Senior Human Researcher - Data Analysis

The Senior Researcher plans and executes applied, athlete-centric research focused on performance and protection in sport. The role sits on the Athlete Guidance team within the Nike Sport Research Lab, studying athletes of all levels in the Lab and in the environments where they train, compete, and live. The researcher turns data from in-person studies and large-scale field sources into clear answers, partnering across the Lab and with digital technology, engineering, data science, service design, and innovation teams.

• Condition, process, and analyse biomechanical and human movement data from in-lab, on-field, and remote studies with athletes of different levels and backgrounds.
• Analyse data across the research lifecycle, from focused in-person studies in the Lab and field to large-scale field data sets from wearables, surveys, and digital platforms, combining sources to answer priority research and product questions.
• Build and maintain repeatable, well-documented data pipelines, quality checks, and analysis workflows in Python and Databricks.
• Apply sound statistical methods to study design, analysis, and interpretation, including mixed, longitudinal, and repeated-measures data.
• Interpret results and share credible, decision-ready insights through reports, dashboards, and visualisations for physical and digital product innovation.
• Partner with data collection researchers on protocols and data standards, and support in-person collections as needed.
• Share feedback to improve data quality, research processes, and athlete insight.
• Apply foundations of computational biomechanics to analysis and interpretation of human movement data.
• Help shape study and data designs, build processing and analysis pipelines, and support data collections as needed so the data is understood from the source.
• Build expertise in sensor data and analysis tools, translate research into actionable recommendations, and see how the Lab’s work serves athletes and shapes the future of sport.

• Master’s degree in biomechanics, bioengineering, exercise science, kinesiology, or a related field.
• 3+ years of research experience analysing human participant or athlete data from laboratory, field, or remote settings.
• Deep knowledge of biomechanics, experimental design, statistical methods, and human movement data.
• Strong foundation in human movement science and biomechanics.
• Hands‑on experience analysing complex data sets, large and small, from motion capture, wearable sensors, surveys, and other field sources.
• Experience working with data from human movement technologies, such as foot and body scanners, instrumented treadmills, pressure mapping, force plates, marker‑based or markerless motion capture, inertial measurement units, electromyography, and other wearable sensors.
• Strong proficiency in Python and SQL for data processing and analysis.
• Experience cleaning, merging, and analysing large, messy, multi‑source data sets, including time‑series sensor and survey data.
• Experience communicating results through clear visualisations and dashboards.
• Demonstrated curiosity, initiative, attention to detail, and comfort working independently in ambiguity.
• Creative and methodical problem‑solving approach, with comfort working with messy, real‑world data and managing multiple projects.
• Strong project management skills and the ability to deliver high‑quality work in a fast‑paced, collaborative environment.
• Excellent verbal and written communication, including explaining complex topics to technical and non‑technical audiences.

• Experience with Databricks or similar large‑scale data platforms is strongly preferred.
• Visual3D, Open Sim, or musculoskeletal modelling is a plus.
• Industry research and experience in running or lateral sports is strongly preferred.

Accommodations are available to complete the interview process, including screen readers, sign language interpreters, accessible and single‑location in‑person interviews, closed captioning, and other reasonable modifications as needed.