to write about a few of them.

I’m going to recount a few pieces of my experience as honestly as possible, trying not to pretend that we were more clever than we actually were. Circle of Moms has usually been an environment where people are comfortable owning up to mistakes and weaknesses; hopefully I can maintain that spirit and provide some interesting lessons in the process.

What is a Pivot?

A year ago, I wrote about why I was building technology for moms. I outlined the thought process we went through in 2008 when we transitioned from “Google+ Circles for Facebook” to “LinkedIn for moms”.

Our story is occasionally highlighted in entrepreneurship classes as an example of a successful pivot. The term “pivot” has become something of a cliche, but unlike many business cliches it has real meaning. The outline for the pivot story is often abbreviated to:

  • Team has an idea they think is brilliant.
  • They build it out, and find out it wasn’t brilliant.
  • Team realizes that some byproduct of what they built was in fact brilliant. Byproduct = pivot opportunity!
  • They pivot the company in this new direction and immediately achieve success, fame, and fortune.

Does that story apply to what actually happened at Circle of Moms? Yes and no.

  • Original idea not being brilliant? Yep.
  • Byproduct = pivot opportunity? Yep.
  • Immediate success, fame, and fortune? Not so much: the pivot is only the beginning of the story.

The Pivoters

In 2008, when we decided to shift our company’s focus to moms, my co-founder Ephraim and I were guided by both personal goals and a business opportunity. On the personal side, we wanted to create a substantial website that would truly improve the world. I admire Zynga’s data-driven approach and relentless execution, but I wouldn’t derive a lot of satisfaction from building up a casual gaming property.

On the business side, four things were pushing us forward:

1) We were confident we could quickly drive distribution among moms. We’d been very successful with viral growth as Circle of Friends, and Circle of Friends’ viral mechanism had been far more effective among moms than anywhere else.

2) Moms were frustrated with the online products available to them. No good ways to meet other moms, poor information available online, difficulty storing and sharing their kids’ special moments, and decade-old tools to communicate with others like them.

3) Advertisers were willing to pay to reach moms. That meant that with moderate scale, we could get to profitability.

4) Moms told us they’d pay for tools and features that make their lives better and simpler. The now quaint-sounding quip I made in 2008 was that our customers weren’t 15-year-olds who had nothing better to buy than fancy ringtones.

In short, four positive indicators: a distribution mechanism (something all too many startups overlook), a high-level consumer need, and two possibilities for monetization.

Great, right? Well, yes — but there was one key constraint. Let me explain by making a comparison.

The Visionary

When I joined Reid Hoffman’s team at LinkedIn in early 2004, LinkedIn had 250,000 users and similar dynamics on items 1-4: a promising if not fully built out viral mechanism, a strong set of needs from a valuable audience, and a couple of potential revenue drivers. But in Reid, LinkedIn also had one unfair advantage.

Before LinkedIn even existed, Reid forcefully described a changing world in which everyone needed a place to hang their professional shingle. His vision of the future professional world was fluid and efficient on one hand, and relationship-driven on the other. This stood in sharp contrast to both the slow-moving, stay-at-a-company-all-your-life world of the 1960s, 1970s, and 1980s, and the transactional career-oriented sites that grew up in the 1990s. And this vision was truly a manifestation of his life: Reid cares deeply both about making the professional world more efficient, and about fostering relationships that can help all parties involved.

Very few founders have the ability to forecast where the world is going and then mix in their startup’s product. Reid’s correct vision of how LinkedIn could succeed — albeit with tactical shifts along the way — allowed the company to make big smart bets and helped its prospects immensely. The execution at LinkedIn was fine but not exceptional; many tactical choices were sub-optimal; the team was solid but not as the level of PayPal’s early team. But the combination of clarity of purpose, a good distribution model, and an ultimately correct view of the world allowed LinkedIn to overcome a number of challenges and become an influential and highly valued company.

