Apply signal processing, statistical analysis, and machine learning techniques to physiological time-series data to identify performance opportunities, investigate algorithm failures, and improve sensor-derived insights
Build and maintain scalable workflows, dashboards, and diagnostic tools for algorithm validation, regression testing, performance monitoring, data quality assessment, and sensor behavior across diverse populations and use cases
Contribute to software development, debugging, testing, and validation processes, ensuring analytical tools, algorithms, and results meet production-quality standards
Communicate findings through clear technical documentation, reports, and visualizations that enable cross-functional teams to make informed product and engineering decisions
QUALIFICATIONS
Bachelor’s or Master’s degree in electrical engineering, biomedical engineering, applied mathematics, statistics, computer science, or a related quantitative field, or equivalent practical experience
5+ years of industry or research experience working with signal processing, machine learning, statistical analysis, algorithm validation, or related quantitative methods
Experience analyzing time-series data and applying core signal processing techniques such as filtering, spectral analysis, peak detection, and windowing
Demonstrated experience independently designing and driving validations studies, including one or more of test methodology development, reference-data comparison, performance evaluation, acceptance-criteria definition, or failure-mode investigation.
Proven track record of investigating intricate or ill-defined technical challenges, pinpointing root causes, and delivering solutions across algorithm, sensor, data, and system domains
Proficiency in Python and/or C, with experience developing reliable tools or software for data processing, analysis, or algorithm validation
Strong foundation in statistical inference and experimental analysis, including the ability to select appropriate methods, interpret results, and communicate analytical trade-offs
Experience working with large datasets and modern engineering or data infrastructure such as Git, CI/CD systems, cloud platforms, or data warehouses
Strong written and verbal communication skills, with the ability to translate complex technical findings into concise documentation, visualizations, and actionable recommendations
Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions.
Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.
WHOOP is an Equal Opportunity Employer and participates in E-Verify to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
