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Senior Data Scientist, Hardware Quality

ouraRemote - United StatesPosted 5 Nov 2026

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About this role

Our mission at ŌURA is to empower every person to own their inner potential. Our award-winning products help our global community gain a deeper knowledge of their readiness, activity, and sleep quality by using their Oura Ring and its connected app. We've helped millions of people understand and improve their health by providing daily insights and practical steps to inspire healthy lifestyles. Empowering the world starts with living our values and empowering our team.

As a quickly growing company focused on helping people live healthier and happier lives, we ensure that our team members have what they need to do their best work — both in and out of the office. At Oura, our mission is to empower every person to own their inner potential. With our award-winning Oura Ring and app, we help millions of people turn insights about sleep, activity, and readiness into healthier, more balanced lives.We are looking for an experienced Senior Data Scientist to partner with Oura’s Hardware, Quality, Firmware, Manufacturing, Supply Chain, Support Engineering, and Finance teams.

This role will help build the data science and decision systems that make Oura hardware more reliable, from early product testing and launch readiness through field performance, warranty outcomes, and long-term product improvement.You will work on some of the most important problems in consumer hardware: understanding why products fail, predicting reliability before issues reach members, connecting manufacturing and testing data to field outcomes, and turning complex engineering data into clear decisions.

This is a full-stack applied data science role that combines data foundations, statistical modeling, machine learning, experimentation, and cross-functional technical leadership. What You Will Do: 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 that connect 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 to identify the highest-leverage reliability risks and translate them into measurable analytical problems.

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Analyze component, supplier, manufacturing-process, and ring-build variation to identify drivers of product quality and inform corrective actions, design decisions, and supplier-quality improvements. Build trusted analytical data products and pipelines that unify hardware, manufacturing, firmware, diagnostic, activation, and warranty data and make them usable for recurring analysis and decision-making. Develop production AI-enhanced dashboards, monitoring, and decision-support tools that surface emerging failure risks and provide clear, actionable recommendations to technical and executive stakeholders.

Establish robust measurement practices for hardware quality, including metric definitions, cohorting, normalization, uncertainty, data-quality checks, and appropriate statistical methods. Translate complex technical findings into concise recommendations that influence hardware roadmaps, launch decisions, quality plans, firmware priorities, warranty strategy, and member experience. Take end-to-end ownership of ambiguous, cross-functional problems and help raise the bar for data science practices through documentation, peer review, reusable tooling, and mentorship.

This is a remote US role with a slight preference for candidates based in San Francisco. What you'll bring: 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.

Experience analyzing manufacturing, supplier, component traceability, MES, ERP, engineering test, or warranty data. Experience with battery degradation, runtime modeling, charging optimization, power management, or electronics reliability. Experience building field-failure analysis, anomaly detection, predictive-maintenance, or early

Source listing: greenhouse_oura

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