Apple is looking to solve business problems by building sophisticated, scalable and robust architecture, tools, data products, and critical data pipelines that are optimized for rapid business intelligence, data analysis, and data science. They aim to use data analysis and predictive modeling techniques to define, build, deploy, and maintain end-to-end operational solutions that have a direct and measurable impact to the company and our customers.
Requirements
- Proficient in at least one programming language (Python/Scala/Java preferred)
- Practical experience (gained through work/classroom/personal projects) with querying language such as SQL and extracting insights from complex data sets
- Theoretical understanding of data modeling concepts, data structures, and machine learning algorithms
- Experience implementing machine learning solutions to answer real-world questions
- Experience with big data processing technology (MapReduce/Spark/S3/Data Lake)
Responsibilities
- Translate ambiguous business problems into technical solutions by working collaboratively with business partners to design, develop, deploy, and maintain data science-driven solutions that drive key business decisions
- Create tools to support self-service analytics, geospatial analytics, automated disaster recovery, self-healing data engineering pipelines, real time reporting, and decision support systems
- Develop, deploy, and operationally support machine-learning models that take real-time and forensic action against a variety of threats to Apple’s ecosystem
- Apply data science to core finance processes to automate and elevate capability, forecasting, accruals, accounting entries prep, IA process, risk mitigation, etc.
Other
- Bachelors or Masters in Data Science, Computer Science, or related Engineering field
- Prior internship experience in Data Science or related field
- Thrives in a fast-paced environment
- Effective communication to translate complex, ambiguous concepts into concise, business-focused solutions
- Standout colleague, meticulous, and a self-starter
