[Remote] Senior Data Scientist, Audio

Note: The job is a remote job and is open to candidates in USA. Deepgram is a platform providing real-time APIs for speech-to-text, text-to-speech, and production-grade voice agents. The Senior Data Scientist, Audio will analyze and characterize conversational audio data, build active-learning and human-in-the-loop workflows, develop representative benchmarks, and create repeatable data and model-improvement 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

Benefits

• Offers Equity
• Offers Bonus

Company Overview

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