Supply Chain Machine Learning Data Scientist - Hybrid - Seattle, WA

Nordstrom•Seattle, WA

3d•Hybrid

About The Position

As a Data Scientist in Supply Chain Machine Learning team at Nordstrom, you will work on solving complex supply chain challenges using machine learning and optimization techniques to enhance operational efficiency and deliver exceptional customer experiences. Your focus will be on analyzing large datasets, building predictive models, and developing algorithms to support decision making in all facets of Nordstrom’s supply chain, such as demand forecasting, order fulfilment and logistics. You will collaborate with cross-functional team (other data scientists, software engineers, product teams and supply chain operations) to design, implement and deploy innovative, robust and scalable solutions. This is an excellent opportunity to leverage your technical and communication skills, apply cutting-edge machine learning methods, and making a meaningful impact to Nordstrom’s operations and customer experience. A day in the life... Partner with product managers, engineers and other stakeholders to understand business problems and identify opportunities for machine learning applications to optimize Nordstrom’s supply chain and customer experience Perform exploratory data analysis and statistical modelling to extract actionable insights from large and complex datasets Build and maintain robust data pipelines to process, clean and transform data from diverse sources (e.g. SQL datasets, APIs, flat files) Design, develop and implement scalable and production-ready machine learning models / optimization algorithms for areas such as demand forecasting, order fulfillment, operational efficiency Implement robust evaluation and monitoring to validate the performance and reliability of machine learning models Communicate complex findings, insights and trade-offs to technical and non-technical stakeholders

Requirements

Bachelor's, Master's, or PhD in Statistics, Data Science, Computer Science, Engineering, Operations Research, or a related technical field; or Equivalent related professional experience.
Minimum 1 years hands-on experience in Data Science or Machine Learning roles
Minimum 1 years of professional SQL experience, performing advanced queries and optimization techniques
Proficient coding skills in Python, with experience writing clean, maintainable, and optimized ML code
Experience applying statistical and machine learning algorithms, such as regression, decision trees, clustering, neural networks, survival analysis, along with model evaluation techniques
A passion for solving complex problems with creative approaches.
Strong communication and collaboration skills, with the ability to work both independently and as part of a team

Nice To Haves

Experience developing and deploying machine learning models in production environments
Familiarity with machine learning libraries/frameworks such as Scikit-learn, TensorFlow, and PyTorch
Curiosity to understand business processes and identify opportunities for innovation and improvement using data-driven approaches
Exposure to cloud-based ML infrastructure and data pipelines (e.g., AWS, GCP, Azure)
Knowledge and experience in supply chain
Motivation to stay current with advancements in data science and machine learning technologies

Responsibilities

Partner with product managers, engineers and other stakeholders to understand business problems and identify opportunities for machine learning applications to optimize Nordstrom’s supply chain and customer experience
Perform exploratory data analysis and statistical modelling to extract actionable insights from large and complex datasets
Build and maintain robust data pipelines to process, clean and transform data from diverse sources (e.g. SQL datasets, APIs, flat files)
Design, develop and implement scalable and production-ready machine learning models / optimization algorithms for areas such as demand forecasting, order fulfillment, operational efficiency
Implement robust evaluation and monitoring to validate the performance and reliability of machine learning models
Communicate complex findings, insights and trade-offs to technical and non-technical stakeholders