About Us

Headquartered in Singapore, SATS Ltd. is one of the world’s largest providers of air cargo handling services and Asia’s leading airline caterer. SATS Gateway Services provides airfreight and ground handling services including passenger services, ramp and baggage handling, aviation security services, aircraft cleaning and aviation laundry. SATS Food Solutions serves airlines and institutions, and operates central kitchens with large-scale food production and distribution capabilities for a wide range of cuisines.

SATS is present in the Asia-Pacific, the Americas, Europe, the Middle East and Africa, powering an interconnected world of trade, travel and taste. Following the acquisition of Worldwide Flight Services (WFS) in 2023, the combined SATS and WFS network operates over 225 stations in 27 countries. These cover trade routes responsible for more than 50% of global air cargo volume. SATS has been listed on the Singapore Exchange since May 2000. For more information, please visit www.sats.com.sg

Why Join Us

At SATS, people are our greatest asset and we build our success on the knowledge, expertise and performance of every contributor, by embracing diversity and uniqueness. As part of our holistic approach and commitment to embracing FAM (Fulfilling, Appreciated, Meaningful) in the workplace, we offer the runway to develop Fulfilling careers that foster your career growth, recognising and Appreciating the strength of talent and capabilities that we continue to build internally; and inspiring and encouraging each other to make Meaningful contributions in the work we do at SATS.

Key Responsibilities

The Role 

SATS is building its data and AI capability and embedding AI into daily operations. The AI centre of excellence provides the AI platform and leads research and innovation. The data engineering and platform team provides the data platform, master data and governance. The data science team builds AI solutions and delivers them at scale. The analytics and AI products team turns these into products and deploys them with the business in the regions and at the stations. 

Forward Deployed Engineers sit in the analytics and AI products team and work directly with the business teams at our stations, the airport cargo and ground handling sites. They turn the shared platform and data into working AI and analytics products and support them in daily use. What they learn in the field goes back to the other teams. 

As a Forward Deployed Engineer, you own defined products or workstreams from build to rollout at one or more stations, with guidance from senior colleagues on architecture and priorities. You write production code against live operational data, support users at launch, and stay accountable for your products once they are in service. 

Key Responsibilities 

Operational Delivery

• Own delivery of defined products or workstreams at one or more stations: scoping, build, integration, testing, launch, post-launch support and rollout.
• Spend time with operational teams and specify the local data reality from direct observation.
• Ship production Python code with tests and documentation against live operational data.
• Reuse and extend existing integrations, configuration and runbooks so that each new station is faster to bring live than the last.
• Make routine design decisions independently and raise architecture and scope questions early. 

Build Shared Platforms

• Use the governed data platform and shared data definitions, and apply the central AI team's patterns and safeguards, including human oversight, access and data residency rules.
• Integrate AI, machine learning and optimisation components into operational workflows using established patterns, with evaluation, monitoring and cost control.
• Build governed datasets and dashboards that let operations teams, airlines and freight forwarders answer their own questions. 

Own Results 

• Track adoption and benefit against the business case and report them accurately.
• Feed field learnings back to the central AI and data platform teams.
• Share knowledge with Associate engineers through pairing and code review. 

Key Requirements

• Bachelor's degree or higher in Computer Science, Engineering, Mathematics, Statistics, Operations Research, Industrial Engineering, Data Science or a related quantitative field, or equivalent practical experience. 
• Typically 3 to 5 years in software, data or machine learning engineering, or equivalent demonstrated impact, including delivery of production systems used by business users.
• Experience taking products or features from scoping through production and supporting them in service.
• Strong Python, SQL and API development, with enough front-end skill to build a working prototype.
• Working experience in at least one of the three areas of depth, and working knowledge of at least one other.
• Experience with cloud data platforms (for example Azure) and with integrating batch and real-time data sources, plus working knowledge of containers (for example Docker) and CI/CD.
• Possessing working knowledge of all three areas of depth is advantageous.
• Exposure to aviation, logistics or complex operations is advantageous