[All remote jobs](https://jobicy.com/jobs.md)[![ŌURA logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2026/07/011c99434cd6-221.webp)](https://jobicy.com/company/oura.md)[ŌURA](https://jobicy.com/company/oura.md) # Senior Data Scientist, Hardware Quality AI Summary Oura is seeking a Senior Data Scientist to improve hardware reliability and quality for the Oura Ring throughout testing, launch, and field performance. The role combines reliability analytics, survival modeling, machine learning, experimentation, data engineering, and decision-support tooling. The successful candidate will connect telemetry, firmware diagnostics, manufacturing records, supplier data, and warranty outcomes to identify failure risks and guide corrective actions. This position requires close partnership with hardware, quality, manufacturing, supply chain, support engineering, finance, and executive stakeholders. It is a US remote role with a preference for candidates based in San Francisco.[Apply for this job](#job-application)[View company](https://jobicy.com/company/oura.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 147,900–203k / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceDirectorPublished14 Nov 2026Apply before14 Dec 2026Listing views22Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. Opportunity details ## About this role. Our mission at [ŌURA](https://jobicy.com/company/oura.md) 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](https://ouraring.com/about-us) 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. * 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-warning systems. * Familiarity with hardware development and launch processes, including EVT, DVT, PVT, and field-test programs. ### Benefits At Ōura, we care about you and your well-being. Everyone here at Ōura has a ring of their own and we are continually looking to improve employee health. What we offer: * 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 Ōura takes a market-based approach to pay, which may vary depending on your location. US locations are categorized into tiers based on a cost of labor index for that geographic area. While most offers will be closer to the starting range, successful candidates’ pay will be determined based on job-related skills, experience, qualifications, work location, internal peer equity, and market conditions. These ranges may be modified in the future. Region 1 $172,000- $203,000 Region 2 $158,950- $187,000 Region 3 $147,900- $174,000 A recruiter can determine your Region based on your US location. We are not considering candidates residing in the following states: Alaska (AK), Delaware (DE), Iowa (IA), Mississippi (MS), Nebraska (NE), South Dakota (SD), West Virginia (WV), and Wisconsin (WI). ŌURA is proud to be an equal opportunity workplace. We celebrate diversity and are committed to creating an inclusive environment for all employees. Individuals seeking employment at ŌURA are considered without regard to age, ancestry, color, gender (including pregnancy, childbirth, or related medical conditions), gender identity or expression, genetic information, marital status, medical condition, mental or physical disability, national origin, protected family care or medical leave status, race, religion (including beliefs and practices or the absence thereof), sexual orientation, military or veteran status, or any other characteristic protected by federal, state, or local laws. We will not tolerate discrimination or harassment based on any of these characteristics. We will work to ensure individuals with disabilities are provided reasonable accommodation to participate in the interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Disclaimer: Beware of fake job offers! We’ve been alerted to scammers posing as ŌURA recruiters, especially for remote roles. Please note: * Our jobs are listed only on the ŌURA Careers page and trusted job boards. * We will never ask for personal information like ID or payment for equipment upfront. * Official offers are sent through Docusign after a verbal offer, not via text or email. Stay cautious and protect your personal details. To all recruitment agencies: ŌURA does not accept agency resumes. Please do not forward resumes to our jobs alias, ŌURA employees, or any other organization’s location. ŌURA is not responsible for any fees related to unsolicited resumes. Show more [Apply now >](https://jobicy.com/jobs/162149-senior-data-scientist-hardware-quality.md) * ![Upload CV](data:image/svg+xml;base64,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) ### Upload your resume now To unlock remote work opportunities and be discovered by global employers. This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a senior, cross-functional applied data science role involving complex physical-product reliability problems, noisy longitudinal data, and production decision systems. It requires strong statistical depth as well as the ability to influence technical and executive decisions amid ambiguous requirements. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$175,450US market range (per year)$160k–$210k0$231.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses three US location-based salary bands ranging from $147,900 to $203,000. The annual cadence is inferred from the stated US full-time senior-level pay amounts, as the posting does not explicitly label the ranges as annual. A reasonable estimated US market range for a senior data scientist specializing in hardware reliability is $160,000 to $210,000 per year. ## Core skills Skills and capabilities most closely associated with this opportunity. [Reliability Engineering](https://jobicy.com/jobs?search_keywords=Reliability%20Engineering.md)[Survival Analysis](https://jobicy.com/jobs?search_keywords=Survival%20Analysis.md)[Statistical Modeling](https://jobicy.com/jobs?search_keywords=Statistical%20Modeling.md)[Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Data Engineering](https://jobicy.com/jobs?search_keywords=Data%20Engineering.md)[Manufacturing Analytics](https://jobicy.com/jobs?search_keywords=Manufacturing%20Analytics.md)[Warranty Forecasting](https://jobicy.com/jobs?search_keywords=Warranty%20Forecasting.md)[Root Cause Analysis](https://jobicy.com/jobs?search_keywords=Root%20Cause%20Analysis.md)[Experimental Design](https://jobicy.com/jobs?search_keywords=Experimental%20Design.md)[Telemetry Analytics](https://jobicy.com/jobs?search_keywords=Telemetry%20Analytics.md)[AWS](https://jobicy.com/jobs?search_keywords=AWS.md)[Databricks](https://jobicy.com/jobs?search_keywords=Databricks.md)[Stakeholder Communication](https://jobicy.com/jobs?search_keywords=Stakeholder%20Communication.md) Sample interview questionsHow would you build a model to forecast warranty claims for a new hardware generation?I would first define the warranty event, exposure window, cohorts, and censoring rules. I would combine manufacturing, component, activation, telemetry, and support data, then use survival or hazard models to estimate time-to-failure while controlling for cohort and usage differences. I would validate forecasts against held-out launch cohorts and provide uncertainty intervals to support operational planning. Describe how you would distinguish a real reliability signal from a data artifact in telemetry data. I would validate data lineage, collection changes, missingness patterns, device and firmware coverage, and timestamp integrity before interpreting an anomaly. I would compare the signal across independent sources such as diagnostic logs, support contacts, return records, and manufacturing cohorts. A signal that persists after these checks and has a plausible engineering mechanism would warrant deeper investigation. What metrics would you use to monitor hardware quality after launch? I would monitor survival and hazard rates, return and warranty rates, failure-mode incidence, runtime degradation, activation success, diagnostic error rates, and supplier or build-level variation. Metrics should be cohort-based and normalized for exposure, usage, firmware version, geography, and device age. Clear alert thresholds and uncertainty estimates are essential for operational use. How do you communicate an uncertain analytical finding to engineering leaders and executives? I would separate observed facts, model assumptions, uncertainty, and recommended actions. I would use a concise narrative supported by a small number of decision-relevant charts, explain the magnitude and confidence of the risk, and propose the next validation step. This keeps discussion focused on action rather than overstating certainty. How would you approach root-cause analysis for an increase in device failures? I would segment failures by build cohort, supplier, component revision, firmware, usage profile, geography, and time since activation. I would link affected devices to manufacturing and diagnostic records, test hypotheses with controlled statistical comparisons, and work with engineering teams to assess technical plausibility. The outcome would be a prioritized set of likely drivers, confidence levels, and corrective-action recommendations. Next step ## Apply now. Follow the employer’s application method and review Jobicy’s safety guidance before sharing personal information. 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