Lead Data Scientist

Nike; Beaverton (OR); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Generous rewards package

Casual environment

Diverse and inclusive culture

Professional development

Job summary

Nike in Beaverton, OR is seeking a Lead Data Scientist for onsite, hands‑on technical leadership on projects using sensor and camera data to understand human movement and athletic performance. You will partner with scientists and engineers to deliver validated insights and reusable software capabilities.

You'll lead end-to-end analyses, apply ML and CV methods, and mentor teams while building reproducible workflows and scalable data products for athlete measurement and performance analysis.

Qualifications

• Master’s degree in a related field.
• 6+ years of applied data science experience with ownership of complex projects.
• Strong Python proficiency and reproducible software development practices.
• Experience with time-series data, IMUs, wearable sensors, and image/video data.
• Excellent communication and collaboration skills.
• Preferred: experience in biomechanics, physiology, or sports science.

Responsibilities

• Lead end-to-end projects using sensor and camera data to study movement.
• Design analytical solutions and guide project execution with scientific partners.
• Apply ML, computer vision, signal processing, and statistical modeling.
• Build reusable datasets, software, pipelines, and documentation.
• Validate methods with rigorous evaluation and transparent reporting.
• Provide technical guidance through code and analysis reviews.

Skills

Python

Communication

Collaboration

Machine learning

Computer vision

Signal processing

Time-series analysis

Research leadership

Education

Master’s degree in Computer Science, Data Science, Statistics, Engineering, Biomechanics, Kinesiology, Applied Mathematics, Physics, or a related field

Tools

IMUs

Wearable sensors

Camera systems

Cloud data platforms

Pose estimation

Sensor fusion

Job description

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

1. Meet a Recruiter or Take an Assessment
2. 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.