Head of Data & AI

The Head of Data and AI leads the strategy, governance, and execution of data and AI initiatives across portfolio companies, turning cross-business data into measurable business value. This leader oversees diverse enterprise data platforms, advanced analytics, and AI/ML solutions spanning manufacturing, airline operations, hospitality, financial services, and solar field construction., * Define and implement the enterprise Data & AI strategy and roadmap aligned with corporate and business-unit objectives.

• Establish data architecture and governance frameworks, including data quality, cataloging, access, privacy, and compliance.
• Oversee development and operation of data platforms (data lake/warehouse), BI dashboards, and advanced analytics models.
• Lead AI/ML initiatives such as predictive maintenance, demand forecasting, fraud detection, data and event hub, data initiatives across a wide array of SaaS providers, and personalization use cases.
• Build and manage a multidisciplinary team of data engineers, data scientists, and analytics professionals.
• Define and track KPIs and ROI for Data & AI initiatives, communicating impact to senior leadership.

Artificial Intelligence (AI), Financial Services, Construction, Artificial Intelligence (AI), Manufacturing Operations, Cataloguing, Data Quality, Reporting Dashboards, Data Lake, Business Intelligence, * Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or related field.

• 5+ years of experience in data, analytics, or AI leadership roles with a track record of delivering high-value initiatives.
• Deep expertise with modern data platforms and tooling (e.g., cloud data warehouses, distributed processing, ML frameworks).
• Strong customer-centric mindset balanced with operational rigor.
• Proven ability to translate complex analytical outputs into business-relevant insights and actions., * Experience operating in multi-business-unit or multi-industry environments.
• Strong stakeholder management skills and ability to align diverse teams around a common data vision.
• Familiarity with responsible AI principles, data privacy regulations, and model governance practices.