Lead Data Scientist

The Lead Data Scientist role at Nike Sport Research Lab (NSRL) is an onsite position in Beaverton, OR, focused on hands‑on technical leadership for projects that use sensor and camera data to understand human movement and athletic performance. You will partner with scientific and engineering teams to deliver validated, reusable insights and software capabilities. What You’ll Do Lead complex projects end-to-end using sensor and camera data to study human movement and performance. Design rigorous analytical solutions, guide project execution, and collaborate with scientific and engineering partners to produce credible, useful, and reusable outcomes. Apply machine learning , computer vision , signal processing , and statistical modeling to problems in human movement and athletic performance. Create analytical workflows using data sources including IMUs , wearable sensors, camera‑based systems, computer vision pipelines, and other measurement technologies. Ensure algorithm outputs are connected to the movement or performance phenomena they represent by making assumptions, limitations, uncertainty, and failure modes explicit. Coordinate complex technical execution by clarifying questions, defining analytical plans, communicating risks, and managing trade‑offs. Design and evaluate models and measurement approaches using appropriate scientific, statistical, and computational validation methods. Build reusable datasets, software, pipelines, and documentation to support reproducible research and future project work. Evaluate emerging methods and technologies and translate state‑of‑the‑art research into practical, scientifically credible capabilities for athlete measurement and performance analysis. Provide technical guidance through code and analysis review and mentorship, communicating methods and findings clearly to technical and non‑technical partners. Required Qualifications Master’s degree in Computer Science, Data Science, Statistics, Engineering, Biomechanics, Kinesiology, Applied Mathematics, Physics, or a related field. 6+ years of applied experience post‑degree in data science, machine learning, statistical modeling, 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, including rigorous signal processing and 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. Technologies You’ll Work With Python IMUs Wearable sensors Computer vision Signal processing Statistical modeling Pose estimation Sensor fusion Cloud data platforms Team and Reporting 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. Benefits Nike offers a generous total rewards package. Casual work environment. Diverse and inclusive culture. Electric atmosphere for professional development. Interview Accommodations Nike offers accommodations to complete the interview process, including: Screen readers Sign language interpreters Accessible and single location for in‑person interviews Closed captioning Other reasonable modifications as needed Hiring Process Meet a Recruiter or Take an Assessment Interview Equal Opportunity Employer NIKE, Inc. is an equal opportunity employer. Qualified applicants will receive consideration without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity, gender expression, veteran status, or disability. #J-18808-Ljbffr