- Build, not just use. Design your own workflows and agents, and use AI to remove whole categories of work instead of doing the same work faster.
- Own the output. AI can speed up the work. It doesn’t make anyone less accountable for it. You understand, verify, and stand behind everything you share, including what an agent produces for you.
- Spend the time you get back at the frontier. The capacity AI creates goes toward problems no one has solved yet.
- Keep pace. Models and tools change every month, and so will your work. You adopt what’s new quickly and drop what no longer earns its place.
We judge AI by what it helps you deliver and whether you stand behind the result, not by how often you use it. In interviews, expect to walk us through a workflow you built and where you drew the line between AI and your own judgment.
Deepgram is looking for a Data Scientist to sit at the intersection between research and data: people who think deeply about what conversational data is actually composed of, what makes data valuable, and how to best leverage that.
Conversational audio presents incredibly rich scientific, engineering, and infrastructure challenges that are orders of magnitude harder than working with text. Speech carries speakers, accents, dialects, emotion, overlapping talk, code-switching, domain vocabulary, and acoustic conditions that range from a quiet studio to a drive-through — all with rich context that moves the conversation. In this role you'll collaborate closely with our research, engineering, and data teams to answer those questions rigorously and at scale.
You'll care deeply about what makes conversational data difficult, you'll characterize it, and you'll shape the strategies that turn it into model gains. This role rewards conviction, creativity in approach, and a real appetite for overturning your own assumptions when the data says otherwise.
This is a hands-on, high-leverage role with unusual latitude to define an area from first principles. It reports to the VP of Data Operations. We're looking for people who:
- See "we've always done it this way" as a starting position to argue with, not a constraint to work around
- Can find the one experiment that settles a question in days rather than months
- Are creative about where signal hides – in metadata, in model confidence, in the failures
- Have the vision to take a scrappy proof-of-concept and scale it 100x
- Are obsessed with using AI to automate and amplify their own impact