SharkNinja Leadership & Management

Hack Week reflects SharkNinja’s belief that big problems are best solved when people from across the business are brought together, encouraged to challenge usual ways of working, and given space to think beyond their day-to-day roles.

“When leadership came in and said get everyone in a room and problem-solve, but think boundaryless, it was truly freeing up people to think differently.”
 

What’s the leadership like on the AI and machine learning team at SharkNinja?

I’m specific about the outcome and flexible about the path. Direction is the part I hold tightly. Every AI effort has to name the business outcome it changes and who owns that outcome once the work ships. If we can’t answer that, we don’t start.

The path is where I step back. I give people real autonomy to lead in the places where they’re most capable and most comfortable because that’s where they move fastest, and I stay close enough to be useful when it counts. Then I keep pushing on the edge of that. The work I’m proudest of has come from somebody taking a problem slightly bigger than the one they raised their hand for.

The last piece is how close we sit to the business; we are not a lab. The people who will use what we build are in the room while we build it, which shortens the distance between an idea and an honest reaction to it. That proximity is also the guardrail. It’s harder to ship something careless when the person who has to live with it is reviewing it next to you.

How does leadership help your team decide where to focus AI or ML efforts?

We start with the business decision and work backward from there. AI is always in the mix when we’re solving a business problem, and it should be. What we’re careful about is the order. We name the decision that actually has to be made, and then we ask honestly whether AI or machine learning is the right way to make it, rather than starting with the technology and going looking for a problem to attach it to. AI isn’t a shiny object we’re chasing.

A recent example is the dashboard we built for star ratings and social listening, where we deliberately left AI out for now. The processing logic and the engineering behind it had to be verified and reviewed by people, because the entire value of that dashboard is that the numbers can be trusted. We’ll layer AI in later as a feature once that foundation is solid.

That thinking shapes how we measure, too. We hold the work to the business outcome it was meant to change, rather than to model specs or confidence scores. Those tell us the model is performing well, not that the decision got better.

What should candidates know about how your team balances strategic direction with experimentation?

We hold the direction steady and leave the path to it genuinely open. Most of what we do begins as a pilot, and we’d rather run two or three different approaches to the same problem than commit early to whichever one sounded best in a meeting, because the version that works is rarely the one we predicted. We keep those pilots small and short on purpose, agreeing up front on what we’re testing, how long we’ll give it and what would tell us to stop, and we settle the data questions before anyone starts building.

What matters most is what happens when a pilot doesn’t work because we’re comfortable saying we were wrong and we try to say it early, while changing course is still cheap. Pivoting isn’t a failure here, but the reason we pilot in the first place, and what we try hard not to do, is let something limp along because it was already announced.

For anyone joining, that means you’ll have real room to try things, and an experiment that doesn’t land won’t be held against you as long as it was a real question and we learned the answer. What we do ask is that you can connect what you’re exploring back to a business outcome that somebody cares about.