Want to work at an innovative, young yet world famous data & software company? Datenna brings mission-critical insights to customers around the world by collecting & analysing huge quantities of data and identifying patterns and connections within that data.
Do you feel right at home collecting (scraping) data from various types of sources? If you welcome a challenge and don’t give up until the last bit of data is in, then apply by sending your resume to email@example.com!
Datenna has a unique dataset containing more than 100 million entities in the Chinese industry and innovation landscape. We offer valuable insights into this data through our interactive front-end application. To be able to do this, Datenna uncovers all sorts of patterns and relationships in data using the latest AI, ML and Data Engineering techniques and technologies.
- You utilize all of your knowledge, intelligence and creativity to collect as much valuable data from public sources as possible.
- You develop scrapers using our scraper code template, in order to hand the data to our data pipeline in a standardised form.
- (Optional) You help improve our scraper code template based on new insights.
- (Optional) You help identify new data sources.
- (Optional) You use NLP techniques to extract specific knowledge from freeform text.
Who are you
- You are studying AI/ML/Data Science/Data Engineering or something similar.
- You are looking to apply your knowledge within an interesting company and gain valuable experience.
- You have experience with programming in Python, also outside of Jupyter Notebook.
- You welcome a challenge and don’t give up until the task is done.
- Experience with web scraping
- Experience with programming in Python
- Available at least 12 hours a week (flexible, from home)
- Knowledge of NLP/information extraction techniques
- Experience with building data processing pipelines
What we offer you
- Valuable hands-on experience with a highly interesting real-life use-case for data science
- A paycheck of 17,50 per hour
- Last but not least: help build something that is world famous: WSJ