Data Scientist Intern, Growth Data Science
- San Francisco, CA
As a Data Science Intern within the Growth Data Science team, you will work on projects that help with Square’s growth. The team exists to surface the right messages to the right sellers at the right time across all our go-to-market channels (web app, in-app, notifications, sms, email, mail, etc) as well as products. We provide sellers with remarkable experiences using machine learning/deep learning to power the best product/feature/content recommendations.
Our algorithms derive value from our unique, rich, and rapidly growing data. We partner with business, product, operations and engineering teams to drive better decisions, automated and human, using sophisticated modeling and machine learning. We’re a passionate team of hackers, statisticians, and optimizers who are resourceful in distilling questions, wrangling data, and driving impactful business decisions.
For this 12 week summer internship, you will:
- Collaborate with the Growth Data Science team’s cross-functional business partners to build remarkable machine learning and analytics solutions for our go-to-market channels
- Take ownership of data science projects from beginning to end: frame and structure questions, collect and analyze data, research, prototype, and build out data science pipelines and models in production, as well as summarize and present methodology and key insights
- Use and learn a diverse set of techniques spanning machine learning (including deep learning), causal inference, and other forms of statistical modeling to solve important business and product problems.
- Help build the next generation of data products at Square
- Are pursuing a MS or PhD in Economics, Statistics, Computer Science, Mathematics, or related technical field
- Hands on experience in executing data analysis, preferably using SQL and Python
- Familiarity with machine learning concepts
- Ability to understand complex business and data systems
- Ability to clearly communicate complex results to technical and non-technical audiences Versatility and willingness to learn new technologies on the job
- Programming experience in one or more object-oriented languages, including: Python, C, C++, Java, Ruby, Scala, and Go.
- Familiarity with Linux/OS X command line, version control software (git), and general software development
- Experience in programming or scripting to enable ETL development
- Hands-on experience with machine learning (e.g. regression, ensemble methods, etc.)
- Familiarity with relational and analytical databases such as PostgreSQL or Snowflake
- Previous industry experience or internships in analytics or data science
- Independent research experience
Technologies we use and teach:
Python (numpy, pandas, sklearn), Snowflake (SQL), Looker (data visualization & reporting)