We provide price optimisation solutions in close collaboration with a major UK retailer's data science team. Together, we build projects to enable quick exploration and productization of the ML models and respective optimisation algorithms in a hybrid-cloud environment. The end goal is to provide APIs for the optimiser to solve pricing class problems across multiple business domains.

We:
Use pySpark on Hadoop to load and transform Big Data to produce smaller, significant features used in modelling and analytics.
Provide the hybrid-cloud environment to support model development from beginning to full productization.
Self-dependently select the best architectural patterns to solve business problems.
Build robust code in the cloud and on-prem to bring models fast and reliably to production
Build clear DevOps cultures and solutions for reproducible patterns in multiple business domains.

Python (3.6+)
ETL: pySpark, Oozie & Airflow
Platform: Hadoop & Azure (incl. Azure ML)
CI/CD: Jenkins & Azure DevOps
IAC: Terraform
Git @ Github

There are many pricing problems in the global retailer world: many of these problems share similar structures and constraints. Due to this, building robust solutions reusable across multiple challenges, leveraging on-prem cluster and cloud environments and ensuring the top quality while maintaining quick iterations is our bread and butter.

The core engineering team consists of 4-5 people in Poland. We collaborate closely with the client’s product management and data science team. Additionally, we collaborate with engineers that work for the client directly.