Lead Data Scientist
Rejoignez l'équipe NIKE, Inc.
Loin de se contenter d'équiper les plus grands athlètes mondiaux, NIKE, Inc. explore les potentiels, abolit les frontières et repousse les limites du possible.
L'entreprise recherche des personnes capables d'évoluer, de réfléchir, de rêver et de créer. L'épanouissement de sa culture repose sur son ouverture à la diversité et sur sa façon d'encourager l'imagination.
La marque a besoin de personnes talentueuses, de leaders et de visionnaires. Chez NIKE, Inc., chacun contribue, par ses compétences et sa passion, à jouer un match difficile en constante évolution.
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. The Data Science team in the NSRL unearths insights and automates recommendations to help everyday athletes* move better, while also supporting our product innovation teams!
Who we are Looking For
We are looking for an outstanding Lead Data Scientist to create capabilities and provide insights that enhance athlete performance, drive product innovation, and elevate consumer experiences, working primarily within our Field Research and Athlete* Services teams in the NSRL.
In this role, you will transform raw athlete data collected in the lab and gathered in the real world using wearable devices and IoT sensors into meaningful insights and data products.
Key Responsibilities
Develop and implement innovative ways of annotating and analyzing athlete* data to improve performance, prevent injury, and encourage continued engagement with Nike.
Collaborate with sports scientists, researchers, and product designers to translate insights into actionable feedback for the development of future footwear and apparel products.
Build and maintain robust data pipelines that ensure high-quality, relevant data collection and storage, facilitating efficient data analysis and model development.
Communicate findings and insights to broad audiences to influence decision-making and strategy.
Qualifications
Advanced quantitative degree (Statistics, Mathematics, Computer Science, Quantitative Social Science, Engineering, Physics or related field)
2 -4 years of industry experience as a data scientist or machine learning engineer
Proficiency in common coding languages (Python, SQL, R)
Experience working with sports-science time-series sensor data (biomechanical, physiological, and performance).
Experience in machine learning, statistical modeling, and algorithm development
Experience working with Amazon Web Services
Demonstrated expertise in using Tableau for insight sharing to broad audiences.
Proven track record of working cross-functionally and critically evaluating complex data from multiple perspectives, including questioning assumptions and validity.
Excellent written and verbal communication skills, including ability to develop and deliver presentations.
Passion for sports and a deep understanding of the athletic performance ecosystem.
Comfortable owning projects end-to-end, including data ingestion, preprocessing, and cleaning, to developing, deploying predictive models, and extracting actionable insights.
NIKE, Inc. est une entreprise en pleine croissance cherchant à intégrer à son équipe des personnes capables de se développer avec elle.
Nike offre un généreux programme de rémunération globale, un environnement de travail décontracté, une culture variée et inclusive et une atmosphère dynamique propice au développement professionnel.
Quels que soient le site ou le poste, les employés de Nike partagent tous la même mission stimulante : apporter inspiration et innovation aux athlètes* du monde entier.
NIKE, Inc. s'engage à embaucher un personnel diversifié. Les candidats qualifiés seront considérés sans tenir compte de leur origine, couleur de peau, religion, sexe, nationalité, âge, orientation sexuelle, identité de genre, expression de genre, statut de vétéran ou handicap.