Note: The job is a remote job and is open to candidates in USA. Deepgram is a platform providing real-time APIs and foundation models for speech-to-text, text-to-speech, and voice agents. The Senior Data Scientist will characterize conversational audio data, design active-learning and human-in-the-loop systems, build representative benchmarks, and develop automated data and model-adaptation pipelines in collaboration with research, engineering, and data teams.
Responsibilities
- 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
- 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
Skills
- 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
- 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
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