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Advanced Innovation
00504953 Requisition #

We are looking for a full-stack Principal Data Scientist to lead the research vision, drive the technical roadmap and help build internet-scale, adaptive experimentation platforms and machine learning solutions that push the boundaries of sport science and digital products.

The candidate needs to be curious, self-motivated and have good interpersonal and communication skills. The candidate needs to demonstrate ability to formulate and execute on multi-year data science initiatives and have proven expertise in signal processing, machine learning, sensor fusion, computer vision or other areas of machine intelligence.

You will:

- Apply expertise in signal processing, machine learning, sensor fusion, computer vision and other areas of machine intelligence to solve complex problems using large, complex and diverse datasets

- Develop and deploy predictive models in production environments to power personalized digital experiences on mobile and web

- Drive the development of internet-scale experimentation capabilities and help identify causal factors that influence athletic performance and behavior

- Lead the creation or acquisition and maintenance of high-quality datasets that facilitate ongoing exploration and help unlock value for the NSRL and partner organizations

- Lead investigation of new technologies, software, machine learning techniques and capabilities

- Communicate technical information, methods and findings clearly and concisely to technical and non-technical teams, partners and senior management

- Create specifications and quotation documentation for external professional services firms as needed to achieve team objectives in a time and cost-effective manner

- Stay connected with machine intelligence innovations across the industry and share findings with team members and partners across the organization

In this role, you will partner with physiology, biomechanics, perception and behavioral research scientists to proactively find opportunities to demonstrate advanced analytics and machine learning in building impactful, data-powered products and solutions. You will work with, coach, mentor and inspire data scientists, data engineers and enterprise technology professionals.

You will also work closely with dynamic and multi-function teams of product managers, designers, solution architects, software and embedded systems engineers. You will build and nurture strong, positive relationships with various partners throughout Nike and together help accelerate the development of innovative products and services.

Required Education/Experience:

- PhD or Masters in a quantitative field (statistics, computer science, electrical/biomedical engineering, economics or similar) or equivalent practical experience

- 7+ years, after PhD, or 9+ years, after Masters, demonstrated experience in data science including expertise in data synthesis, machine learning, statistical modeling, causal inference, or signal processing

- Demonstrated expertise with Python and/or Matlab, machine learning and deep learning frameworks like Scikit-learn, TensorFlow, Keras, PyTorch

- Demonstrable ability to define, initiate and supervise analytics and modeling efforts

- Demonstrable ability to translate business needs into requirements and strategy for research teams

- Demonstrable ability to interpret, and implement methods described in research papers and articles in signal processing, machine learning, deep learning, mathematical modeling and related fields

- Good interpersonal and communication skills, with demonstrable experience communicating technical information in written and verbal formats

- Experience with AWS suite, Spark, Databricks, SQL

- Experience with adaptive experimentation and A/B testing, bandit optimization, causal inference, time-series analysis, predictive modeling, forecasting and other approaches

- Understanding and working knowledge of cardiovascular physiology, biomechanics, perception science or related fields

- Experience with bio-signals, wearables, inertial measurements

- Peer-reviewed machine-learning publications, or other examples (Github, apps, products)

- Experience leading or leading team of data scientists, engineers in a research or product setting

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