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$ cat jobs/vice-president-data-architecture-engineering-ai-kohl-s-0219f38bb7a3.json

Vice President, Data Architecture, Engineering & AI

kohl s·US·United States·mid
mldata science
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Role Specific Information Job Description About the Role The VP, Data Architecture and Engineering is accountable for the vision, strategy, and execution of the enterprise data platform and engineering capabilities that enable business growth, operational efficiency, and customer centric innovation. This leader partners closely with business and technology teams to elevate data into a durable and scalable enterprise asset. In alignment with the VP of A.I and Transformation, who defines enterprise A.I strategy and prioritizes business use cases, this role owns the data architecture, platforms, and engineering foundations required to operationalize, scale, and reliably run A.I solutions in production across the enterprise. What You’ll Do Define and own the data product and technical data science strategy, aligning with enterprise technology and business priorities Lead the design, development, and lifecycle management of data products that support business operations, customer insights, and innovation Build and scale data science capabilities, including machine learning, AI-driven solutions, and advanced analytics to optimize retail operations and customer engagement Partner with business units, analytics, engineering, and product management to prioritize high-value use cases for both data products and data science applications Oversee data governance, stewardship, and metadata management to ensure accuracy, compliance, and trust Champion modern data and AI architectures, including cloud-native platforms, APIs, data streaming services, and real-time analytics Drive data democratization, ensuring business users can easily discover, access, and leverage insights Foster a product mindset and a research-to-production pipeline for data science solutions, ensuring scalability and usability Manage, mentor, and grow a high-performing team of data product managers, architects, engineers, and data scientists Define and monitor KPIs for both data products and data science initia

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