2026/06/30 – 2027/06/30
Job summary
Description
In this role, you will design and build machine learning systems and perform data analysis to improve the quality of large-scale geospatial datasets. You will develop both classical machine learning and NLP models to extract structured information, detect anomalies, and measure data quality.
You will be involved end-to-end—from experimentation and model development to integration, productionization, and deployment at scale. This role requires a strong combination of data science expertise and solid software engineering skills.
Responsibilities
Design, develop, and deploy machine learning models
Build and maintain scalable data and software solutions
Apply data science techniques to generate insights and improve data quality
Own and deliver features and improvements end-to-end
Collaborate with cross-functional teams to integrate models into production systems
Must-have Qualifications
Strong programming skills, with extensive experience in Python
Proven experience in machine learning and data science (e.g., classification, clustering, feature engineering, anomaly detection, neural networks)
Experience working with LLMs, including prompting and agent-based workflows
Solid understanding of classical ML algorithms (e.g., SVM, Random Forest, Naive Bayes, k-NN)
Experience with ML frameworks and libraries such as PyTorch, TensorFlow, Keras, Scikit-learn, NumPy, and Pandas
Strong analytical thinking and problem-solving skills
Ability to balance creativity with practical delivery in existing systems
Excellent written and verbal communication skills
Proactive, ownership-driven mindset with the ability to adapt and deliver in a fast-paced environment
Extra Merit Qualifications
Experience with Scala or Java
Knowledge in one or more of the following areas: NLP, information retrieval, data mining
Experience in statistical modeling and building predictive models
Assignment start: 2026-06-30
Remote work: No
Assignment duration: 1 year
Geographical region: Sweden\Skåne län, \Malmö (MALMÖ)
Reply no later than: 2026-06-30
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