Lead the integration of disparate data sources, both structured and unstructured, to create a unified and comprehensive view for analytics and reporting.
Oversee the deployment of machine learning models into production environments, ensuring seamless integration with business processes.
Implement key performance indicators (KPIs) to monitor and measure the success of data science and engineering initiatives, making data-driven decisions.
Manage budgets for data science and engineering projects, including resource allocation, software licensing, and hardware procurement.
Provide training and development opportunities for the data science and engineering team to enhance technical skills and stay current with industry advancements.
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