Deepgram - Senior Data Scientist, Audio

• 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 • WHAT YOU'LL OWN • Understand and characterize our data. Build the analysis that tells us what we actually have — languages, conditions, domains, speakers, quality — and what's underrepresented. • Design and build active-learning loops. Decide what's worth working on next based on where it will move performance, and make that decision systematic rather than intuitive. • Think deeply about how to best leverage humans in the loop. Human attention is the scarcest input in this system. Design the workflows, tooling, and model-assisted steps that make it count. • Make representative benchmarking possible. Build the curated datasets and methodology that let us make honest claims about model quality across the full diversity of real-world speech.Senior • Change our minds about what data strategies actually work. Run the experiments that separate what works from what everyone assumes works. • Care about data consistency, cleanliness, and organization — and about making data legible and accessible to non-technical teams, not just to the people who built the pipelines. • Bring method and automation to model adaptation. Turn one-off, domain- and customer-specific model work into repeatable, documented pipelines.