Machine Learning (40 uur p/w)
Who are we
Cohelion is an ambitious data company based in lively Rotterdam and sunny Valencia. We have been in business for almost 20 years. Ever since that day, Cohelion has been creating data integration, data management and big data solutions for organizations.
With a dedicated team we work on a high-end generic data platform, currently in use by various organizations; among them multinationals with hundreds of offices worldwide.
All our colleagues are individually skilled, highly motivated team players and passionate to create simple and user-friendly data solutions that empower organizations with valuable insight and information.
Background of the assignment
Cohelion provides data integration services to large multinational organizations. One of those is a global air cargo handler that operates in over 300 airports worldwide. 5 years ago we had created a ML algorithm to create forecasts of operational tonnage volumes per station/customer/month. This was made using 8+ years of detailed historical data. A Python script was created by an experienced ML expert that has run since then.
Quite a lot has changed since then:
- More data is available
- The data quality has improved
- Additional requirements have come in, for example to also take regional holidays into account
- Azure Machine Learning has matured
- And perhaps most important: the recent COVID pandemic has distorted many trends in historical data
All of the above makes that a review/update is needed of the current algorithm that takes the above into account.
We do see a possibility this assignment/project can be done within a project group of 2 students.
We expect from this assignment:
- A new algorithm that performs better than the existing one (better means: forecast is
closer to actuals)
- Ideally it would use Azure Machine Learning services
- Sharing knowledge within our organization
The following experience is recommended
- Experience with time-series forecasting (RNN or otherwise)
- Experience with Azure ML stack (no requirement, but a bonus)
We can provide the following
- Large, realistic (but anonymized) data sets
- Current forecasts as benchmark
- a very skilled and “gezellig” team to work with
- beautiful views on the Maas river
- healthy lunch and regular afternoon drinks
- a fair internship compensation