# Senior Algorithm Validation Scientist at Whoop

AI Level 1, AI centrality 0 out of 100. Boston, MA.

## Details

- Company: [Whoop](https://jobsbylevel.com/companies/whoop)
- AI level: AI Level 1 (score 0 out of 100)
- Location: Boston, MA
- Posted: October 6, 2026
- Apply: https://jobsbylevel.com/go/d6fede4f-d42f-49f4-b0ec-d528af0f2d9b

## Description

At WHOOP, we’re on a mission to unlock and inspire performance for life. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. WHOOP is seeking an Senior Algorithm Validation Scientist to join the Sensor Intelligence organization and help ensure our physiological sensing algorithms deliver accurate, equitable, and scientifically validated performance for our members. In this role, you will design rigorous statistical analyses and validation studies that evaluate algorithm performance throughout development and after deployment, translating complex real-world data into evidence that informs algorithm quality and product decisions. You’ll partner closely with scientists, engineers, and product teams to understand algorithm behavior, quantify uncertainty, identify opportunities for improvement, and help ensure members receive accurate and trustworthy insights. RESPONSIBILITIES Design and execute rigorous statistical analyses and validation studies to evaluate the accuracy, precision, reliability, robustness, and generalizability of physiological sensing algorithms against reference-standard measurements Develop equivalence and non-inferiority analyses for hardware revisions, manufacturing changes, algorithm updates, and other changes that may affect the member experience, defining appropriate performance metrics, confidence intervals, hypothesis tests, and uncertainty estimates Evaluate algorithm performance across demographic groups, physiological characteristics, environmental conditions, and behavioral contexts to identify performance differences, edge cases, and opportunities to improve algorithm quality and equity Analyze experimental and observational production data to understand real-world algorithm behavior and draw defensible conclusions, applying approaches such as within-subject designs, difference-in-differences, matching, and other methods to account for confounding factors Develop reproducible analysis workflows and automated validation processes that enable analyses to be consistently repeated across new cohorts, hardware revisions, and algorithm releases Partner with Algorithm Scientists, Machine Learning Engineers, Product Managers, Clinical Scientists, and Software Engineers to design validation protocols, establish statistical best practices, communicate findings to technical and non-technical audiences, and translate evidence into product improvements QUALIFICATIONS Advanced degree in Statistics, Biostatistics, Data Science, Applied Mathematics, Biomedical Engineering, Epidemiology, or a related quantitative discipline, or equivalent practical experience 3+ years of experience applying statistical methods to complex experimental or observational datasets, ideally within healthcare, medical devices, wearables, digital health, consumer technology, or a related field Strong foundation in statistical inference, hypothesis testing, regression, experimental design, uncertainty quantification, and validation study design; experience with equivalence, non-inferiority, causal inference, method-comparison, or related approaches is a plus Proficiency analyzing large datasets using Python, R, or similar statistical programming tools, along with SQL and experience working with large-scale data environments Experience with physiological, wearable, biomedical sensor, time-series, or longitudinal data; experience evaluating machine learning or signal processing algorithms is a plus Familiarity with advanced statistical methodologies such as mixed-effects models, Bayesian statistics, survival analysis, fairness and parity analysis, or related approaches Strong communication and collaboration skills, with the ability to translate complex statistical findings into clear conclusions and recommendations for both technical and non-technical stakeholders Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the

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