Machine Learning Engineer / Data Scientist

Urbanvind is a fast-growing startup developing innovative solutions for public transport analytics. Our solutions help cities and transport operators better understand passenger flow. Our core product, CrowdFlow, uses Bluetooth and WiFi sniffing with machine learning to provide real-time passenger counts and Origin-Destination insights, aiming to replace expensive traditional hardware. Urbanvind was started by EIT Urban Mobility graduates and is funded by EIT Urban Mobility. Our team is focused on using data and AI to improve urban mobility. We are in the process of closing a funding round which will support our expansion.

www.urbanvind.com

We are looking for a Machine Learning Engineer / Data Scientist to join our core team. Your primary focus will be on enhancing and refining the algorithms for our CrowdFlow product, improving its accuracy, and extracting insights from our datasets. You will work with real-world mobility data to solve complex problems. For students, there is potential for this role to support a Master's thesis. You will be a core member of our team, expected to contribute, challenge, and grow with us, not just fulfill an intern's tasks.

Master's student (with potential for a thesis) or a recent Master's graduate in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field.

Strong theoretical background and practical experience in machine learning, statistical modeling, and data analysis.

High proficiency in Python is essential.

Demonstrable experience in data science projects (academic or industry), including data cleaning, feature engineering, model development, and validation.

Experience with common data science libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch).

Ability to work with complex datasets and derive actionable insights.

A proactive problem-solver who can independently research and implement new techniques.

Comfortable challenging existing ideas and proposing new solutions.

Good communication skills.

Role Focus: Directly contribute to improving the core algorithms of CrowdFlow, aiming to make our passenger analytics more accurate and insightful.

Thesis Opportunity: For Master's students, this position offers an opportunity to conduct research for your thesis using real-world data.

AI-First Culture: At Urbanvind, we employ an AI-first approach. We focus on building functional solutions efficiently, using AI to enhance and accelerate our development. We value practical, working solutions and rapid iteration.

Remote Flexibility with a Hub: This is a remote position. We also offer access to our office at the UtrechtInc incubator in the Netherlands for collaboration.

Growth Potential: With our upcoming funding and growth, this role offers a significant opportunity for early team members to take on substantial responsibilities and influence the company's technical direction. High performers will be in a strong position to shape their roles and contribute as key members of our engineering team as we scale.

Learning Environment: We offer a dynamic environment to learn and develop your skills quickly, working on current technology and applied problems.

Team & Culture: Join an ambitious team. We value innovation, proactivity, and a practical approach to problem-solving.

About Us: Urbanvind is developing CrowdFlow to offer a more accessible and insightful way for transport operators to understand passenger movement. Our goal is to enhance public transport efficiency through better data.