The Two Climbers

Imagine two climbers. The first is a mountaineer. He knows he wants to climb Everest — it’s the most famous mountain in the world. He identifies the best people to help him, maps out the route, understands the challenge. When he actually goes to climb it, he encounters a blizzard and one of his colleagues gets sick — so he has to adjust his strategy. But he knows where he’s going and has a good understanding of how to get there.

Then picture a second climber. His goal is to get up as high as possible. He finds himself in San Francisco, on a foggy day. He knows from Wikipedia that Mt. Davidson is the city’s highest point. He can’t see more than a few feet ahead, but he can tell the difference between up and down, and sets off to cleverly find a way to the top.

Now map these types to two entrepreneurs starting companies. At Circle of Moms, I was the guy wading through the fog (and no, I didn’t have Google Maps on my phone). That wasn’t necessarily by choice: I’d spent several years working for a visionary founder, and would have emulated him had that been realistic.

Though you might not know it from reading popular accounts of startup stories, my non-visionary status wasn’t unusual: a pivoting company almost always starts out in the “fighting through the fog” mode. Circle of Moms was an extreme pivot case, a business largely chanced into by two men without kids. As such, we were unlikely to ever be blessed with an intuitive vision like Reid’s to guide our team. What were the biggest challenges our users were facing, and how was our company uniquely positioned to combine our resources and the technologies du jour to solve them? Those were questions Reid was able to answer instinctively and intuitively about LinkedIn; I found I really had to work to make progress on them for Circle of Moms.

Not having that intuition makes it harder to get one’s hands around a sense of purpose; I don’t think I ever perfected my elevator pitch. It was a challenge to find something like “the place to hang your professional shingle” (LinkedIn), “holding the Internet in your hands” (iPad), or even the smart journal that helps you share life with the ones you love (Path).

Embracing the Pivot Style

We ultimately settled on “motherhood, shared and simplified” as a tagline for Circle of Moms. It’s pithy, but not descriptive: better as part of the logo on a website than as a hook to convince potential recruits to join a team.

Lest you worry that I spent those 4.5 years crying myself to sleep, I’ll assert that tagline clarity rarely makes or breaks a company. But that communication challenge is representative of an area where we weren’t as strong. Like LinkedIn, we did what we could to capitalize on our strengths. In our case, that meant doubling down on execution and making the best of the resources we had.

Data-driven pivoting was certainly one strength of ours, and we were constantly making small shifts. We may not have found the most massive mountain, but we maneuvered the hills around us adeptly. We got features out quickly and then focused on optimizing them. We did well on SEO, figured out what worked on social media, crafted good viral flows, and had success creating and customizing content for email. Early on, we had an ad sales partner who helped us to become an attractive destination for brand advertisers. With revenue from those ad campaigns, we managed to grow the team steadily, while never wasting large amounts of money in unnecessary ways.

We had more trouble in areas like reinventing our home page, coming up with crazy new products, or pulling disparate features into a single experience. And press was probably never our strength.

Keys to Success

All else being equal, I’d rather be a visionary than a pivoter. For Circle of Moms, success happened because we found a good tack and paid attention to our constraints. I never had the vision for Circle of Moms that Reid had for LinkedIn. Yet our team managed to survive and prosper, because of a few key things we did well:

1) Turning the size of our user base into an advantage. Early on, many moms signed up for Circle of Moms, but didn’t do much on our site. In 2011, we built out technology to scientifically test out and optimize many different types of email content. That technology, coupled with a strong team writing new content, allowed us to reinvigorate our base of six million moms. Without that large user base, we never would have been able to find and optimize the best posts.

2) Hedging against Facebook-related risks. When Circle of Moms launched, we were almost 100% dependent on Facebook for traffic; in 2009 many VCs considered that an unacceptable risk and weren’t interested in funding us. That changed so much that 2-3 years down the line, during acquisition conversations with Sugar, I don’t remember the subject of Facebook traffic ever being brought up.

