Lead, AI & Data Products (Technical)

Our client is a globally recognized organization headquartered in Southeast Asia, specializing in aviation support services, air cargo handling, ground operations, and institutional food solutions. The company provides a wide range of services, including passenger assistance, ramp and baggage handling, aviation security, aircraft cleaning, and laundry operations for the aviation sector.

Beyond aviation services, the organization operates large-scale central kitchens and food production facilities, catering to airlines, corporate clients, and institutions across diverse cuisines and service formats. With a strong presence across Asia-Pacific, Europe, the Americas, the Middle East, and Africa, the company supports global travel, trade, and logistics through an extensive international network, further strengthened by strategic acquisitions in recent years.

Role Description:

• The organization is investing in a small number of high-impact AI and data products intended to operate across its global station network.
• Each requires a single accountable owner who can hold the technical architecture and the product direction together, from first implementation through multi-station scale.
• We are looking for a Lead, AI & Data Products to take that ownership. 
• This is a senior individual contributor role for an engineer or applied scientist who has moved into product leadership, not a product manager with adjacent technical exposure.
• You will be expected to reason about model design, data architecture and production trade-offs at the level of the engineers building the system, and to hold the roadmap, business case and stakeholder relationships at the level of the executives funding it. 
• The role operates horizontally.
• You will act as the single-threaded owner for assigned initiatives, working with the AI Centre of Excellence on patterns and feasibility, with Data Engineering and Platform on the data foundation, with Operational Excellence and the business product owners on process change, and with the forward deployed engineers in region on station rollout.

This is a hands-on role.

• You will review and contribute to code, make architectural calls, and spend meaningful time at the frontline understanding operations directly rather than through documentation.

Responsibilities:

Technical product ownership

• Own the product end to end for assigned AI and data initiatives: discovery, technical architecture, roadmap, delivery sequencing and global rollout readiness.
• Act as single-threaded owner. One person accountable for whether the product works, is adopted, and delivers its business case across stations and regions.
• Convert programs into products. 
• Move initiatives currently run as projects onto a product footing with a roadmap, a lifecycle, named users and a benefit measure.
• Make the build decisions: in-house, CoE pattern, capability center, or vendor. Own the trade-off and the consequences.

Technical depth

• Set the architecture for AI and data products spanning forecasting, optimization, simulation, GenAI and agentic workflows, and defend it in technical review.
• Work in the codebase. Review designs and pull requests, prototype where it resolves ambiguity faster than discussion, and contribute directly when delivery depends on it.
• Own production quality: model robustness, explainability, evaluation, monitoring, latency and cost in service, not only at launch.
• Build on the platform. Consume governed data through the semantic layer and apply CoE guardrails rather than constructing parallel capability.

Business and delivery leadership

• Translate operational problems into technical solutions and, equally, translate technical constraints into terms operations and commercial leadership can act on.
• Own the business case with the business product owner: the benefit hypothesis, the measurement approach and the reported outcome.
• Lead without direct authority across engineering, data science, operations and regional teams, including delivery through the capability center and vendor partners.
• Work from the frontline.
• Regular Gemba at stations, with operational understanding built firsthand.

Requirements:

• 10+ years in engineering, applied AI or data science, with a substantial portion spent building and running systems in production rather than specifying them.
• Demonstrable record of owning technical products end to end and scaling them beyond a first implementation into multi site or multi-market operation.
• Depth in one or more of: forecasting (time series, probabilistic, hierarchical), optimization (MIP, LP, heuristics), simulation, or production GenAI and agentic systems, with working literacy across the others.
• Strong Python and SQL, with practical command of the modern data and ML stack: cloud data platforms, pipelines, model deployment, evaluation and monitoring.
• Experience influencing senior stakeholders and operating across engineering, operations and commercial functions without direct reporting lines.
• Aviation, logistics or complex physical operations exposure is an advantage, not a prerequisite.

Qualifications

• Bachelor's, MSc or PhD in Computer Science, Engineering, Data Science, Mathematics, Operations Research or a related field. Personal characteristics
• Technical credibility earned through building, not through managing builders.
• Judgement on trade-offs between scope, speed and quality, with the confidence to decide under incomplete information.
• Clear communication with engineers, operators and executives, adjusting register without losing precision.
• Comfort with ambiguity and being accountable for outcomes rather than activity.