Data Science

Turning data into gold

Our Prague data science group consists of an Advanced Analytics as well as a Data Management and Visualization team. We’re part of a larger global team that includes colleagues in the U.S. and Singapore who work closely with us. This multinational team has experienced data scientists working full-time, as well as student interns.

The diverse capabilities of our team are used for projects throughout our business in manufacturing, finance, research, human and animal health. We partner with internal clients to clients to analyze internal and external datasets that provide insights and data science tools.

Our Advanced Analytics team uses R, Python, Spark, Matlab, and SAS to perform statistical modeling, econometrics, machine learning, natural language processing, simulations, optimizations, and computational fluid dynamics.

The Data Management and Visualization team uses Tibco Spotfire, Tableau, Microsoft SQL Server, Oracle, Teradata, Pipeline pilot, Informatica, AWS, and IBM Cognos to map, model, and transform data into reports.

Our DevOps stack that includes Confluence, Jira, Bitbucket, and Jenkins is used by both teams for collaboration and automation.

Our colleagues come from more than 10 countries and have a lot of experience from different European countries:

  • Charles University, Czech Republic
  • Czech Technical University, Prague, Czech Republic
  • Center for Economic Research and Graduate Education, Prague, Czech Republic
  • University of Chemistry and Technology, Czech Republic
  • University of Economics in Prague, Czech Republic
  • Palacky University Olomouc, Czech Republic
  • Vilnius University, Lithuania
  • University of Thesaly, Volos, Greece
  • University of Cambridge, UK
  • Universite Paris Descartes, France
  • EPF Graduate School of Engineering, Sceaux, France
  • University of Amsterdam, Netherlands

Data Management and Visualization

Here are a few examples of the types of projects our team works on:

Cross-team data analytics project

Multi-year analytics initiatives usually require several IT teams to collaborate. Collaborating with our global teams on these large projects also provides great opportunities to get hands on experience working with enterprise technologies and complex BI architectures.

We contribute to the following activities:

  • Analyzing business needs, data requirements, and data sources
  • Modelling data: database design, and defining database layer mapping
  • Developing reports: creating dashboards and visualizations using the latest reporting technologies


  • Automated forecasting platform for global finance released in three top markets
  • Data platform with an analytical application package on top of it to provide deeper and better insights into the vaccines market
  • Single integrated data warehouse for IT infrastructure reporting with front end dashboards

Collaboration with internal teams

We created an extension for the BI tool, Spotfire, that generates the required project documentation automatically with a single click. This significantly reduces the amount of time spent on producing the process documentation required for BI projects.

Medium & Small Size Reporting Project

Delivering one or more dashboards with the provided data sources usually only takes one or two colleagues from our team. These projects are typically finished within a few months or less.

Achieving these results involves the following activities:

  • Collaborating with business stakeholders to analyze their needs
  • Developing and designing reports, then iterate on them with business stakeholders
  • Defining small ETL transactions within the dashboard itself

Smaller projects usually offer the freedom to design and influence final products as well as the project itself. Some small projects have even exceeded expectations enough to evolve into more complex efforts that require more data sources, complex transformations, and advanced analytical models.

Examples of small dashboards:

  • Description of individual manufacturing steps and time variations  for each of those steps. Identifying high impact opportunities for production process improvement are planned for the future.
  • Description of the  cybersecurity risk associated with each company system
  • Comparison of sales performance against competitors