The moment a customer makes a payment on Amazon is when trust is established – trust that the item is delivered on time, a refund is provided quickly if needed, a digital movie purchased will play immediately, a seller receives their disbursement, and hundreds of other experiences across Amazon when a customer completes a payment. The Payment Acceptance & Experience (PAE) team, within the Consumer Payments organization, has the mission to build the most trusted, intuitive, and accessible payment experience on Earth. Within PAE, the PAE ML team has a mission to enhance customer payments experience that requires advancing the state of the art in machine learning. We work backwards from the customer to create value for them by leveraging an underlying applied science methodology. We deploy our solutions through Native AWS services that operate at Amazon scale. We strive to publish our solutions and share our findings so that the broader Amazon scientific community can benefit.
As an applied scientist on our team, your role is to leverage your strong background in Computer Science and Machine Learning to help build the next generation of our model development and assessment pipeline, harness and explain rich data at Amazon scale, and provide automated insights to improve machine learned solution that impacts Payments experience of millions of customers every day. This role requires a pragmatic technical leader comfortable with ambiguity, capable of summarizing complex data and models through clear visual and written explanations. The ideal candidate will have experience with machine learning models and applying science to various business contexts. We are particularly interested in experience applying predictive modeling, natural language processing, deep learning, and reinforcement learning at scale. Additionally, we are seeking candidates with strong rigor in applied sciences and engineering, creativity, curiosity, and great judgment.
Your responsibilities include:
. Analyze the data and metrics resulting from traffic into Amazon Consumer Payments experiences.
. Design, build, and deploy effective and innovative ML solutions to improve various components of the Consumer Payments experience, using predictive modeling, recommendations, anomaly detection, ranking, and forecasting.
. Evaluate the proposed solutions via offline benchmark tests as well as online A/B tests in production.
. Publish and present your work at internal and external scientific venues in the fields of ML/NLP/IR/Forecasting.
Your benefits include:
. Working on a high-impact, high-visibility product, with your work improving the experience of millions of customers.
. The opportunity to use (and innovate) state-of-the-art ML methods to solve real-world problems.
. Excellent opportunities, and ample support, for career growth, development, and mentorship.
. Competitive compensation, including relocation support.
The PAE ML team operates primarily out of Amazon's Seattle office. We are a new and expanding team where you will have an opportunity to influence our goals and mission. We collaborate with Software Engineering, Data Engineering, Product Management and Marketing teams within Amazon Consumer Payments to solve and deploy machine learning solutions at scale.
Please visit https://www.amazon.science for more information
- MS in CS, Machine Learning or in a highly quantitative field.
- Hands-on experience (academic or industrial) in predictive modeling and big data analysis.Strong coding and problem-solving skills in at least one programming language such as Python, Java, Scala etc.
- Working knowledge of web-scale data processing (e.g., PySpark).
- Sound theoretical understanding of broad machine learning concepts, with deep and demonstrable expertise in at least one topic or application of machine learning.
- PhD in CS, Machine Learning or in a highly quantitative field.
- Prior work experience as an applied scientist or a data scientist at a consumer product company.
- Experience using an object-oriented language to write production-ready code.
- At least one record of publication in one of the following areas: information retrieval, natural language processing, recommender systems, reinforcement learning or multi-armed bandits.
- Industry experience working with anomaly detection, ranking, customer segmentation, or recommender systems.
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
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.