Active learning cockpit for managers with (unsupervised) anomaly detection

The dream for every (process) manager is to have a cockpit that monitors the processes in her supervision and that notifies when an incident happens. Just like when an airplane pilot gets notified when the plane is approaching the ground at high speeds for example.

Lots of big companies with big processes use software to register all the steps that happen in a process. They often use SAP for that.

In this internship, you will

  • start with talking to a process manager at a client of ours and discover which processes they run and what they would like to see in their cockpit.
  • Then you will dive into their enterprise software (SAP) to extract the relevant data and events.
  • Using that data, you will explore machine learning algorithms (unsupervised or supervised) and process mining techniques that are able to detect the relevant metrics and events that will end up in the cockpit dashboard.
  • If time (and interest) allows, you will then work on the frontend of the dashboard itself to shape the manager’s experience. The cherry on the cake is that the dashboard allows input from the end user s.t. it functions as a labeling tool s.t. the entire setup can become an active learning system.

Profile / Required skills

  • Strong analytical abilities, knowledge of different statistical methods, not scared by mathematics and a familiarity with research studies.
  • Familiarity with statistical analysis languages and tools like Python, SQL.
  • Excellent verbal and written communication in English.
  • You are currently pursuing a degree in computer science or related field.

Internship Duration

The duration of the internship can be flexible and depends on the candidate preference and the project requirements. The typical duration is 6 to 8 weeks.