Applied Scientist Intern

What you'll do::
You will work embedded in the POI team on concrete research and engineering tasks, with guidance from senior engineers/scientists. You will:

• Explore and experiment with ML/AI approaches to solve POI-domain problems such as entity matching, address parsing, data quality assessment, or coverage analysis

• Implement and evaluate models and algorithmic solutions on real-world, large-scale geospatial datasets

• Design and run experiments, analyze results, and translate findings into clear insights, recommendations and implementation

• Be part of the development of data pipelines and tooling that support model training, evaluation, and analysis

• Collaborate with Applied Scientists, Engineers, and Product stakeholders to understand requirements and integrate your work into the broader team workflow

• Document experiments, methodologies, and results clearly to support knowledge sharing within the team

What you'll need: :
• Currently enrolled in a Master's programme in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field

• Solid grounding in machine learning fundamentals — supervised/unsupervised learning, model evaluation, feature engineering

• Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn (from coursework, research, or personal projects)

• Programming proficiency in Python; experience with data manipulation libraries (pandas, NumPy, Spark is a plus)

• Familiarity with NLP or embedding-based methods (e.g., Sentence Transformers, BERT-based models) is a strong plus

• Interest in geospatial data, POI systems, addressing, or location intelligence

• Analytical mindset with the ability to design experiments, interpret results critically, and communicate findings clearly

• Collaborative and curious — comfortable asking questions, working iteratively, and learning from feedback

What you'll learn::
By the end of the internship you will have:

• Worked on production-scale geospatial and POI data with real business impact

• Gained experience in the full ML experimentation cycle - from problem framing and data analysis to model development and evaluation

• Deepened your understanding of applied ML in a domain where data quality, scale, and semantic complexity are central challenges

• Collaborated in a cross-functional team of scientists, engineers, and product managers