Thomas Ogden
Thomas Ogden is a Senior ML Engineer in Financial Engineering at Spotify. He builds tools, mostly with probabilistic machine learning on sequences and graphs. He once did a PhD in Quantum Optics theory and still thinks about physics a lot.
Session
Sailors avoid the word ‘rope’. Once it has a job, it becomes a line with a specific name: halyard, sheet or warp. In forecasting, we often do the opposite — projections, baselines, scenarios and targets all end up being called ‘the forecast’.
In practice, forecasts live in a high-dimensional space. They vary by origin date, prediction horizon, scenario assumptions, uncertainty representation, reconciliation level and decision context. Treating them as a single artefact creates ambiguity, semantic drift and misaligned expectations.
In this talk, I’ll show how we reframed forecasting at Spotify as a structured prediction problem rather than simply a modelling task. I’ll cover practical design patterns for representing forecast objects across multiple origins and scenarios, handling probabilistic outputs, implementing hierarchical reconciliation and tracking lineage and versioning in Python-based systems.
Aimed at data scientists and ML engineers working with production systems, this talk offers a framework for thinking about forecast dimensionality and concrete implementation patterns you can apply in your own forecasting platforms.