3) Moving away from several ineffective products. Between 2008 and 2012, we had four distinct primary product focus areas: communities of moms with similar interests, Child Page to chronicle each child’s development, local moms’ groups, and blog-style content. I wish we’d found the perfect feature; short of that, the discipline to pull away from things that weren’t working was the next best thing. And our mini-pivots ultimately allowed us to find a path to grow usage 2.5x in our last 14 months as a standalone company.

I’ll be writing future posts on each of those three topics.

Looking back, I’m glad to have both learned and accomplished a lot. Over those 4.5 years, we built a strong team and a community of millions of moms. We succeeded more than we failed. And I left the company in strong hands — Sugar’s and Ephraim’s — when I departed last month. Sugar’s team brings a number of new skills to the table, and I look forward to seeing how the combined companies will address some of the challenges we struggled with on our own.

Thanks to Elaine, Mark, Matt, Ephraim, and Tom for feedback on early drafts of this article

Why An Asocial Geeky Dude is Building Technology for Moms

In early 2007, I left one of the hottest companies in Silicon Valley. LinkedIn had millions of users, top investors and was already profitable. Our 80 person team had grown from twelve when I’d joined in 2004, and was poised to grow by another factor of ten over the next 3-4 years. I’d been leading the LinkedIn analytics team, and for a quantitative Internet guy, the data opportunities don’t get much better than LinkedIn’s.

I left LinkedIn because I had an itch to start my own company, though I wasn’t sure what that company should be. Let’s do a little entrepreneurial counseling exercise. Here was my background:

  • Mathematical and Computational Science degree from Stanford
  • Built PayPal’s early statistical modeling technology for fraud detection
  • Started to rank sports teams and help needy gamblers beat the Vegas spread

And my demographic/psychographic characteristics at the time:

  • 29 year-old married male without kids
  • only marginally less touchy feely than Dick Cheney

Which of the following would you have suggested I build?

  • a) a socially optimized search engine
  • b) a cloud-based collaborative filtering system
  • c) an algorithmically personalized form of social networking
  • d) a high frequency, automated stock trading system
  • e) a company with the tagline “motherhood, shared and simplified”

Yeah, I wouldn’t have guessed (e) either.

How we found moms (and moms found us)

In September 2007, I launched a Facebook application with Ephraim Luft, who was my year at Stanford. Our app, Circle of Friends allowed me to create one circle for my math geek friends, another for my developer friends, and a third for my sports analytics friends (ah, diversity).

The application took off pretty quickly, attracting millions of users within a few months, and I got a crash course on scaling a web app (tip: don’t use MyISAM for tables that might become huge, unless you prefer learning MySQL DBA tricks to sleeping). Thanks in large part to our rapid growth, we secured funding in January 2008 from several top “micro cap” investors — Mike Maples, Jeff Clavier, and Naval Ravikant.

Around the time of our funding, we accidentally did something very clever. We built a small feature to allow users to upload their own (tacky clip art) circle icons. This was well-received, and we soon started to prioritize the new circles/icons that were most popular. I added some Bayesian logic to tie circles to the appropriate demographic, and we soon had a product that would upsell “Drinking Buddies” circles to 20-something males, and “special friends with heart in my life…” circles to 16-year old girls.

Still, as satisfying as it was to see hundreds of thousands of people creating a “WARNING NAUGHTY WHEN DRUNK” circle (see image on right) with 15 of their naughty-when-drunk-iest friends, we had no illusions that we’d discovered the future of human social interaction. We had some traction and some interesting data, but Circle of Friends’ usage was about as deep as a backyard kiddie pool, and revenue prospects were dim. We weren’t quite sure how to proceed, so I did what I do best: dug into the data.

I noticed that several hundred thousand people had created a “Circle of Moms”. It turned out that those circles were way more active than any others — moms shared more photos, had longer and deeper conversations, and accepted more of one another’s invitations.

