Senior Data Scientist, Audio

• At Deepgram, we expect an AI-first mindset—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance.
• 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
• 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.
• Hands-on work on real data pipelines and model-facing problems in data science, ML, or applied research
• Strong Python and data tooling; comfort building analysis, scoring, and automation yourself
• Experience with data characterization, data selection, active learning, or similar "what should we work on next" problems
• Working familiarity with speech/audio or NLP models — you can reason about model output quality, confidence, and error modes
• A track record of turning ambiguous, messy data situations into measurable model or product improvements
• You build systems others run without you in the room — a reusable harness, not a one-off notebook
• Strong communication skills, especially translating complex findings for audiences who don't share your background
• An active AI-tool user. Not aspirational — tell us what you use and what you've built with it
• It would be great if you also had:
• Direct experience with ASR/TTS, audio data, or multilingual/code-switched data.

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