Machine learning, big data; near real-time scoring environment. If these areas resonate with you, then join us to work on extremely motivating challenge at Amazon Web Services (AWS) marketing. We build and run custom machine learning models to solve challenging business problems at scale. If you are a strong Data Engineer, self-starter and learner who is passionate about working with massive amounts of data to build state-of-art system on AWS platform, then this is the right opportunity for you. You will work with a team of highly skilled engineers and scientists, to build the next generation ML platform using AWS services. As part of your job, you will deal with large amounts of training data, partner with Scientists and help rapidly prototype new models that meet stringent performance requirements, perform offline and online testing, and push these models to production.
As part of this role, you will be required to:
- Analyze and extract relevant information from large amounts of historical data to help automate and optimize key features and ML processes
- Establish scalable, efficient, automated processes for large scale data analyses, model development, validation and implementation
- Work closely with scientists and engineers to create and deploy new features
- Work closely with stakeholders to solve various business problems
You are fascinated by the power of large scale systems and using machine learning algorithms to optimize decision making. And you're looking for a career where you'll be able to build, to deliver, and to impress. You look at problems holistically, and thrive on the intricate complexity of designing feedback loops and ecosystems. You want to work on projects where you are implementing solutions to real problems that require creative solutions and deep understanding of the problem space. You will partner with scientists and engineers to challenge yourself and others to constantly come up with better solutions. You'll be given an opportunity to own and drive initiatives - from customer facing features, system innovation, all the way down to the datasets that the back-end services consume.