Machine Learning Engineer Data Scientist – Geospatial Data

• Strong programming skills, with extensive commercial experience in Python
• Proven professional experience in Machine Learning and Data Science
• Hands-on experience with ML techniques such as:
  • classification
  • clustering
  • feature engineering
  • anomaly detection
  • neural networks
• Practical experience working with LLMs, including prompting and agent-based workflows
• Solid understanding of classical machine learning algorithms, such as:
  • Support Vector Machines (SVM)
  • Random Forest
  • Naive Bayes
  • k-Nearest Neighbors (k-NN)
• 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

• 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

,[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] Requirements: Python, Machine learning, Data science, LLMs, Support Vector Machines, SVM, Random Forest, Naive Bayes, k-Nearest Neighbors (k-NN), PyTorch, TensorFlow, Keras, scikit-learn, NumPy, Pandas, Analytical skills, Problem-Solving, Communication skills, Scala, Java, Natural language processing, NLP, Information Retrieval, Data mining, Statistical modelling, predictive models