Building ML-driven Predictive Maintenance on industrial equipment – an A/B test
This internship will be about building a Predictive Maintenance set-up on sensor data and testing it in A/B test with the existing algorithmic approaches in place.
About these internships
element61 works with various customers in the field of Industry 4.0. Sensor data & continuous data feeds share the status of machines, their equipment and the production process. Objective of predictive maintenance aims to identify machine/part/equipment of full-production-line failure before it happens.
Goal of this internship
This internship will be about building a Predictive Maintenance set-up and testing it in A/B test with the existing algorithmic approaches in place. We will work on a standalone predictive maintenance dataset and aim to challenge the status-quo and the performance of the as-is development algorithm. This internship will be an opportunity to build an end-to-end approach, methodology & leveraging the expertise & previous work of element61. The internship will require to first understand the industrial process, then research and aimed to build an end-to-end Proof of Concept.
In essence of time this internship won’t focus on the data engineering but rather deep dive on the Machine Learning.
As an intern, we expect you to:
- Translate your academic knowledge into business solutions & a first hands-on experience;
- Do data development using Python, SQL in Azure and Databricks;
- Show your documentation and reporting skills through presentations and demo’s;
- Show creativity and out-of-the-box thinking in this challenging use-case.
What are we looking for:
- Character: customer-oriented, able to work in a team, keen to create something new in international teams;
- Working Practice: analytical, structured and result-orientated;
- Passionate about analytics, machine learning technology & applications and eager to learn;
- Enthusiasm to be part of a growing team & industry.
Language: working level English
Interested to find out more ? Send us your profile and motivation at email@example.com