Apple: Data Science Engineer
Seeking a Data Science Engineer to lead the design and implementation of systems and tools to support the fraud prevention efforts of Analytic Insight.
Location: Austin, TX
Position: Data Science Engineer
This job is closed.
Apple’s Analytic Insight team is responsible for mitigating fraud, waste and abuse company-wide. AI Data Science Engineering is building an environment to enable cutting-edge data analysis over Petabytes of data. We work side-by-side with data scientists and implement scalable, easy-to-use systems and tools. We are seeking a customer-oriented, passionate and driven Data Science Engineer with experience in building big data systems. Must be able to lead the design and the implementation of a big data analytics system.
- Mastery of one of C++, Python, Java, Scala or equivalent language
- Experience with Relational databases and NoSQL databases
- Hands on experience with Hadoop, Spark, Hive/Pig, HBase
- Solid understanding of the full software development lifecycle
- Excellent problem solving, critical thinking, and communication skills
- Strong ability to evaluate and apply new technologies in a short time
- Experience in building data science or data analysis tools a plus
- Machine learning background a plus
- Familiar with Agile software development process, Test-Driven development and Continuous Integration a plus
You will lead the design and implementation of systems and tools to support the fraud prevention efforts of Analytic Insight. The job will include:
- Working with infrastructure teams to design and implement the Big Data data warehouse and analytics platform
- Design and implement proof-of-concept solutions for new technologies and machine learning algorithms
- Develop new tools, capabilities and infrastructure to improve how data scientists conduct data analysis and model research
The candidate is expected to be self-motivated, pro-active and solution-oriented individual.
BS degree in computer science or equivalent field plus 7-10 years experience, or equivalent.