The Company
Metropolis is an artificial intelligence company that uses computer vision technology to enable frictionless, checkout-free experiences in the real world. Today, we are reimagining parking to enable millions of consumers to just "drive in and drive out." We envision a future where people transact in the real world with a speed, ease and convenience that is unparalleled, even online. Tomorrow, we will power checkout-free experiences anywhere you go to make the everyday experiences of living, working and playing remarkable - giving us back our most valuable asset, time.
About the Role:
We are looking for a highly motivated Data Science Intern to join our team. This role offers an exciting opportunity to work on real-world data challenges, contribute to analytical projects, and gain hands-on experience with modern data science tools and techniques.
Key Responsibilities:
- Collect, clean, and analyze large datasets from various sources
- Build and test predictive models using machine learning techniques
- Assist in developing data visualizations and dashboards
- Work with the engineering and product teams to translate data insights into business actions
- Document methodologies and present findings clearly
Requirements:
- Currently pursuing or recently completed a degree in Computer Science, Statistics, Mathematics, or related field
- Strong understanding of data science concepts and statistical techniques
- Hands-on experience with Python, R, SQL, or similar tools
- Familiarity with libraries like pandas, scikit-learn, matplotlib, and seaborn
- Basic understanding of machine learning algorithms
- Good communication and problem-solving skills
When you join Metropolis, you’ll join a team of world-class product leaders and engineers, building an ecosystem of technologies at the intersection of parking, mobility, and real estate. Our goal is to build an inclusive culture where everyone has a voice and the best idea wins. You will play a key role in building and maintaining this culture as our organization grows.
