Data scientist at the department of Catchment and Urban Hydrology


Delft, Netherlands 🇳🇱

Use your big data skills to have societal impact by starting this job as a:

Data scientist at the department of Catchment and Urban Hydrology

The unit Inland Water Systems has over 175 employees, divided into the following six departments: Operational Water Management and Early Warning, Catchment and Urban Hydrology, Flood Risk Management, River Dynamics and Inland Shipping, Fresh Water Ecology and Water Quality and Water and Delta Management.

The Department of Catchment and Urban Hydrology is involved in a wide range of projects. In the Netherlands we work for Rijkswaterstaat and the Water Boards. Internationally, we are active in projects for the World Bank Group and the Asian Development Bank, e.g. in South East Asia, South-America and Africa. Furthermore, we participate in H2020 projects for the European Union. We develop new approaches and tools for hydrology at different levels: from the global and river basin scale to the level of districts and roads. The Department currently consists of 25 hydrologists, one third of the team holds a PhD. A research and development and a customer-oriented attitude are equally important to us. The Department is located in Delft, the Netherlands.

Our customers are asking for fast and accurate modeling solutions where we have to deal with data rich and data poor environments. The model results are needed for flood forecasting, river management and climate change adaptation strategies. The Department of Catchment and Urban hydrology is developing such fast modelling approaches for both our hydraulic and hydrological modeling. One possibility is to apply machine learning techniques, such as neural nets, to improve our process-based models. We want to develop skills in this field where there is still little experience.

What can you expect as a Data Scientist?

  • Apply different data analysis techniques in our projects;
  • Develop new methods for use of big data in hydrology (in the broadest sense), including EO data;
  • Expand on the field of applied data, including new types of social media.

What do we expect from you as a Data Scientist?

  • A MSc, in data science or environmental sciences, with proven experience on (big) data analysis;
  • Preferably a PhD, on data science or the environmental sciences;
  • 0-4 years of experience;
  • Entrepreneurial skills to develop R&D proposals, including financing;
  • Communication skills to co-operate with Deltares colleagues and partners;
  • You do not shy away from self-reflection.

Working at Deltares
We offer an informal, dynamic and challenging working environment. Your personal development and building on your resume is important to us. Therefore we offer excellent training opportunities and future perspective.

Our terms of employment are competitive with a good pension scheme and 34 days of leave. We have special facilities on our campus, like daily lunch lectures, the Delta Flume and our data-lab (iD-lab).

Deltares is an independent institute for applied research in the field of water, subsurface and infrastructure. Worldwide we work on smart solutions, innovations and applications for people, environment and society. To achieve our goals, we develop our own expertise, innovative software and services.

Deltares supports governments, industries, water boards, and municipalities in their ambitions to reduce water and energy consumption and developing and implementing sustainable energy technologies. Deltares places high demands on the quality of its knowledge and advice. Enabling Delta life is our core business.

Are you interested in this job and do you fit the job qualifications? Then apply directly via the apply button and upload your CV and your motivation letter, before May 26th, 2018.

For more information on the content of this vacancy you may contact Lucas Janssen, Head of department Catchment and Urban Hydrology ([email protected] of +31 6 51901895) or Melissa Ruts, HR Recruitment Officer ([email protected] of +31 (0)88-335 83 30).