Manager, Data Science at Apple
Apple's Strategic Data Solutions (SDS) team is looking for a talented manager who is passionate about leading a team of data scientists that craft, implement, and operate analytical solutions that have direct and measurable impact to Apple and its customers. You will build and lead a team of SDS data scientists, who 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.
- Practical experience with and theoretical understanding of algorithms for classification, regression, clustering, and anomaly detection
- Familiarity with database modeling and data warehousing principles and SQL.
- Familiarity with Big Data tools like Spark, Hive etc.
- Strong programming skills in Java, Python, or similar language
- 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
- Creativity to engineer novel features and signals, and to push beyond current tools and approaches
- 2+ years experience in hiring & leading team of data scientists.
- Ability to coach data scientists and a drive to invest in team’s success.
- Excellent presentation skills, distilling complex analysis and concepts into concise business-focused takeaways
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. or Masters in a quantitative field, such as Computer Science, Applied Mathematics, or Statistics, or equivalent professional experience.