Senior Human Performance Researcher - Data Analysis

Global Sports Jobs Limited; Beaverton (OR); Sur place; USD 90 000 - 135 000; Plein temps

Résumé du poste

Nike Sport Research Lab is seeking a Senior Researcher to plan and execute applied biomechanics research focused on performance and protection for athletes at all levels. You will transform data from lab, field, and wearables into actionable insights for product innovation.

The role requires mastery of biomechanics, experimental design, and robust data analysis using Python, SQL, and Databricks. You will collaborate with engineers, data scientists, and designers to shape study designs and data

Qualifications

• Master’s degree in biomechanics, bioengineering, exercise science, kinesiology, or related field.
• 3+ years of research experience analysing athlete data from lab, field, or remote settings.
• Deep knowledge of biomechanics, experimental design, statistics, and movement data.
• Strong Python and SQL proficiency for data processing.

Responsabilités

• Plan and execute applied, athlete‑centric research for performance and protection.
• Analyse biomechanical data from in-lab, field, and remote studies across athletes.
• Build repeatable data pipelines and analysis workflows in Python and Databricks.
• Interpret results and share insights through reports and dashboards.
• Collaborate with cross‑functional teams across Lab and digital tech.

Connaissances

Python

SQL

Biomechanics

Statistical methods

Data pipelines

Experiment design

Data analysis

Communication of results

Formation

Master’s degree in biomechanics

Outils

Databricks

OpenSim

Visual3D

Description du poste

About the Role

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.

Key Responsibilities

• 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.

Required Experience & Qualifications

• 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.

Preferred Skills

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

The Application Process

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.