Machine Learning Engineer - Strategic Data Solutions (Compliance)

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 leads efforts to mitigate monetary losses, ensure financial crimes regulatory compliance, optimize business processes, and improve the customer journey across our programs. We are looking for a hardworking individual who is passionate about crafting, implementing, and operating analytical solutions that have a direct and measurable impact on Apple and its customers. As an SDS Machine Learning Engineer, you will design, build, and maintain the next generation of compliance services, consumed by both internal and external stakeholders, to drive strategic impact across multiple lines of business at Apple. You will use tools and platforms such as Python, Clojure, Splunk, SQL, and Spark, among others. Apple's dedication to customer privacy, the evolving nature of regulatory requirements, and the enormous scale of the business present exciting challenges to traditional machine learning and data science techniques. On this team, we will push the limits of existing data science methods while delivering tangible business value! Engage with business teams to find opportunities, understand compliance requirements, and translate those requirements into technical solutions. Design a data science approach, applying tried-and-true techniques or developing custom algorithms as needed for the compliance problem. Collaborate with engineers and platform architects to implement robust production real-time and batch compliance decisioning solutions. Ensure operational, regulatory, and business metric health by monitoring production decision points. Investigate emerging risk typologies and regulatory changes, identify behavior patterns, and respond with agile logic updates. Communicate results of analyses to business partners and executives, and partner with teams across Apple to find additional opportunities and services that improve loss avoidance and the overall customer experience.

Education in a quantitative field, such as Computer Science, Applied Mathematics, or Statistics, or equivalent professional experience. 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 analytical pipelines and applications in a general programming language such as Python, Scala, Java, or Clojure. Experience with financial crimes compliance, such as AML, KYC, sanctions screening, or transaction monitoring. Experience with model explainability, documentation, and auditability in regulated environments. 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. Experience with Splunk or similar log analytics tools.