Data Scientist

Senior Data Scientist Responsibilities

• Deliver against the overall data science strategy to drive in‑store and digital merchandising, marketing, customer loyalty, and operational performance.
• Partner with product development to define requirements which meet system and customer experience needs for data science projects.
• Partner with Merchandising, Supply Chain, Operations and customer insights to understand the journey that will be improved with the data science deliverables.
• Build prototypes for, and iteratively develop, end‑to‑end data science pipelines including custom algorithms, statistical models, machine learning and artificial intelligence functions to meet end user needs.
• Partner with product development and technology teams to deploy pipelines into production MLOps environment following Safe Agile methodology.
• Proactively identify data‑driven solutions for strategic cross‑functional initiatives, develop and present business cases, and gain stakeholder alignment of solution.
• Responsible to define, document and follow best practices and approval for ML/AI development at Meijer.
• Own communication with data consumers (internal and external) to ensure they understand the data science products, have the proper training, and are following the best practices in application of data science products.
• Define and analyze Key Performance Indicators to monitor the usage, adoption, health and value of the data products.
• Identify and scope, in conjunction with IT, the architecture of systems and environment needs to ensure that data science systems can deliver against strategy.
• Build and maintain relationships with key partners, suppliers and industry associations and continue to advance data science capabilities, knowledge and impact.
• Perform other duties as assigned or required.

• Deliver against the overall data science strategy to drive in‑store and digital merchandising, marketing, customer loyalty, and operational performance.
• Partner with product development to define requirements which meet system and customer experience needs for data science projects.
• Partner with Merchandising, Supply Chain, Operations and customer insights to understand the journey that will be improved with the data science deliverables.
• Build production ready prototypes for, and iteratively develop, end‑to‑end data science pipelines including custom algorithms, statistical models, machine learning and artificial intelligence functions to meet end user needs.
• Partner with product development and technology teams to deploy pipelines into production MLOps environment following Safe Agile methodology.
• You will architect, design, and lead the development and implementation of machine learning algorithms and models for a data science capability within Digital Services, Merchandising, Marketing, Supply Chain and Operations.
• Design, code, and implement new industry‑leading design patterns in data science, to create the convention/technique/practice that teams are accountable to follow.
• Foster a culture of innovation by keeping up‑to‑date with the latest industry trends and research across data science, AI and retail.
• Mentor data science teams and promote a culture of curiosity, accountability, & enthusiasm.
• Provide data science strategy to ensure code modernization and industry standard patterns for data science.
• Provide guidance to development teams within domain (Commerce, Services or Mobile).
• Lead non‑functional requirements (NFRs) such as security, reliability, performance, maintainability, scalability, and usability related to data science.
• Promote the implementation of new technology, solutions, and methods to improve business processes, efficiency, effectiveness, and value delivered to customers.
• Proactively identify data‑driven solutions for strategic cross‑functional initiatives, develop and present business cases, and gain stakeholder alignment of solution.
• Responsible to define, document & follow best practices & approval for ML/AI development at Meijer.
• Own communication with data consumers (internal and external) to ensure they understand the data science products, have the proper training, and…