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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.
Job Responsibility:
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 production-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 have:
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
What we offer:
Medical/Vision, Dental, Retirement and Paid Time Away