We Are Dialpad: Natalie Owen

Name: Natalie Owen

Role: Data Science Operations Manager

What do you do at Dialpad?

I’ve always been drawn to problem solving and taking on new challenges, so I wear a few different hats at Dialpad. Officially I’m the Data Science Operations Manager, which means I help the machine learning team organize their work, plan projects, coordinate with other teams within Dialpad and manage the performance and development of the operations team. But I’m also the acting team lead for Dialpad’s Kitchener-Waterloo office which Dialpad inherited after the TalkIQ acquisition. That entails ensuring everyone in the Kitchener-Waterloo office has what they need to do their jobs well and that we’re doing cool things outside the office like team bowling nights and happy hours.

How did you get into your career?

After falling out of love with the idea of being a full-time developer, I followed my passion for building smart working systems and processes into a quality assurance career. From there I realized that I had a great opportunity at the management level to improve the work life of my direct reports. I’d always been a big believer in servant leadership and letting people focus on what they’re good at to grow professionally. So I decided to transition into Operations Management, where I could help people remove barriers and improve processes full time. The best part is that I still get to use my computer science degree to better relate to and communicate with my teams on the day-to-day.

What drew you to Dialpad?

I became a Dialer after the acquisition of TalkIQ. I had started with them about 6 months prior to the announcement, and was excited about the opportunity to bring our real-time speech recognition and analytics technology to Dialpad’s much bigger customer base. So far everyone has been incredibly welcoming and enthusiastic about what we’re building together. And I look forward to helping the team use machine learning to refine and expand Dialpad Ai and solve more of our customers’ problems.

What's one thing people would be surprised to learn about machine learning?

People often wonder if machine learning will lead to an eventual Skynet situation. I love Terminator as much as the next person, but the answer is no. There’s a big difference between machine learning and true artificial intelligence. Computers aren’t capable of learning the way that humans do. My favourite example is that a child only needs to see one picture of a horse to be able to identify any other horse from any other angle; a computer shown the same image would only be able to identify another very similar image of a horse. They need much more data to make the same connections that our brains do intuitively.

What advice do you have for people who are just starting out in your field?

Always be open to learning. I don’t come from a data science background so I’ve had to learn a lot to be effective for my team. I regularly learn new things from people who report to me and I always aim to hire people smarter than I am. Also never be scared to ask questions or “look stupid.” If you leave your ego at the door and listen to the opinions and experiences of everyone you interact with (even the non-technical people), you will learn a lot and be able to help build better products for your customers.

Interested in joining Natalie's team? We have openings across departments and offices from Kitchener-Waterloo to San Francisco! Take a look at our careers page below.