Senior Data Scientist, Hardware Quality

🔥 0 minutes ago

🇺🇸 United States – Remote

💵 $147.9k - $203k / year

⏰ Full Time

🟠 Senior

📊 Data Scientist

👻 Ghost score 4%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

ŌURA

WebsiteLinkedInAll Job Openings

201 - 500 employees

🏥 Healthcare

🏭 Manufacturing

💼 Consulting

Healthcare • Manufacturing • Consulting

ŌURA is a company that produces smart rings designed to provide in-depth health metrics and insights. The Oura Ring tracks a variety of health-related data such as sleep patterns, heart rate, activity levels, and stress. It aims to help users live healthier and more productive lives through accurate biometrics tracked via a comfortable ring worn all day and night. With features that benefit women's health, heart health, and overall wellness, ŌURA rings are marketed as both technologically advanced and user-friendly devices for personal health monitoring.

📋 Description

• 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

• 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

• 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

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume