Building predictive models: Using your in-house data we build and validate machine learning models, combined with external data sources and deployed at scale.
We transform your existing code base (yes - even Excel Sheets) and transform it into high-quality data products implemented as R- or Python packages. Easy to maintain, deploy and well documented.
We integrate a high-performance data science infrastructure within your company which makes most out of existing open-source tools like R- and Python, facilitates collaboration and is based on solid ground - either in the cloud (AWS, Azure, GCloud) or on-premises (Kubernetes).
Refactoring of an R-Shiny dashboard, implementation of new features and interactive forms. Implementation as an easy-to-maintain R-package and support for continous integration and deployment within a docker container.
Refactoring of the Uniqa PIM market risk model codes to an R-package. Implementation of a unit-test-suite to ensure code quality and correctness of calculations.
Implementation of a contiuous integration code pipeline for the market risk modelling packages. Setup and integration of the Jenkins build server and R package repositories for end users.
We are constantly extending the border of what is possible with our team, using modern open-source technologies and rock-solid infrastructure — on-premises or in the cloud.