Jakub Stech

Data Science Architect, DataSentics a.s.

Jakub is a data science architect at DataSentics, a European machine learning and cloud data engineering boutique. His key area of expertise is bringing data science solution for business use case based on 360° view on the customer, e.g. CRM campaings interaction data and digital customer interaction data such as ad impressions, to understand customer needs and personalise his experience using machine learning approaches.

Past sessions

Ceska sporitelna is one of the largest banks in Central Europe and one it's main goals is to improve the customer experience by weaving together the digital and traditional banking approach. The talk will focus on the real world (both technical and enterprise) challenges during shifting the vision from powerpoint slides into production: Implementing Spark and Databricks-centric analytics platform in the Azure cloud combined with a on-prem data lake in the EU-regulated financial environment Forming a new team focused on solving use cases on top of C360 in the 10 000+ employee enterprise Demonstrating this effort on real use cases such as client risk scoring using both offline and online data Spark and its MLlib as an enabler for employing hundreds of millions of client interactions personalized omni-channel CRM campaigns

Speakers: Jakub Stech and Veronika Pješčaková

Ceska sporitelna is one of the largest banks in Central Europe and one it’s main goals is to improve the customer experience by weaving together the digital and traditional banking approach. The talk will focus on the story of how in order to reach this goal Ceska Sporitelna created a new team focused on building use cases on top of a combined digital and offline customer engagement 360 powered by a Spark and Databricks-centric agile advanced analytics platform in the Azure cloud combined with a on-prem data lake. This talk will cover:

  • The customer engagement 360 vision powered by machine learning and the cloud
  • Deep dive into the use case of optimizing and personalizing programmatic ad buying on the individual user and ad placement level thanks to Spark MLLib and NLP on top of hundreds of millions of ad interaction data
  • Deep dive into the use case of supporting the seamless transition of the customer journey from digital to traditional offline channels
  • The approach to building the agile analytics platform and experience of adopting the cloud in a EU-regulated financial institution