[All remote jobs](https://jobicy.com/jobs.md)[![Deepgram logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2020/12/WRILS-201212160008-406229.jpg)](https://jobicy.com/company/deepgram.md)[Deepgram](https://jobicy.com/company/deepgram.md) # Senior Data Scientist, Audio Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/deepgram.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 178k–220k / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceDirectorPublished14 Oct 2026Apply before13 Nov 2026Listing views37Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Deepgram seeks a senior data scientist to characterize conversational audio data and develop scalable, model-facing data strategies. The role centers on active learning, representative benchmarking, human-in-the-loop workflows, and repeatable model-adaptation pipelines for speech AI. The successful candidate will work closely with research, engineering, and data teams to convert ambiguous data problems into measurable improvements in model performance. This is a highly autonomous, hands-on role in a fast-changing AI environment, requiring strong Python, applied ML, and communication skills. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis senior role combines applied data science, ML experimentation, speech or NLP model reasoning, data infrastructure, and strategic prioritization. It requires independently designing high-leverage experiments and production-ready systems in a rapidly evolving AI setting. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$199,000US market range (per year)$180k–$235k0$258.50k AI insightThe employer discloses a credible annual base-compensation range of $178,000–$220,000 USD, with a midpoint of $199,000. Estimated US-market annual compensation for a senior data scientist focused on applied ML, speech/audio data, and model improvement is approximately $180,000–$235,000 USD. ## Core skills Skills and capabilities most closely associated with this opportunity. [Python](https://jobicy.com/jobs?search_keywords=Python.md)[Data science](https://jobicy.com/jobs?search_keywords=Data%20science.md)[Machine learning](https://jobicy.com/jobs?search_keywords=Machine%20learning.md)[Active learning](https://jobicy.com/jobs?search_keywords=Active%20learning.md)[Speech recognition](https://jobicy.com/jobs?search_keywords=Speech%20recognition.md)[Audio data analysis](https://jobicy.com/jobs?search_keywords=Audio%20data%20analysis.md)[Natural language processing](https://jobicy.com/jobs?search_keywords=Natural%20language%20processing.md)[Data characterization](https://jobicy.com/jobs?search_keywords=Data%20characterization.md)[Benchmarking](https://jobicy.com/jobs?search_keywords=Benchmarking.md)[Data pipelines](https://jobicy.com/jobs?search_keywords=Data%20pipelines.md)[Model evaluation](https://jobicy.com/jobs?search_keywords=Model%20evaluation.md)[Human-in-the-loop systems](https://jobicy.com/jobs?search_keywords=Human-in-the-loop%20systems.md)[Experiment design](https://jobicy.com/jobs?search_keywords=Experiment%20design.md)[Data quality](https://jobicy.com/jobs?search_keywords=Data%20quality.md)[Technical communication](https://jobicy.com/jobs?search_keywords=Technical%20communication.md) Sample interview questionsHow would you determine which portions of a large conversational-audio corpus should be labeled or improved first?I would begin by profiling coverage across languages, domains, acoustic conditions, speaker attributes, and quality signals. I would combine this with model confidence, error analysis, customer impact, and dataset gaps to prioritize slices where incremental data is most likely to improve target metrics. I would then validate the prioritization through a small, controlled active-learning experiment before scaling. Describe how you would build a representative benchmark for an ASR model. I would define the intended use cases and construct a stratified evaluation set spanning languages, accents, noise levels, overlap, domains, and speech styles. I would establish consistent annotation guidelines, quality-control procedures, and slice-level metrics such as WER or task-specific measures. The benchmark would include versioning, provenance, and documented limitations so model claims remain reproducible and honest. Tell us about a time you turned a messy data problem into a measurable model or product improvement. A strong example would explain how I first identified inconsistent sources or weak labels, established measurable quality and performance baselines, and isolated the highest-impact failure modes. I would describe the experimental changes, quantify the resulting improvement, and explain how I productionized the process so it could be reused rather than remaining a one-off analysis. How would you use human reviewers efficiently in a model-improvement pipeline? I would reserve human review for examples with the greatest expected information value, such as uncertain predictions, high-impact domains, disagreements among models, and underrepresented segments. Model-assisted pre-labeling, clear reviewer interfaces, consensus checks, and targeted audits can reduce effort while protecting label quality. I would continuously measure reviewer agreement, throughput, cost, and downstream model lift. What AI tools do you use in your data-science workflow, and how do you ensure their output is reliable? I use AI tools to accelerate exploratory analysis, code scaffolding, documentation, experiment design, and error triage, while retaining ownership of technical decisions. I validate generated output through tests, reproducible analyses, source inspection, and comparison against known baselines. For sensitive data or model-improvement work, I also apply access controls, privacy requirements, and human review before operational use. Opportunity details ## About this role. ### Company Overview Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale. More than 200,000 developers and 1,300+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram’s voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software, with unmatched accuracy, low latency, and cost efficiency. Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words. There is no organization in the world that understands voice better than Deepgram. ### Company Operating Rhythm 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. Every team member who works at Deepgram is expected to actively use and experiment with advanced AI tools, and even build your own into your everyday work. We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here. Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do. Additionally, we move at the pace of AI. Change is rapid, and you can expect your day-to-day work to evolve just as quickly. This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9-to-5. 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. ### What We’re Looking For * 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. Notice: We’re aware of individuals impersonating Deepgram recruiters. All legitimate Deepgram recruiting communication comes from an @[deepgram.com](http://deepgram.com) email address. If you’ve received a message claiming to be Deepgram, please forward it to careers@deepgram.com. 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