The SKA Observatory will generate 700 PB of science ready data products per year. These will be made available to the astronomical community through a worldwide network of SKA Regional Centres (SRCs), which will be organised by local communities that bring together radio astronomy institutes and compute centres.

Science analysis platforms provide scientists with an interface to software and workflows, data and processing hardware in a uniform way. Generally an interface through a browser (e.g. using Jupyter notebooks) and APIs for programmatic access. The future SKA science analysis platform provided by the SRCs should allow complex distributed analysis and data exploitation including science pipelines, machine learning and other advanced techniques working on one of the most significant existing science data lakes and using a complex federated computational environment. This system should not be only performant and efficient but, also, these complex features should be presented to the community in an easy way. In preparation for this, the SKA Regional Centre Steering Committee (SRCSC) has initiated several prototyping activities to investigate how currently existing tooling from other instruments and fields could be leveraged to provide the infrastructure within which the SRCs will operate. In this contribution we present the work of the prototyping team focused on development of the SKA science analysis platform, which operatess under the name “Team Tangerine”.

Every field, and even every instrument within a field, is different, and therefore the definition of what a science analysis platform is and does cannot be defined in a uniform way. Because of this there are also many resources which either describe existing platforms, or more theoretically discuss what they typically should support. The first task of the Tangerine team is therefore to draft a vision of what this term means in the SKA context, based on literature study and the requirements from of the SKA. In parallel we make a qualitative comparison of the main platforms known to us and assess how an SKA platform could look, based on currently existing platforms or components. In this contribution we aim to present the vision on the SKA science analysis platform.