Lead Data Scientist - Nike Sports Research Lab - Nike Innovation

About the role

Description from the employer’s posting. Check the employer’s page for the current version.

The Nike Sport Research Lab (NSRL) is a multidisciplinary team of researchers, innovators, scientists, data scientists, and engineers who lead with science to make athletes* measurably better. We deliver validated insights and capabilities that inform the future of Nike products and services. WHO YOU’LL WORK WITH The Lead Data Scientist partners with researchers in biomechanics, physiology, perception, and related disciplines, along with data scientists, engineers, product managers, and other innovation partners. This role reports to the Director of Data Science and provides hands-on technical leadership within multidisciplinary project teams. *If you have a body, you are an athlete. WHO WE ARE LOOKING FOR Nike Sport Research Lab is looking for an experienced Data Scientist who combines strong algorithmic skills with practical project leadership and genuine curiosity about human movement and performance. This person can independently solve difficult technical problems, guide project-level decisions, and help other contributors deliver high-quality work. They understand that sensing and machine-learning systems are only useful when their outputs can be connected to the physical and scientific realities they are intended to represent. The successful candidate is comfortable working with ambiguous research and innovation questions, translating them into rigorous analytical plans, and collaborating across scientific and technical disciplines. They remain hands-on in analysis and software development while communicating assumptions, limitations, trade-offs, and findings clearly. This is a Lead role focused on technical execution, project coherence, and mentorship, rather than organization-wide data science strategy. Master’s degree in Computer Science, Data Science, Statistics, Engineering, Biomechanics, Kinesiology, Applied Mathematics, Physics, or a related field. Will accept any suitable combination of education, experience and training. 6+ years of relevant applied experience post-degree in data science, machine learning, statistical modelling, signal processing, computer vision, or a related technical field, including ownership of complex projects. Strong Python proficiency and experience building tested, maintainable, reproducible analytical software using modern version control, code review, and development practices. Experience working with complex measurement data, such as time-series signals, IMUs, wearable sensors, image or video data, camera-based systems, or multimodal datasets, with rigorous signal processing, computer vision model evaluation and validation. Strong communication and collaboration skills. Experience with human movement, biomechanics, physiology, sport science, pose estimation, sensor fusion, and cloud data platforms is strongly preferred. WHAT YOU’LL WORK ON You will provide hands-on technical leadership for complex projects that use sensor and camera data to understand human movement and performance. You will develop rigorous analytical solutions, guide project execution, and work closely with scientific and engineering partners to deliver credible, useful, and reusable outcomes. Apply machine learning, computer vision, signal processing, statistical modeling, and related methods to problems in human movement and athletic performance. Develop analytical workflows using data from IMUs, wearable sensors, camera-based systems, computer vision pipelines, and other measurement technologies. Connect algorithm outputs to the movement or performance phenomena they represent, making assumptions, limitations, uncertainty, and failure modes explicit. Lead the technical execution of complex projects by clarifying questions, defining analytical plans, coordinating contributions, and communicating risks and tradeoffs. Design and evaluate models and measurement approaches using appropriate scientific, statistical, and computational validation methods. Build reusable datasets, software, pipelines, and documentation that support reproducible research and future project work. Investigate and evaluate emerging methods, technologies, and state-of-the-art research, translating promising advances into practical, scientifically credible capabilities for athlete measurement and performance analysis. Provide technical guidance, code and analysis review, and mentorship while communicating methods and findings clearly to technical and non-technical partners. We offer a number of accommodations to complete our interview process including screen readers, sign language interpreters, accessible and single location for in-person interviews, closed captioning, and other reasonable modifications as needed. If you discover, as you navigate our application process, that you need assistance or an accommodation due to a disability, please complete the Candidate Accommodation Request Form .