Sr. Applied Scientist, Private Brands Discovery

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 methods from Bayesian Optimization, multi-armed bandits and reinforcement learning, causal and statistical inference, and econometrics to drive discovery 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 defining a long-term science vision for discovery across placements and translating that direction into specific plans for scientists, as well as partnering engineering and product teams. This is a role that combines science leadership, organizational ability, technical strength, 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. This person will have sound judgment and help recruit and groom high caliber science talent.



As a Senior Applied Scientist in Private Brands Discovery you will:

* Drive applied science projects in machine learning end-to-end: from ideation over prototyping to launch. For example, starting from deep scientific thinking about new ways to support customers’ journeys through discovery, you analyze how customers discover, review and purchase Private Brands to innovate marketing and merchandising strategies.
* Propose viable ideas to advance models and algorithms, with supporting argument, experiment, and eventually preliminary results.
* Invent ways to overcome technical limitations and enable new forms of analyses to drive key technical and business decisions. For example, you might work with engineers to design the necessary infrastructure to operate an RL or bandit policy at low-latency, accounting for the iterative nature of scientific discovery.
* Present results, reports, and data insights to both technical and business leadership.
* Constructively critique peer research and mentor junior scientists and engineers.
* Innovate and contribute to Amazon’s science community and external research communities.

Please visit https://www.amazon.science for more information.

Amazon Science gives you insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work

Basic Qualifications

* 5+ Years of relevant, broad research experience after a PhD degree
* 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: data-driven optimization (e.g., multi-armed bandits, Bayesian optimization, reinforcement learning, or related fields), causal inference, high-dimensional statistical models, econometrics and time series modeling
* Relevant publications in well-known associations, for example NeurIPS, ICML, AISTATS, ICLR, or ACL
* Experience working with very large real-world data sets and building scalable models from big data
* Experience in building machine learning models for online advertising and personalized recommendations
* Effective verbal and written communications skills



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, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.