Data Scientist- Data Cloud

AppleSan Diego, CA
74d

About The Position

Ready to work with large-scale data systems that generate invaluable insights for teams that develop Apple's operating systems? The systems required to efficiently build and manage that software produces billions of events per day and provides a unique opportunity to generate invaluable insights. Join an early-stage team that is building a modern analytics platform using an innovative approach to software engineering, ML, and cloud data management! We're seeking an exceptional Data Scientist to transform complex data into timely, valuable insights that influence decisions across Apple's Software Engineering (SWE) organization. In this role, you will join a team that is building an analytics platform and growing an internal community focused on unlocking the power of data. As part of this team, you will create new SQL models in dbt and Snowflake, optimize and extract new data sources using Python, and collaborate with a variety of Apple teams to provide actionable insights in tools like Streamlit and Tableau.

Requirements

  • Skilled at creating intuitive data visualizations in tools like Tableau, Streamlit, and Plotly that simplify complex insights
  • Practical experience with Snowflake
  • Fluency with frameworks like dbt, Pandas, SciPy, and PySpark
  • Track record of executing complex projects efficiently while following software engineering best practices
  • Exceptional communication skills with experience building complex analyses and narratives out of data

Responsibilities

  • Design and implement complex analytical models using dbt, SQL, and Python
  • Optimize and create data extraction capabilities and algorithms
  • Ensure data quality, governance, and security standards
  • Document and share methodologies and analytical best practices
  • Partner with engineers and the wider community on platform direction
  • Design intuitive and interactive data visualizations to communicate analytical results
  • Lead analytics projects through all phases, including defining investigations, exploring data, conducting analysis, interpreting, and presenting results to teams and leaders
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