As a data science and machine learning advocacy intern, you’ll research various data science and machine learning techniques and algorithms, and use this research to create small teaching projects in PyCharm. These projects will ideally showcase features in PyCharm that make the work of data scientists easier. 

You will then learn how to present these projects in content which is fun, engaging, and aimed at our target audience of Python data scientists. This role is perfect for someone who has some background in data science, and who enjoys teaching and sharing knowledge about this area in an accessible way.

Project scope includes:

• Project creation: find suitable datasets to showcase specific topics in data science or machine learning, and use these to put together short tutorials.

• Text content creation: write blog posts about these tutorials, showcasing how they can be implemented in PyCharm.

• Assistance with video creation: help write scripts and create screencasts for shorts and short videos about these tutorials.

Your responsibilities will be:

• Research and develop tutorials about topics in data science and machine learning that can be well showcased in PyCharm.

• Write clear, engaging technical blog posts about your work.

• Collaborate with developer advocates to create videos and shorts.

By the end of the internship, you will:

• Gain hands-on experience with data science and machine learning topics in Python.

• Learn the ins and outs of PyCharm.

• Build a public portfolio of demo projects and content.

• Learn effective developer advocacy practices: writing, video content, and teaching.

• Understand how to communicate complex technical topics in an accessible way.

• Experience the impact of developer advocacy in a real-world ecosystem.

• Programming experience in Python, and familiarity with tools such as Jupyter notebooks, databases, and Git/GitHub. 

• Background in data science, with some experience doing data analysis, statistics and/or machine learning. This includes small personal projects or coursework.

• Strong written communication skills; ability to explain technical concepts clearly.

• Curiosity and enthusiasm for learning new things and experimenting. Work in data science is rarely deterministic, so being willing to explore and try things out is a big plus.

• Collaborative mindset – open to feedback and eager to improve.