#WorkItWednesday featuring Tyler Watson, Staff Data Scientist

‍Meet Tyler Watson (he/him), Staff Data Scientist with Grindr!

Tyler’s background is in international development and retail. Tyler made the career shift into data science five years ago after a bit of self-study and an immersive data science program. While this may be an uncommon career path for political science undergrads, in Tyler’s own words, “I like to tell people I traded one of the sciences for another - nobody has ever laughed at that, but it’s too early to say those samples represent the overall population.”

Learn more about what brought Tyler to Grindr below and check out our careers page to stay up-to-date on open roles!

What drew you to the dating space?

I hadn’t worked in the dating space before starting with Grindr in 2021! My previous job as a data scientist was focused on using machine learning (ML) to reduce costs at a large retailer. This was full of interesting challenges, but I felt disconnected from our customers and had a feeling the people working on personalization, recommender systems, and other models that customers interacted with directly were having more fun.

It’s in my nature to spend time wondering why people behave the way they do in a given situation. I find it interesting to think about the differences between what people say they want and what they demonstrate they actually want, and how people signal things they want to communicate but might not want to say outright…it was inevitable that I would eventually start working on user-facing data science projects.

Social apps, and dating apps in particular, are huge messy ecosystems where noticing patterns in behavior is extremely rewarding since it ultimately helps you understand people better.  

What was your career path to the software industry?

I started my career as an English teacher in Seoul, South Korea because I was scared to start a desk job right out of college. This was an incredible two-year experience that had a major impact on my personality and how I view the world. One of the most important things I learned was that I wasn’t cut out to teach young kids because I couldn’t help laughing when they misbehaved.

I initially wanted to join the Foreign Service when I moved back, but the US State Department was less interested in that than I was. Fortunately, I was able to find a role in international development consulting (a fairly niche field that only really exists around government agencies that fund foreign aid projects). I spent several years working in project management and business development, which involved international travel to places not often visited by tourists. I was also in meetings with experienced foreign officials and non-profit leaders that had life experiences completely different from mine. I like to think this taught me to look for common ground with people I meet.

I started to get frustrated with the way we made decisions though - our approach was often to just ask the person who had spent the most time in the relevant country what they thought we should do. This was what really pushed me to start looking into data analytics, and then data science. Eventually, I learned enough about data science to know I wanted to dive in and make a full career change - I’ve been working in software ever since.

Tell us why you joined Grindr. What do you love most about our mission and our culture?

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The most rewarding part of working in international development for me was seeing the positive impact our work had on people’s lives. Whether it was increasing access to education, training farmers to be more efficient, or helping small businesses bring their products to market, the work I did ultimately helped improve livelihoods and increased the amount of joy in the world, which was a key driver in my decision to join Grindr.

The opportunity to do challenging and exciting data science work while also facilitating human connection, helping underserved communities, and increasing the amount of joy in the world was what attracted me to Grindr. It's fulfilling to know that if I do my job well, people are more likely to find love, friendship, and sex - whichever of those they need in their life.

How have you grown professionally while on our team?

Working at Grindr has given me ample opportunities to develop my skills and grow my expertise. In my previous role, the scope of my work usually started with data engineers giving me access to the stakeholder team’s data and ended with me delivering a model to ML engineers who would integrate it into that team’s systems. At Grindr, data scientists move much faster and work in areas that may be considered data science, data engineering, product analytics, and machine learning engineering. Developing a wider range of skills has been challenging and rewarding - expanding my focus beyond the narrow part of data science where I had previously focused was one of the things I was hoping for here. It’s made me much more resourceful, and it helps me navigate stakeholder conversations better.

I work with teams outside of engineering much more at Grindr than I did in my previous role. This has given me a cross-functional understanding of how different parts of the business work together. I have also had opportunities to present my work to people around the entire company, which has made me better at adjusting my message to the audience and thinking through what they care about.  

What interesting problems is your Grindr team solving for and what are you looking forward to for the future?

My team is focused on building high-performing machine learning and experimentation platforms at Grindr. Grindr has done a good job serving our users throughout our history (better at some points than at others), but there are features people have come to expect from social and dating apps that we don’t offer yet. The full rollout and adoption of our new experimentation platform will help us get there. It will allow us to observe the impact our product changes have on user behavior and thus make changes to the app that our users are excited about and value.

Setting this foundation will also allow us to evaluate the impact that different versions of ML models have when they are in production. For example, does personalization in the grid lead to a better user experience, or do we see that people prefer the more straightforward distance-based approach we currently use? We are setting standards now for how we will build, evaluate, deploy, and monitor ML models and getting the tools in place to support those standards so that we can speed up our time from idea to production. Once these are in place, the sky's the limit for ML at Grindr - there are countless ways we will be able to help users make meaningful connections.

Do you have a favorite memory of working at Grindr so far or a moment that stands out to you that really captures what it is like to work here?

I was on Team Grindr this summer in the AIDS/Lifecycle ride from San Francisco to Los Angeles. There were about 15 of us on our team, and we spent a week biking 545 miles together to raise funds for the San Francisco AIDS Foundation and Los Angeles LGBT Center. Our leadership made it clear through their support that participation in this event was important to the company - many of our employees and users have been impacted by HIV and AIDS. In the months leading up to the event, we received repeated shout-outs during our all-hands meetings and encouragement while we trained.

While most companies make charitable donations, I felt like this went far beyond optics and was something Grindr employees at every level were genuinely happy to support. This experience captured the overall environment of positivity and support here. People at Grindr truly believe in supporting the global queer community.

If you are interested in working with talented data scientist like Tyler at a mission-driven company with a human centered product, you should consider applying for open positions with us!