Applied Data Scientist
Applied Data Scientist
Published | Deadline | Location |
---|---|---|
yesterday | 5 Oct | Wageningen |
Use your data skills in a wide variety of projects, contributing with your solutions to our mission ‘Better
Ships, Blue Oceans’. Join our team!
Job description
What are you going to do?
As a datascientist, you will develop and maintain data pipelines, using our wide variety of data sources. These originate from our model scale basin experiments, on-board measurements, simulator studies and output from time domain simulations and CFD computations. In your projects you are bridging the gap between the research outcome and the requested data driven applications by other MARIN departments. To achieve this, collaborating with colleagues and other internal stakeholders is key.
Other tasks are to develop methods to ensure the quality of our data sources and the outcome of predictions during its lifecycle. Furthermore you will be involved in the development of MARIN DS/AI packages coded in Python. Together with the team you will take care of internal software package releases for the MARIN DS/AI applications and advice internally about the deployment and proper use of these packages.
Specifications
- 32—34.6 hours per week
- Wageningen View on Google Maps
MARIN
Requirements
Who are we looking for?
You have completed an MSc degree in data science, computer science, mathematics or equivalent and have a strong affinity with physics and system architectures. You are familiar with the key aspects of data acquisition, data exploration, data cleaning, training, validation, prediction and presentation of results. You will be expected to strengthen the team with your skills to deploy and maintain machine learning models in production reliably and efficiently (MLOps) and you have a keen eye for relevant developments in the field of data science. In addition to coding skills in Python, ideally you also have experience with one or more of the following topics: database systems, lifecycle management, distributed computing and Linux. Initiating improvements and new developments comes naturally to you. You should also have a good command of the English language and be willing to learn Dutch if it is not your native language.
Conditions of employment
What do we offer?
- A stimulating environment which will allow you to develop while maintaining the freedom to choose your own path.
- An attractive salary and benefits package, including our annual profit sharing and excellent pension scheme.
- Lifecycle allowance of 5,55% of your gross monthly salary.
- 29 vacation days (fulltime) + 5 scheduled days between Christmas and New Year.
- Plenty of opportunities for your professional development and career advancement.
- Development through training courses, further education and coaching.
- Flexibility in working hours and a hybrid working arrangement, to support your work-life balance.
Employer
MARIN
Who are we?
MARIN is a globally recognized top institute in maritime research, based in Wageningen, the Netherlands. Our mission is ‘Better Ships, Blue Oceans’: we stand for clean, smart and safe shipping and sustainable use of the sea. We do this as an independent knowledge partner for the maritime sector, governments and society. We work with around 400 specialists in various fields who are proud of an open and collaborative culture in which you can explore your ideas and take initiative.
Department
Data science team
Who will be your colleagues?
You will join our growing Data science team, which is part of the Research & Development department. The total R&D department consists of around 50 researchers and software engineers. As data science team we conduct research, develop and maintain data driven methods for maritime operations. Data science and Artificial Intelligence are identified as key technologies in the MARIN strategy for the future. The data science programme is part of the R&D technology plan and consists of four sub-programmes: bringing cutting-edge machine learning methods to the maritime domain; data-driven autonomous decision making or decision support, e.g. reinforcement learning; combining first-principles methods with data-driven methods; addressing robustness and explainability of data-driven methods.
Additional information
Interested?
If you have any questions, please contact Eelco Frickel, Teamleader Data Science +31 (0) 6 39 27 85 93, [email protected] .
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