Senior Data Scientist, Hardware Quality

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop and enhance reliability and quality models for both current and future Oura Ring products, which encompass warranty forecasts, runtime degradation, survival analysis, failure-mode prediction, and cohort-based field-performance evaluations.

• Create diagnostics and root-cause analysis systems that integrate ring telemetry, firmware and engineering diagnostics, manufacturing records, supplier and component information, testing outcomes, activations, and warranty results.

• Collaborate with Hardware, Quality, Firmware, Battery Engineering, Manufacturing, Supply Chain, and Support Engineering teams to pinpoint reliability risks and define analytical challenges.

• Evaluate component, supplier, manufacturing process, and ring build variations to identify product quality drivers and inform corrective actions, design choices, and supplier quality enhancements.

• Construct analytical data products and pipelines that unify hardware, manufacturing, firmware, diagnostic, activation, and warranty data.

• Develop production AI-enhanced dashboards, monitoring tools, and decision-support systems for identifying emerging failure risks and providing actionable recommendations.

• Establish measurement practices for hardware quality, including metric definitions, cohorting, normalization, uncertainty, data quality checks, and statistical methods.

• Convert technical findings into recommendations that influence hardware roadmaps, launch decisions, quality strategies, firmware priorities, warranty approaches, and member experiences.

• Take ownership of ambiguous, cross-functional challenges and enhance data science practices through documentation, peer review, reusable tools, and mentorship.

⛳️ Requirements

• A minimum of 6 years of experience in data science, machine learning, reliability engineering, quality analytics, or a related quantitative discipline, with a history of managing high-impact issues from start to finish.

• Proven experience applying statistical modeling, machine learning, experimentation, or advanced analytics in the realms of reliability, quality, manufacturing, hardware, IoT, electronics, automotive, medical devices, or another physical product sector.

• Strong background in reliability and time-to-event methodologies, including survival analysis, hazard modeling, accelerated-life testing, degradation modeling, warranty forecasting, or similar techniques.

• Proficient in SQL and Python, with a history of developing analytical data models, statistical or machine-learning workflows, and production-grade measurement systems.

• Solid data engineering intuition, with experience in unifying and transforming data from various operational and technical systems into dependable datasets for analysis and modeling.

• Experience handling noisy, incomplete, longitudinal, or high-volume telemetry and test data, along with the ability to differentiate data artifacts from significant product signals.

• Experience in designing experiments or observational analyses that support technical decisions, including an understanding of confounding factors, bias, uncertainty, and validation.

• Familiarity with contemporary cloud and data platforms such as Databricks, AWS, dbt, Snowflake, or similar tools.

• Demonstrated ability to clearly communicate complex analytical concepts to engineers, quality and operations partners, product leaders, and executives.

• Proven capability to collaborate effectively across distributed, cross-functional teams and to drive progress when requirements and data are still in flux.

• Nice to have: experience with consumer electronics, wearables, IoT, batteries, embedded systems, firmware, sensors, or connected devices.

• Nice to have: experience in 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 in 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 and 8 days of flexible wellness time off

• Paid sick leave and parental leave