Data Scientist – Merchandising & Inventory Machine Learning - Hybrid – Seattle, WA

Nordstrom • Seattle, WA

1d • Hybrid

About The Position

As a Data Scientist in the Merchandising & Inventory Machine Learning team at Nordstrom, you will work on solving complex merchandising and inventory challenges using machine learning and optimization techniques to enhance item performance 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 merchandising, such as demand forecasting, assortment, inventory positioning and pricing optimization. You will collaborate with cross-functional teams (other data scientists, software engineers, product teams, merchandising & inventory business partners ) 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 make a meaningful impact on 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 item lifecycle performance. 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 product ion -ready machine learning models / optimization algorithms for areas such as demand forecasting, assortment, and pricing optimization. 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 retail
• 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 item lifecycle performance.
• 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 product ion -ready machine learning models / optimization algorithms for areas such as demand forecasting, assortment, and pricing optimization.
• 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

Benefits

• Medical/Vision, Dental
• Retirement and Paid Time Away
• Life Insurance and Disability
• Merchandise Discount and EAP Resources
• 401k, medical/vision/dental/life/disability insurance options, PTO accruals, Holidays, and more.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees