Machine Learning Engineer / Data Scientist – Geospatial Data

Our requirements

• Strong programming skills, with extensive commercial experience in Python
• Proven professional experience in Machine Learning and Data Science
• Experience with ML and data science libraries/frameworks, including PyTorch, TensorFlow, Keras, Scikit-learn, NumPy, and Pandas
• Strong analytical and problem-solving skills
• Experience taking ML solutions from experimentation to production
• Ability to work effectively with existing software and data systems
• Strong ownership mindset and ability to independently drive tasks to completion
• Good written and verbal communication skills
• Ability to work effectively in a fast-paced, collaborative environment
• Willingness to relocate to Malmö, Sweden for the duration of the project and be available for occasional on-site work
• Hands-on experience with ML techniques such as: classification clustering feature engineering anomaly detection neural networks
• Solid understanding of classical machine learning algorithms, such as: Support Vector Machines (SVM) Random Forest Naive Bayes k-Nearest Neighbors (k-NN)

Optional

• Commercial experience with Scala or Java
• Knowledge of Natural Language Processing (NLP)
• Experience with Information Retrieval
• Experience in Data Mining
• Background in statistical modelling
• Experience building predictive models
• Experience working with large-scale or geospatial datasets

Your responsibilities

• Design, develop, test, and deploy machine learning models
• Develop solutions for large-scale geospatial data processing and quality improvement
• Apply machine learning and data science techniques to identify patterns, generate insights, and detect anomalies
• Perform feature engineering and develop predictive and classification models
• Work with NLP and LLM-based solutions to extract and structure information from data
• Design and implement prompting strategies and agent-based workflows
• Build and maintain scalable data processing and software solutions
• Take ownership of ML features and improvements throughout the entire development lifecycle
• Integrate machine learning models into existing production systems
• Support model productionization, deployment, monitoring, and further improvement
• Collaborate with data scientists, software engineers, and other cross-functional stakeholders
• Balance experimentation and innovation with practical implementation within existing systems

About the project

This is how we organize our work

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