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Why We Lyssn: Brian Pace

“Why We Lyssn” is our monthly feature where we spotlight the exceptional individuals who make up our diverse team. Each month, we introduce you to someone from a different department, sharing their unique perspective, fun facts, and reasons they’ve chosen to be part of our mission. This month we’re delighted to introduce Brian Pace, our Director of Clinical AI, who brings expertise in psychotherapy process, outcome research, and behavioral science to our technology.

What inspired you to join Lyssn?

I worked with Lyssn prior in an academic setting. I also took a conversation analysis course in undergrad and found it interesting. I collaborated with Zac, Lyssn’s Chief Science Officer, to identify nuanced behaviors in therapeutic conversations. When the opportunity came to join the team full-time in 2019, it felt like a natural progression of work I was already passionate about.

What does your day-to-day look like?

I oversee clinical quality metrics from conception to platform release. My role includes managing data labeling teams for machine learning models, collaborating with data science and development teams during product creation, and monitoring releases to ensure our models accurately identify key clinical behaviors that guide practice.

If you could switch jobs with anyone, who would it be and why?

I would switch with our Principal Design Lead, Sarah. I’m really intrigued by how she transforms data into visualizations that make complex information easier to understand and interact with.

What is a fun fact about yourself?

I really enjoy fly fishing in my free time. 

Brian’s evolution from researcher to AI innovation leader embodies the passion at Lyssn’s core. If you’re passionate about research for software that matters in a collaborative, supportive environment, we’d love to meet you. Check out our Careers Page to join our mission of transforming mental health care through innovation.

Proven science. Powerful AI.
Profound improvement.

Let Lyssn reduce burnout and transform the way you implement and model fidelity to evidence-based practices.