Assaia International AG provides AI-based software solutions for global airports and airlines. We were founded in February 2018 in Switzerland, headquartered in Zurich and New York and we have a fast-growing team located across Europe and North America.
Assaia offers AI-based computer-vision solutions that monitor and analyse aircraft turnaround processes from video streams. Assaia’s solutions help airports and airlines improve on-time performance, efficiency, safety, and sustainability metrics, helping them get their passengers to their destinations on time. We work with aviation companies across the world including New York JFKIAT, Toronto Pearson, United Airlines, British Airways, London Heathrow and more. See details and demos on our website and Linkedin!
About the role
We're looking for a Data Scientist to join Event Time Advancements, the team behind Assaia's flight event time and delay prediction capabilities – especially departure delay prediction and related operational forecasting problems.
This is an applied ML role with end-to-end ownership. You'll work across the full lifecycle: building datasets and features from evolving event histories, training and evaluating models, and helping keep them reliable in live use together with backend engineering.
A big part of the role is working on dynamic prediction problems rather than one-row-per-case modelling. The same flight or operational event evolves over time through repeated updates, and the model needs to make useful predictions from a changing picture as new information arrives. This makes the work especially interesting for someone who enjoys temporal modelling, sequential reasoning, and building systems that stay useful across multiple decision points.
You should be comfortable making judgment calls on data trust, validation design, and trade-offs, and defending those calls to both technical and non-technical stakeholders.
The team builds and owns its own tooling for dataset generation, training, and parts of real-time inference – so software engineering discipline (testing, review, deliberate design trade-offs) matters here as much as modelling skill.
You'll directly shape live prediction quality for flight-event timing and delay-related capabilities used by operational teams, help determine which data is usable as ETA expands to new airports and prediction tasks, and turn one-off experiments into repeatable workflows the team can build on.
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