Senior Data Scientist, Data Flywheel
Senior Data Scientist who will characterize conversational audio data, build active-learning and human-in-the-loop systems, and develop repeatable model-improvement pipelines. The role requires strong Python and data-pipeline experience, familiarity with speech or NLP models, and the ability to turn ambiguous data problems into measurable gains.
About the job
Responsibilities
- Characterize conversational data across languages, conditions, domains, speakers, and quality, identifying underrepresented areas.
- Design and build active-learning loops to prioritize work based on expected model-performance impact.
- Design human-in-the-loop workflows, tooling, and model-assisted processes.
- Build representative benchmark datasets and methodologies for evaluating model quality across real-world speech diversity.
- Run experiments to determine which data strategies improve model performance.
- Improve data consistency, cleanliness, organization, accessibility, and documentation.
- Develop repeatable, automated pipelines for domain- and customer-specific model adaptation.
Requirements
- Hands-on experience with real data pipelines and model-facing problems in data science, machine learning, or applied research.
- Strong Python and data-tooling skills, including building analysis, scoring, and automation systems.
- Experience with data characterization, data selection, active learning, or similar prioritization problems.
- Familiarity with speech/audio or NLP models, including model-output quality, confidence, and error modes.
- Track record of turning ambiguous data problems into measurable model or product improvements.
- Experience building reusable systems and automation rather than one-off analyses.
- Strong communication skills, including explaining complex findings to non-technical audiences.
- Active use of AI tools and experience building with them.
Nice to Have
- Experience with ASR/TTS, audio data, or multilingual and 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 customers or specific domains.
- Comfort collaborating directly with research and engineering teams on shared infrastructure.
Compensation
- Annual salary range: $165,000–$220,000 USD.
Skills
Python, Data Pipelines, Data Science, Machine Learning, Active Learning, Data Selection, NLP, Speech Recognition, Text-To-Speech, Audio Data, Benchmarking, Model Evaluation, Pseudo-Labeling, Llm Annotation, GDPR
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