Applied Scientist, Private Brands - NAC - Scale

Job summary
The Private Brands Discovery team designs innovative machine learning solutions to drive customer awareness for Amazon’s own brands and help customers discover products they love. Private Brands Discovery is an interdisciplinary team of Scientists, Economists, and Engineers, that incubates and builds disruptive solutions using cutting-edge technology to solve some of the toughest science problems at Amazon. To this end, the team employs state-of-the-art methods from deep learning, Bayesian optimization, multi-armed bandits, reinforcement learning, causal and statistical inference, econometrics, or any other novel approach that drives discovery of products across the customer journey. The solutions have to scale in production systems and the models can be trained in datasets of several terabytes. The team also works closely with academic researchers in ML and statistics at elite institutions called Amazon Scholars. These Scholars support us with science innovation, hypothesis formulation, and experimentation.

To be successful in this role, you need to be comfortable translating your science vision into specific plans for scientists and engineers, as well as partnering with product teams. This is a role that combines scientific excellence, organizational ability, product focus and business understanding. The ideal candidate will be an independent thinker who can make convincing, information-based arguments. With a focus on bias for action, this individual will be able to work equally well with Science, Engineering, Economics and business teams.

As an Applied Scientist in Private Brands Discovery you will:

· Drive collaborative research and creative problem solving.
· Constructively critique peer research and mentor junior scientists and engineers.
· Create experiments and prototype implementations of new learning algorithms and prediction techniques.
· Collaborate with engineering teams to design and implement software solutions for science problems.
· Innovate and contribute to Amazon’s science community and external research communities. Stay current on the state-of-the-art literature.
· You will be designing, prototyping models or analyzing experiment results. Research and critique publications. Huddle with your team to execute designs and contribute to team goals. Participate in fun team activities or a casual lunch.


Key job responsibilities
Responsibilities:

· Drive collaborative research and creative problem solving
· Constructively critique peer research and mentor junior scientists and engineers
· Create experiments and prototype implementations of new learning algorithms and prediction techniques
· Collaborate with engineering teams to design and implement software solutions for science problems
· Contribute to progress of the Amazon and broader research communities by producing publications

A day in the life
You will be designing, prototyping models or analyzing experiment results. Research and critique publications. Huddle with your team to execute designs and contribute to team goals. Participate in fun team activities or a casual lunch

Basic Qualifications

* PhD degree in CS, CE, or related technical field
* 3+ years of experience in building scalable machine learning solutions for business applications
* Experience in programming and testing, e.g., in Java, C++, Python or a related language
* Effective verbal and written communications and presentation skills, in particular, the ability to convey rigorous mathematical concepts and considerations to non-experts and stakeholders

Preferred Qualifications

* Expertise in one or more of the following domains: 1) data-driven optimization (e.g., multi-armed bandits, Bayesian optimization, reinforcement learning, or related fields), 2) causal inference, 3) high-dimensional statistical models, 4) econometrics and time series modeling, 5) Natural Language Processing (NLP) and deep modeling, e.g., attention-based DNN
* Track-record of applied problem solving, business judgment, diving deep, and thinking big
* Relevant publications in well-known associations, for example NeurIPS, ICML, AISTATS, ICLR, or ACL
* Experience with processing distributed datasets for modeling, for example via Spark


Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, disability, age, or other legally protected status. If you would like to request an accommodation, please notify your Recruiter.