Data Analyst in Environmental Sciences

at  Aarhus Universitet

What if you could use your data science expertise to tackle environmental challenges and shape sustainable solutions for the future? Do you thrive in dynamic, interdisciplinary teams, and would you like to work on innovative projects that significantly impact pollution legislation and environmental protection? And what if your expertise were not only applied in research but also in advising government ministries and key stakeholders to make data-driven decisions that safeguard our environment? If this sounds like you, we would love to hear from you!
The Department of Environmental Science at Aarhus University seeks to advance its research and advisory programs by focusing on large environmental data trends, pollutants, and chemometrics, aiming to address key global challenges. The academic employee position in environmental data science is crucial to this strategy, as the role will enhance our ability to analyse and interpret complex environmental data. The position is to be filled as soon as possible.

QUALIFICATIONS

The ideal candidate possesses the following qualifications:

• A MSc or PhD in computational chemistry, statistics, mathematics, computer science, or a related field
• Advanced knowledge of Python and its scientific libraries and data stack (e.g., NumPy, pandas, SciPy, scikit-learn, PyTorch)
• Proficiency in statistics and machine learning, including the ability to use statistical techniques to analyse data, build predictive models, and evaluate their performance
• Software engineering skill, including documentation, testing, and CI/CD practices
• Experience in, or motivation to learn, the design, implementation, and deployment of machine learning models
• Willingness to learn new topics in both technical (data science) and domain-specific areas (chemical and environmental science)
• Curious, proactive, motivated, and capable of working independently as well as collaboratively in teams
• Experience in, and willingness to, supervise and train other team members and colleagues
• Strong communication skills, with the ability to guide and inspire non-technical audiences
• Excellent written and spoken English

Also desirable:

• Experience in processing, analyzing, and interpreting chemical, biological, or environmental datasets
• Domain knowledge in environmental chemistry (e.g. non-target screening, mass spectrometry) and/or omics (e.g., genomics, transcriptomics, proteomics, metabolomics)
• Experience with cloud environments (e.g., Microsoft Azure)
• Knowledge of databases and data standardization (e.g., SQL, NoSQL, ontologies)
• Proficiency in additional programming languages is an advantage

You will participate in both research projects and advisory projects, the latter mainly for the Danish ministry of Environment. Both research and advisory projects aim to understand the fate and dynamics of environmental pollutants, helping ministries and environmental agencies regulate chemicals and protect our environment. You will work alongside a dynamic, multidisciplinary team of laboratory technicians, students, and researchers, making collaboration a key part of your role.
Your main task will be to develop offline and online data treatment tools and resources for the Environmental Chemistry and Toxicology section. Depending on your interests, you could also explore data management, curation and analysis, GIS and sharing educational material. Your expertise in prediction-focused data science methods will be essential in solving chemometric tasks and analysing large monitoring programs, directly contributing to solving real-world environmental issues.
In your role, you will design and implement cutting-edge data science solutions, developing chemometric and bioinformatic pipelines to process, analyse and interpret multi-modal data (e.g., omics, images, etc.). You will also have the opportunity to supervise students, collaborate with various research groups, and shape the Department’s data science strategy. Plus, you will take part in department-wide initiatives such as workshops, presentations and events related to data science.