Summary

Summary

Posted: Feb 24, 2021

Weekly Hours: 40

Role Number:200146617

Imagine what you could do here. At Apple, new ideas have a way of becoming outstanding products, services, and ...Summary

Summary

Posted: Feb 24, 2021

Weekly Hours: 40

Role Number:200146617

Imagine what you could do here. At Apple, new ideas have a way of becoming outstanding products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.

Apple's Strategic Data Solutions (SDS) team is looking for a talented individual who is passionate about crafting, implementing, and operating analytical solutions that have direct and measurable impact to Apple and its customers.

As an SDS data scientist, you will employ predictive modeling and statistical analysis techniques to build end-to-end solutions for improving security, fraud prevention, and operational efficiency across the company, from manufacturing to fulfillment to apps and services.

Apple's dedication to customer privacy, the adversarial nature of fraud, and the enormous scale of the business present exciting challenges to traditional machine learning and data science techniques. On this team, you will push the limits of existing data science methods while delivering tangible business value.

Key Qualifications

• Practical experience with and theoretical understanding of algorithms for classification, regression, clustering, and anomaly detection
• Working knowledge of relational databases, including SQL, and large-scale distributed systems such as Hadoop and Spark
• Ability to implement data science pipelines and applications in a general programming language such as Python, Scala, or Java
• Ability to comprehend and debug complex systems integrations spanning toolchains and teams
• Ability to extract meaningful business insights from data and identify the stories behind the patterns
• Excellent presentation skills, distilling complex analysis and concepts into concise business-focused takeaways
• Creativity to engineer novel features and signals, and to push beyond current tools and approaches

Description
Engage with business teams to find opportunities, understand requirements, and translate those requirements into technical solutions
Design data science approach, applying tried-and-true techniques or developing custom algorithms as needed by the business problem
Collaborate with data engineers and platform architects to implement robust production real-time and batch decisioning solutions
Ensure operational and business metric health by monitoring production decision points
Investigate adversarial trends, identify behavior patterns, and respond with agile logic changes
Communicate results of analyses to business partners and executives
Research new technologies and methods across data science, data engineering, and data visualization to improve the technical capabilities of the team

Education & Experience
Ph.D. in Computer Science, Machine Learning, Statistics, Operations Research or related field; or
Ph.D. in Math, Engineering, Economics, or hard science with data science fellowship; or
M.S. in related field with 3+ years experience applying data science to real business problems

Additional Requirements