Machine Learning Engineer – Strategic Data Solutions Full-time

Summary
Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something – you’ll add something.

Do you love the challenge of solving complex problems that can have a direct and meaningful impact on the company? Do you want to be part of a supportive team that’s constantly learning and having fun while solving tough business problems? We’d love to talk to you if you do!

At Apple, new ideas have a way of quickly becoming outstanding products, services, and customer experiences. Bring passion and dedication to your career, and there’s no telling what you could accomplish!

Strategic Data Solutions (SDS) empowers internal partners and optimizes the customer experience by delivering data-driven solutions that mitigate fraud, improve security, and optimize efficiency. Our work touches all parts of Apple, from manufacturing to fulfillment to apps and services. The enormous scale and complexity of the problems and our data present exciting opportunities for pushing the limits of existing data science methods.

As an SDS machine learning engineer, you will work with teams across Apple, using 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.

Description
– Engage with stakeholders to translate ambiguous business problems into technical solutions, including finding opportunities, breaking them into solvable segments, defining requirements, assessing level of effort, etc.

– Work cooperatively to design data science-driven solutions, balancing the utility of tried-and-true techniques and the benefits of custom solutions.

– Collaborate with technical partners to implement robust real-time and batch decisioning in production.

– Create reporting and monitor decisioning quality to maintain operational and business metric health.

– Investigate trends, assess threat impact, and respond with agile logic changes.

– Communicate with stakeholders with varying technical backgrounds and business.

– Priorities about your work.

– Share what you’re learning about novel technologies and methods (in data science,
machine learning, data engineering, and software engineering, etc) to improve your team’s overall technical capabilities.