Principal, Data Scientist, Nike Sport Research Lab

Nike•Beaverton, OR

11h

About The Position

The Nike Sport Research Lab (NSRL) is a multidisciplinary team of researchers, innovators, and scientists who lead with science to make athletes measurably better. We deliver validated insights and innovative capabilities to drive the future of Nike products and services. if you have body, you are an athlete WHO YOU’LL WORK WITH The Principal Data Scientist partners with research scientists in physiology, biomechanics, perception, and behavioral science, as well as other data scientists, engineers, product managers, designers, and solution architects. This role reports to the Director of Data Science and collaborates across dynamic, multi-functional teams to accelerate innovative product development. The team is recognized for its culture of curiosity, collaboration, and impact within Nike and the broader industry. WHO WE ARE LOOKING FOR Nike Sport Research Lab seeks a Principal Data Scientist who combines deep technical expertise with a passion for advancing human performance. The ideal candidate is a hands-on creative problem-solver, a collaborative leader, and a strategic thinker who thrives in multidisciplinary environments. This individual brings a proven track record of leveraging science and technology to drive innovation in sport science, product development, or related fields.

Requirements

PhD or Master’s in Statistics, Computer Science, Electrical/Biomedical engineering, Economics, or related field, Will accept any suitable combination of education, experience and training.
7-9+ years demonstrated experience in Data Science, including expertise in data synthesis, machine learning, statistical modeling, causal inference, or signal processing.
Demonstrated expertise with Python, and data analysis stacks (such as NumPy, SciPy, pandas, Spark, etc.)
Experience with AWS infrastructure, Spark, Databricks, and SQL.
Demonstrated practice of software engineering best practices within technical orgs (git for version control, structured code reviews, automated tests, CI/CD pipelines, and maintainable, reproducible build environments)
Proven ability to define, initiate, and supervise analytics and modeling efforts.
Strong track record of translating business needs into research requirements and strategy.
Ability to interpret and implement methods described in research papers and articles in signal processing, machine learning, deep learning, mathematical modeling, and related fields.
Excellent interpersonal and communication skills, with experience communicating technical information in both written and verbal formats.

Nice To Haves

Experience with experimental design and statistical inference, including adaptive experimentation, A/B testing, bandit optimization, and causal inference.
Proficiency in advanced machine learning methods, including classification, regression, deep learning, and computer vision algorithms.
Familiarity with time-series analysis, predictive modeling, and forecasting using large-scale, real-world datasets (e.g., sensor, image, or behavioral data).
Experience developing and deploying models that learn from expert input and emulate expert decision-making.
Understanding of, or working knowledge in, cardiovascular physiology, biomechanics, perception science, or related fields.
Experience with bio-signals, wearables, and inertial measurement technologies.
Peer-reviewed machine learning publications or other demonstrable contributions (e.g., GitHub, apps, products).
Experience leading or mentoring teams of data scientists and engineers in research or product settings.
Experience designing and delivering clear, impactful data visualizations for technical and non-technical audiences.
Experience building and maintaining robust data pipelines, ensuring data quality, integrity, and accessibility for analysis and modeling.

Responsibilities

Apply expertise in signal processing, machine learning, sensor fusion, computer vision, and other areas of machine intelligence to solve complex problems using large, diverse datasets.
Develop and deploy predictive models in production environments to power personalized digital experiences for athletes and consumers.
Drive the development of large-scale experimentation capabilities and help identify causal factors influencing athletic performance and behavior.
Lead the creation, acquisition, and maintenance of high-quality datasets that enable ongoing exploration and unlock value for NSRL and partner organizations.
Investigate and evaluate new technologies, software, machine learning techniques, and capabilities to keep NSRL at the forefront of innovation.
Communicate technical information, methods, and findings clearly and concisely to technical and non-technical teams, partners, and senior management.
Create specifications and documentation for external professional services firms to achieve team objectives efficiently.
Stay connected with machine intelligence innovations across the industry and share insights with team members and partners throughout Nike.

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