Applied Science Manager

Job description

DESCRIPTION

Amazon is the 4th most popular site in the US. Our product search engine, one of the most heavily used services in the world, indexes billions of products and serves hundreds of millions of customers world-wide. We are working on a new initiative to transform our search engine into a shopping engine that assists customers with their shopping missions. We're looking at every aspect of search, from query understanding to front-end UX, ranking and relevance, indexing and tiering and asking how we can make big step improvements by applying advanced Machine Learning (ML) and Deep Learning (DL) techniques. This is a rare opportunity to develop cutting edge ML solutions and apply them to a search problem of this magnitude. Some exciting questions that we expect to answer over the next few years include:

Can we deeply understand customer intent and personalize their search experience even when they type broad queries such as “dress” or “espresso machine”?
Can we reduce the cost of serving customer queries on Amazon by orders of magnitude using ML to predict n-grams and tuples that many queries decompose into, apply expensive ranking functions offline to identify the most relevant products that match these terms, and index these for efficient online retrieval? We expect this to lead to exciting research at the intersection of systems and ML.
Can we deeply understand the catalog to surface products that offer the most value to a customer? The challenge here is that the definition of value is subjective and personal, and therefore requires a deeper understanding of the customers intent as well as preferences.
Can we use deep learning to transfer behavioral signals from frequently purchased products in the head to products in the tail where behavioral signals are sparse? The challenge here is the scale, and the fact that the head and torso contain only a small fraction of products while the tail contains an overwhelmingly large fraction of the products in the catalog.
We have hired ML experts from leading research labs and academia to spearhead this effort. Our research leaders include Trishul Chilimbi (formerly MSR), Inderjit Dhillon (UT Austin), Guy Lebanon (formerly Georgia Tech), and S.V.N. (Vishy) Vishwanathan (formerly UC Santa Cruz). We are looking to hire Software Development Engineers (SDEs) and ML Applied Scientists at all levels, with experience in Search, Personalization, NLP, Systems, ML, DL and UI Design. Internship opportunities are also available throughout the year and we are flexible with duration and start dates. You will be working alongside world-class scientists and engineers to build next generation search systems and will be able to deploy your ML models into production. Our team is proud of its collaborative and open research environment, where long term thinking and risk taking are highly rewarded. We value academic collaborations and encourage our scientists and engineers to participate and publish in top conferences such as NIPS, ICML, KDD, SIGIR and WWW. Positions are available in Palo Alto, Berkeley, Seattle, Boston, Barcelona, and Tokyo.

The Query Understanding Modeling team is responsible for developing and deploying state of the art machine learning and NLP models to extract semantic information on product search queries issued by millions of Amazon customers each day.

You will be responsible for building a team of scientists and developers who are experienced in taking an idea to reality – from prototype to a customer-facing product, their career development, as well as the road map definition and prioritization for the organization. You will be expected to be heavily entrepreneurial in style and be experienced to develop a business plan as well as dive deep in the scientific and technical details of the product your team is building.

Desired profile

BASIC QUALIFICATIONS

· Master degree in computer science, physics, statistics or other quantitative sciences.
· Minimum of 7 years of experience in applied science, 3+ years of with people management responsibility.