Senior Data Scientist, Hardware Quality
New
Ō
ŌURAConsumer hardware
Remote - United StatesFull-TimeSenior
SalaryRegion 1 $172,000- $203,000; Region 2 $158,950- $187,000; Region 3 $147,900- $174,000
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Job Details
- Experience
- 6+ years of experience in data science, machine learning, reliability engineering, quality analytics, or a closely related quantitative field
- Required Skills
- AWSPythonSQLMachine LearningSnowflakeDatabricks
Requirements
- Have 6+ years of experience in data science, machine learning, reliability engineering, quality analytics, or a closely related quantitative field.
- Demonstrate ownership of high-impact problems from start to finish.
- Have applied statistical modeling, machine learning, experimentation, or advanced analytics to reliability, quality, manufacturing, hardware, IoT, electronics, automotive, medical devices, or another physical-product domain.
- Have experience with reliability and time-to-event methods such as survival analysis, hazard modeling, accelerated-life testing, degradation modeling, or warranty forecasting.
- Be proficient in SQL and Python, including analytical data models, statistical or machine-learning workflows, and production-quality measurement systems.
- Have experience unifying and transforming data from multiple operational and technical systems into reliable analytical datasets.
- Have worked with noisy, incomplete, longitudinal, or high-volume telemetry and test data.
- Have experience designing experiments or observational analyses, including consideration of confounding, bias, uncertainty, and validation.
- Have experience with modern cloud and data platforms such as Databricks, AWS, dbt, Snowflake, or similar tools.
- Be able to explain analytical concepts to engineers, quality and operations partners, product leaders, and executives.
- Be able to work across distributed, cross-functional teams and drive progress as requirements and data evolve.
Responsibilities
- Build reliability and quality models for current and next-generation Oura Ring products, including warranty forecasts, runtime degradation, survival analysis, failure-mode prediction, and cohort-based field-performance analysis.
- Develop diagnostics and root-cause analysis systems connecting ring telemetry, firmware and engineering diagnostics, manufacturing records, supplier and component data, testing results, activations, and warranty outcomes.
- Partner with hardware, quality, firmware, battery engineering, manufacturing, supply chain, and support engineering teams to identify reliability risks and define analytical problems.
- Analyze component, supplier, manufacturing-process, and ring-build variation to identify product-quality drivers and inform corrective actions, design decisions, and supplier-quality improvements.
- Build analytical data products and pipelines that unify hardware, manufacturing, firmware, diagnostic, activation, and warranty data.
- Develop production AI-enhanced dashboards, monitoring, and decision-support tools that surface emerging failure risks and recommendations.
- Establish measurement practices for hardware quality, including metric definitions, cohorting, normalization, uncertainty, data-quality checks, and statistical methods.
- Translate technical findings into recommendations for hardware roadmaps, launch decisions, quality plans, firmware priorities, warranty strategy, and member experience.
- Own ambiguous cross-functional problems and improve data science practices through documentation, peer review, reusable tooling, and mentorship.
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