Lead Data Scientist (Remote Work Eligible)

Open to remote work except in South Dakota, Vermont and West Virginia.

The annual base salary for this position ranges from $119,400.00 in our lowest geographic market to $267,500.00 in our highest geographic market. Actual salary will vary based on a candidate's location, qualifications, skills and experience.

Information about benefits can be found here.
 

WHO WE ARE LOOKING FOR

We are looking for an experienced Lead Data Scientist to join Nike’s Insights, Data Science, and Analytics (IDSA) team. This role will be part of a multi-functional agile squad and will provide technical expertise and leadership in building predictive and prescriptive analytic solutions to support Nike’s supply and inventory planning strategies and optimization goals.

The candidate needs to be a diligent teammate with strong hands-on data science and analytics experience, drive, and curiosity. You know how to rise above the numbers and explain the crucial insights to users at all levels. You simplify and distill business complexity into testable hypotheses and scalable solutions. While you are proficient in a plethora of advanced modeling techniques, you are able to identify the technique optimal for the task at hand based on the business requirements, your knowledge of the data and the technique’s assumptions, interpretability and adaptability. You ask good questions, are continually learning as well as finding opportunities to share knowledge with others.

WHAT YOU WILL WORK ON

If this is you, you will be part of IDSA’s Supply & Inventory Planning (S&IP) Data Science squad. We deliver data science products and solutions to support Nike’s business strategies and initiatives spanning demand, supply, and inventory management.

You will have opportunities to influence product direction, guide standard methodologies in data science and engineering, and help deliver technical projects from inception to completion. Specifically, you will:

• Work with the squad through assessment of business requirements, data constraints, and pros/cons of alternative analytics techniques to determine the best solution approach. Contribute to the planning, scheduling and value measurement of the work to meet timeline targets and success criteria.

• Drive implementation, and maintenance of production-grade data science (statistical, mathematical, and AI/ML) and operation research models to determine optimal level of factory utilization, capacity, costs, lead times, and reduction of un-planned.

• Help deliver improvement of aligning manufacturing capacity with demand by understanding product attributes, partner’s manufacturing capabilities and complexity.

• Explore business drivers of cost, timely performance (of products and materials), root causes of quality issues, and …