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VP of Data Science and Architecture
1 week ago
The Internet of Behaviors Company is committed to revolutionizing the retail industry through innovative technologies and data-driven solutions. As the VP of Data Engineering & AI, you will play a key role in shaping our data strategy and leading our Data Engineering & AI Team.
About the CompanyWe believe that physical retail is not dead but has yet to evolve alongside today's modern customers. Our goal is to connect physical storefronts with customers in a way that rivals the convenience of online shopping. To achieve this, we need experts like you who can develop cutting-edge software and hardware solutions to reimagine the retail experience.
- Data-Driven Solutions: Develop data-driven solutions to improve customer experience, personalized service, and ultimately, a delightful way to shop.
- Team Leadership: Lead and mentor a leadership team responsible for Data Engineering, Data Science, Data Analytics, and AI/ML modeling.
- Data Strategy: Develop a comprehensive data strategy to align with user needs, strategic business objectives, stakeholder expectations, and technological advancements.
We're looking for an expert with a strong understanding of data engineering, including data architecture design, data pipeline development, data modeling, data quality, and governance. Your experience in platforms and data science tools to solve complex business problems, including machine learning, quantitative methods, predictive modeling, and any other relevant analytics technology frameworks and techniques, will be invaluable to our team.
Requirements- Bachelor's or Master's degree in Engineering, Computer Science, Artificial Intelligence, Information Technology, or a related field.
- 10+ years of hands-on experience in data engineering practices with AI model development and training.
- Track record of recruiting and developing talent in a global environment; strong management expertise and proven experience developing senior talent.
- Expert understanding of data engineering, including data architecture design, data pipeline development, data modeling, data quality, and governance.
