Director of AI and Machine Learning
OneSpaWorld
Full-time
On-site
Coral Gables, Florida, United States
AI and Machine Learning
Director of AI & Machine Learning
Position Summary
The Director of AI & Machine Learning will lead enterprise-wide AI transformation to accelerate revenue growth, improve operational efficiency, and modernize the company’s data and analytics platform. This senior leadership role defines AI strategy, builds high-performing teams, and delivers measurable business outcomes through advanced machine learning and agentic automation.
The Director will oversee internal teams and external consultants—including Data Scientists, Data Engineers, Data Architects, ML Engineers, and Automation Specialists—while partnering with executive leadership to embed AI into core business strategy.Key Responsibilities
Enterprise AI Strategy
Position Summary
The Director of AI & Machine Learning will lead enterprise-wide AI transformation to accelerate revenue growth, improve operational efficiency, and modernize the company’s data and analytics platform. This senior leadership role defines AI strategy, builds high-performing teams, and delivers measurable business outcomes through advanced machine learning and agentic automation.
The Director will oversee internal teams and external consultants—including Data Scientists, Data Engineers, Data Architects, ML Engineers, and Automation Specialists—while partnering with executive leadership to embed AI into core business strategy.Key Responsibilities
Enterprise AI Strategy
- Define and execute a multi-year AI and machine learning roadmap aligned with corporate strategy.
- Identify high-impact opportunities for:
- Revenue growth through predictive analytics and customer intelligence
- Pricing optimization and demand forecasting
- Agentic automation for operational efficiency
- Establish measurable KPIs tied to revenue lift, cost savings, and productivity improvements.
- Present AI strategy and progress to executive leadership.
- Lead development and deployment of production-grade ML models.
- Build scalable MLOps frameworks for model lifecycle management.
- Implement experimentation and testing frameworks.
- Ensure responsible AI practices, compliance, and data governance.
- Identify workflows suitable for AI-driven automation.
- Deploy intelligent agents integrated with ERP, HR, and operational systems.
- Reduce manual processes and increase productivity through automation.
- Measure ROI of automation initiatives.
- Lead modernization of enterprise data platform and operations, including:
- Data lakehouse architecture
- Real-time streaming pipelines
- Data governance and cataloging
- DataOps and MLOps integration
- Analytics and BI modernization
- Partner with IT Ops to scale infrastructure for AI workloads.
- Improve data quality, availability, and accessibility across the company.
- Lead teams of Data Scientists, ML Engineers, Data Engineers, Data Architects, and Automation Specialists.
- Manage consultants, vendors, and technology partners.
- Develop hiring strategy and succession planning.
- Establish AI Centers of Excellence.
- Drive adoption of AI across departments.
- Collaborate with Sales, Marketing, Finance, Operations, and Product leadership.
- Translate complex AI concepts into business value.
- Support executive-level decision-making on AI investments.
- Foster a data-driven culture throughout the organization.
- Bachelor’s degree in Computer Science, Data Science, AI, Engineering, or related field (Master’s/PhD preferred).
- 10–15+ years in AI, machine learning, or advanced analytics.
- 5–8+ years leading enterprise-scale teams and programs.
- Proven track record delivering AI initiatives that increased revenue or reduced costs.
- Experience modernizing enterprise data platforms.
- Strong understanding of:
- Machine learning and deep learning
- Data architecture and engineering
- MLOps/DataOps
- Cloud infrastructure
- Automation platforms and agentic workflows
- Experience presenting to executive leadership.
- Experience with generative AI and large language models.
- Experience in your industry vertical.
- MBA or executive leadership training.
- Revenue growth attributable to AI initiatives.
- Operational cost reduction via automation.
- Adoption of AI tools across departments.
- Data platform modernization milestones.
- Time-to-production for ML models.
- Team growth and retention.