Senior Data Scientist, Hardware Quality

ŌURA

. Build and evolve 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.

Posted 11/5/2026full-timeRemote • United StatesSenior💰 $147,900 - $203,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in data science and reliability engineering, with a strong focus on statistical modeling, machine learning, and quality analytics. Proficient in building analytical data models and communicating complex concepts across cross-functional teams.

Highest-signal resume keywords

Data ScienceMachine LearningReliability EngineeringStatistical ModelingSQL

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Statistical ModelingSurvival AnalysisWarranty ForecastingData EngineeringExperiment DesignTelemetry AnalysisPythonAnalytical Data ModelsTime-To-Event MethodsDegradation Modeling

Soft Skills

Cross-Functional CollaborationCommunicationProblem SolvingMentorshipJudgment

Tools & Technologies

AWSDatabricksSnowflakeDbtFirmware

Industry Keywords

Consumer ElectronicsIoTManufacturingQuality AnalyticsEmbedded Systems

Tech Stack

Tools & technologies

AWSCloudERPIoTPythonSnowflakeSQL

About the role

Key responsibilities & impact

Build and evolve 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 unifying hardware, manufacturing, firmware, diagnostic, activation, and warranty data.
Develop production AI-enhanced dashboards, monitoring, and decision-support tools for emerging failure risks and actionable recommendations.
Establish measurement practices for hardware quality, including metric definitions, cohorting, normalization, uncertainty, data-quality checks, and statistical methods.
Translate technical findings into recommendations influencing 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.

Requirements

What you’ll need

6+ years of experience in data science, machine learning, reliability engineering, quality analytics, or a closely related quantitative field, with a track record of owning high-impact problems end to end.
Demonstrated experience applying statistical modeling, machine learning, experimentation, or advanced analytics to reliability, quality, manufacturing, hardware, IoT, electronics, automotive, medical devices, or another physical-product domain.
Strong experience with reliability and time-to-event methods, such as survival analysis, hazard modeling, accelerated-life testing, degradation modeling, warranty forecasting, or related techniques.
Strong proficiency in SQL and Python, with experience building analytical data models, statistical or machine-learning workflows, and production-quality measurement systems.
Strong data-engineering intuition, including experience unifying and transforming data from multiple operational and technical systems into reliable datasets for analysis and modeling.
Experience working with noisy, incomplete, longitudinal, or high-volume telemetry and test data, and the judgment to distinguish data artifacts from meaningful product signals.
Experience designing experiments or observational analyses that support technical decisions, including an understanding of confounding, bias, uncertainty, and validation.
Experience with modern cloud and data platforms such as Databricks, AWS, dbt, Snowflake, or similar tools.
Proven ability to communicate complex analytical concepts clearly to engineers, quality and operations partners, product leaders, and executives.
Proven ability to work effectively across distributed, cross-functional teams and to drive progress when requirements and data are still evolving.
Nice to have: experience with consumer electronics, wearables, IoT, batteries, embedded systems, firmware, sensors, or connected devices.
Nice to have: experience analyzing manufacturing, supplier, component traceability, MES, ERP, engineering test, or warranty data.
Nice to have: experience with battery degradation, runtime modeling, charging optimization, power management, or electronics reliability.
Nice to have: experience building field-failure analysis, anomaly detection, predictive-maintenance, or early-warning systems.
Nice to have: familiarity with hardware development and launch processes, including EVT, DVT, PVT, and field-test programs.

Benefits

Comp & perks

Competitive salary and equity packages
Health, dental, vision insurance, and mental health resources
An Ōura Ring of your own plus employee discounts for friends & family
20 days of paid time off plus 13 paid holidays plus 8 days of flexible wellness time off
Paid sick leave and parental leave