Principal Product Manager Technical en Barcelona

The Search NLP team is responsible for developing and deploying state of the art machine learning and NLP models to extract semantic information from product search queries, item descriptions, reviews, and many other sources of information created by millions of Amazon customers each day.

As Product Manager in our group you will be responsible for helping in designing customer experiences, coordinating dependencies, launches, document services, coordinating internal and external weblabs with external teams, organizing data annotation efforts, etc.
Additionally, your role will include: 1) identifying growth opportunities to scale the recommendations across and beyond the site; 2) revisiting the long term strategy for the product; and 3) driving PR/Legal strategy and reviews.

At Amazon Search, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), one of the world's leading internet companies. We provide a highly customer-centric, team-oriented environment in our offices located in Barcelona (Spain) and Berlin (Germany).

More on Search...

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.

#techleader

· 8-10 years of product management or similar experience
· Demonstrated success in leading product/system development at scale
· Excellent strategic customer-focused thinking skills demonstrated through multiple successful product release cycles
· Strong analytical and quantitative skills; bias toward making data-based decision and comfortable with analysis
· Proven ability to make smart feature versus time-to-market trade-offs; experience using data and metrics to back up assertions of business value
· Ability to juggle multiple priorities and create a sense of urgency in a fast-paced, dynamic environment
· Proven ability to lead cross-functional teams through influence versus direct management; excellent interpersonal skills
· Superlative written and verbal communication skills
· Computer Science, Information Systems or equivalent Bachelor's Degree
· Experience working with engineering teams to define, scope and prioritize product scenarios

On the technical side: being familiar with the technologies applied in AI and Search products, in particular in the areas of NLP and Machine Learning.