Senior Data Scientist

Nike•Beaverton, OR

About The Position

NIKE, Inc. is seeking an experienced Senior Data Scientist to join its Data Science & Advanced Analytics team within Nike’s Supply Chain and Planning Technology (SCPT). This role is part of a cross-functional Agile squad and is crucial for providing technical expertise and leadership in building predictive and prescriptive analytical solutions for Nike’s global supply chain and operations, focusing on manufacturing, materials sourcing, and supply planning. The ideal candidate is a dependable teammate with drive, curiosity, strong hands-on data science and analytics experience (including forecasting, optimization, and simulation), and a deep understanding of supply chain and operations. They should be adept at distilling business complexity into testable hypotheses and scalable solutions, proficient in various advanced modeling techniques, and able to identify the optimal technique based on business requirements, data availability, and technique limitations. A consulting mindset, continuous learning, and knowledge sharing are highly valued.

Requirements

Advanced quantitative degree (Statistics, Mathematics, Operations Research, Computer Science or related field) and at least 5 years of related industry experience as data scientist or applied scientist, or Bachelor’s degree and 7-12 years related work experience. Any suitable combination of education, experience or training will be accepted.
Deep knowledge of and hands-on ability in data science and optimization methodologies, including classical models, artificial intelligence and machine learning algorithms, and linear and non-linear optimization techniques.
Hands-on experience building optimization models using Python and commercial solvers (e.g., Gurobi, CPLEX, or equivalent).
Advanced skills in programming languages (particularly Python and SQL) and ability to apply them for data acquisition, preprocessing, modeling, and monitoring.
Familiarity with the wide range of data science/analytics software tools (e.g., Jupyter Notebook, SQL consoles, Hadoop, Spark) and cloud computing platforms (e.g., Amazon Web Services, Databricks).
Experience in building, training, scoring, tuning and maintaining predictive models in production at enterprise scale and familiarity with mainstream packages relevant to managing all stages of the model lifecycle.
Deep knowledge and experience in supply chain, supply planning, manufacturing & sourcing, and logistics.
Some hands-on ability and knowledge in data engineering, software engineering, and at-scale production.
Proven track record of working cross-functionally and having a consulting mindset to critically evaluate complex business information from multiple perspectives, including questioning assumptions and validity.
Familiarity with the Agile development process and demonstrable ability to prepare a project plan, communicate the plan to the team and break the work down to trackable tasks.
Excellent written and verbal communication skills, including ability to develop and deliver presentation.

Nice To Haves

Open-minded, thoughtful, collaborative, and determined to produce great work for a global audience.

Responsibilities

Design, develop, and productionize optimization and analytical models to support supply chain decisions.
Apply statistical modeling and simulation techniques to complement optimization-based solutions.
Conduct model performance analysis, scenario testing, and sensitivity analysis to support decision-making under uncertainty.
Build and maintain end-to-end analytical pipelines, covering data ingestion, feature engineering, model training, validation, and deployment.
Work with the squad and global supply chain and operations partners to assess business requirements, data constraints, and pros and cons of alternative modeling techniques to determine the best solution approach and business tradeoffs.
Contribute to the planning, scheduling, and value measurement of work to meet timeline targets and success criteria.
Explore business drivers of cost, on-time performance of products and materials, root causes of quality issues, and strive to achieve Nike’s sustainability goals.
Help shape Nike’s analytical platforms and products by identifying foundational data science capabilities and creating reusable analytical components.
Support the adoption of analytic products through effective storytelling and collaboration with key partners.
Share knowledge with others on the team and contribute to best practices for model governance, reproducibility, and responsible use of data science solutions.