AI Training Data Delivery Director

Role Summary: The Director of HITL (Human-in-the-Loop) Delivery is the senior executive accountable for the global AI Training Data delivery organization. You will own the P&L and be responsible for building a world-class, scalable, and highly efficient operational engine that serves as the foundation of our business. This is not a traditional BPO leadership role; it requires a unique blend of rigorous operational discipline, large-scale workforce management, and a deep, practical understanding of the AI/ML lifecycle. You will lead a multi-tiered organization of operations leaders to deliver unparalleled quality and efficiency, manage executive-level client relationships, and drive the strategic growth of our global delivery footprint. Responsibilities Strategic & Financial Leadership Full P&L Ownership: Own and manage the complete Profit & Loss (P&L) for the entire HITL Delivery business unit, with a relentless focus on margin improvement, revenue growth, and cost optimization. Global Workforce Strategy: Architect and execute a long-term global workforce strategy. Make critical decisions on geographical footprint, talent sourcing, labor arbitrage, and diverse staffing models (e.G., full-time, gig economy, specialized contractors) to create a competitive advantage. Business Scalability & Organizational Design: Design and build the organizational structure, processes, and leadership team required to scale operations from hundreds to thousands of data specialists across multiple geographies without sacrificing quality or efficiency. Commercial Partnership: Serve as the senior delivery expert in partnership with the Solutioning team on our largest and most strategic deals. Provide critical input on delivery feasibility, risk assessment, and commercial modeling to ensure new business is both winnable and profitable. Operational Excellence & Governance Lead Global Operations: Lead a team of Directors and Senior Managers ("leader of leaders") who oversee the day-to-day delivery of all client projects. SLA & Quality Governance: Be the ultimate owner of all delivery SLAs. Establish and govern the highest standards for data annotation quality, throughput (AHT, TAT), and overall operational performance across the entire portfolio. Drive Continuous Improvement: Foster a culture of data-driven, continuous improvement. Champion methodologies (e.G., Lean, Six Sigma) adapted for the AI data context to systematically enhance processes, tools, and team performance. Portfolio Risk Management: Identify, assess, and mitigate systemic risks across the client portfolio, including operational, financial, and reputational risks. Executive Client & Stakeholder Management Executive Client Relationships: Own and cultivate relationships with senior executives at our key client accounts. Act as the senior point of accountability for all delivery matters, leading executive steering committees and quarterly business reviews (QBRs). Strategic Internal Collaboration: Partner with the Director of AI Transformation to champion and deploy new AI-powered tools and co-pilots into the live workforce, ensuring their successful adoption and ROI. Collaborate with the Director of Platform Deployment to provide critical feedback on platform usability, performance, and feature requirements from an operator's perspective. Work in lockstep with the Director of Solutioning to ensure a seamless transition from pre-sales to delivery. Skills & Qualification Required Qualifications: Experience: 10+ years of progressive experience in large-scale BPO, ITO, or technology-enabled service operations , with at least 3 years in a senior leadership role leading other senior managers or directors . P&L Ownership: Demonstrable and mandatory experience owning and managing a significant P&L ($6M+ annually) for a service delivery organization. Global Operations Leadership: Proven success in building, scaling, and managing large (1000+ FTE), distributed teams across multiple geographies and cultures. AI/ML Acumen: A strong, strategic understanding of the AI/ML development lifecycle. You must be able to articulate the direct impact of data quality, consistency, and annotation methodology on a client's model performance. You are not a data scientist, but you speak their language.