Senior Applied Scientist, Featured Merchant Algorithm
Description
Do you shop at Amazon? Do you know that box that says 'Add to Cart' and shows a price? Our team owns the model which picks that offer and the customer experience around the display of the offer price and the elements surrounding the 'Add to Cart' button which inform a purchase decision. We pick and display offers several billion times a day across all surfaces (mobile app, mobile web, desktop, Alexa shopping) worldwide. Our mission is to be the world’s first and most trusted choice for every customer on earth to discover and evaluate any product or service. Our team blends machine learning models to rank and select the best offer from the most trusted merchant for all products sold on Amazon along with a world-class front-end user experience for offer comparison to our global customers. We are responsible for the experiences and services that enable developers, including our own, to create tailored shopping experiences for every customer, product, business and marketplace offered by Amazon. We build scalable and extensible frameworks which allow for many teams at Amazon to innovate within the Offers Experience in a federated manner. If you are passionate about influencing and delivering the next-generation Amazon customer buying experience, we want to meet you. We are looking for a Senior Applied Scientist to join one of the most impactful and visible teams in Amazon.
Key job responsibilities
- Build, innovate and maintain FMA's key offer selection algorithms
- Collaborate with peer scientists and partner organizations to align on strategic algorithmic inputs
- Research and deliver innovative techniques for ranking, simulation and evaluation systems
- Build and maintain AI, ML and LLM integrations
Basic Qualifications
- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
PREFERRED QUALIFICATIONS
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.