Senior Data Scientist, Data Flywheel

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.

Experience with ensemble labeling, pseudo-labeling, or LLM-assisted annotation.

Familiarity with data provenance, PII/GDPR-aware pipelines, or model-improvement compliance.

Experience building custom or fine-tuned models for specific customers or domains.

Comfort working directly with research and engineering teams on shared infrastructure.

Backed by prominent investors including Y Combinator, Madrona, Tiger Global, Wing VC and NVIDIA, Deepgram has raised over $215M in total funding. If you're looking to work on cutting-edge technology and make a significant impact in the AI industry, we'd love to hear from you!

Deepgram is an equal opportunity employer. We want all voices and perspectives represented in our workforce. We are a curious bunch focused on collaboration and doing the right thing. We put our customers first, grow together and move quickly. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, gender identity or expression, age, marital status, veteran status, disability status, pregnancy, parental status, genetic information, political affiliation, or any other status protected by the laws or regulations in the locations where we operate.

We are happy to provide accommodations for applicants who need them.

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