That was interesting enough to us that we started to think about building out a product just for moms. We asked some questions around consumer demand, revenue opportunity, and our ability to provide real value to mothers. We got deep answers to these questions; I’ll spare the details and summarize in one word: “yes.”

We launched Circle of Moms in October 2008, and haven’t looked back since.

Moms need community, empathy, support… and some kickass algorithms

One of the best things about building Internet technology is the opportunity to do work that touches millions of lives. But a million times ten minutes of mindless game play is nothing but ten million wasted minutes. On the other hand, effectively touching the lives of millions of mothers can help to raise a great next generation of humans.

When a mom wakes up at 3 AM for the sixth straight night, needing to comfort her toddler who had been sleeping through until morning, she needs both support and information. This is hardly a new problem, but the Internet has done relatively little to improve moms’ collective support system or knowledge. Fast forward a few years, and her seven-year-old is having trouble concentrating in school and Mom is concerned he’s falling behind. By combining data and community, we can help a mom — and indirectly, her child — through these tough situations.

Being focused on moms keeps us honest as technologists. Shocking though it may be, many techies — myself included — might on occasion build something that’s cool or interesting but not especially useful. But when you have an audience that actually needs your product, it forces you to keep your eye on the ball.

To fully address these needs, we’re able to combine two great styles of Internet product development. The Google-style algorithmic approach to product development is often great for spam filters, optimized search results, and ad targeting. Facebook-style social incentives are great at encouraging users to do “work” like tagging photos, translating text into different languages, and indicating their likes and interests.

I love this balance. In our case, it means that Circle of Moms is fundamentally about community and connecting people, but we’re not just a social site. To take the next step beyond social, we aim to provide our users with good information and guide them in the right direction based on our understanding of them. Social cues encourage our users to share more information about their kids; we then use that information to improve their content experience in emails and on the site. Likewise, we derived a list of important milestones children accomplish via moms’ contributions; we then algorithmically customize the list each user sees based on what we know about them.

When we decided to build out Circle of Moms, we made a conscious decision to constrain our problem space. That makes this sort of pragmatic approach to technology a lot easier. We can focus on building a playgroup finder for your area, a baby name chooser tailored to your preferences, and a guide to the development of your child, all as separate products.

Like LinkedIn, we’re a vertical social network with an awesome data set. We discovered that at 18 weeks, moms on the East Coast are 40% more likely to give their children solid food than moms on the West Coast. We learned that San Francisco has lots of babies but very few school children. And our data tell us that conservative moms name their kids Reagan and Sarah, while liberal moms prefer Jalen and Jada.

The examples above are interesting stories, but they’re just a start. Connecting moms to knowledgable peers with shared experiences and values is a hard problem, and to get it right we need to be successful across multiple avenues. Parsing conversations for keywords and underlying meaning (which we do) is one step in that direction; getting users to self-identify in intuitive ways is another. One thing that defines us as a company is that we keep iterating in data-driven ways to create a great product and help to solve those 3 AM problems.

And fortunately, I work with an awesome group of engineers who are rapidly pushing our technology forward to build useful products for moms. Brian‘s a low-key dad and a brilliant engineer who does everything from architecting and building complex search systems to writing email copy. Chris is a gold-buying, Tetris-dominating, cloud engineer extraordinaire, who uses the latest ec2 technologies to automate everything except his daily five cups of coffee. Regina, our in-house yoga master, has an awesome mix of front-end and back-end web development skills and puts together different technologies in clever and scalable ways. Hoi Ying is a fun and talented young developer, who’s become a force for us as we beef up our back-end technology (and our Hello Kitty collection!). Emma is a sponge of new information, with a great mix of engineering, product, analytics, and marketing talents: she crunches numbers, builds features, and improves the content our users see.

Collectively, they — and the rest of our eighteen person team — have made us profitable, reached millions of moms, and created a product and community that’s helping both moms and the high school class of 2027. That’s not what I expected when I left LinkedIn four years ago, but it’s pretty darned rewarding.