The photo shows an Amazon truck parked with the company logo and the word prime painted on the side
To help deliver more value to Prime members, scientists within Amazon’s Prime organization develop methods to help consumers discover and utilize Prime benefits.

The science behind Amazon Prime

Amazon’s scientists have developed a variety of scientific models to help customers get the most out of their membership.

In his 2020 shareholder letter, Jeff Bezos, executive chair of Amazon’s board of directors, shared that more than 200 million people around the world have a Prime membership — along with its attendant benefits.

Those include delivery benefits (like free one and two-day delivery), digital benefits (such as Prime Video and Amazon Music), and shopping benefits (including Prime Day member deals). Prime members are also able to download thousands of e-books, magazines and comics for free, get unlimited photo storage, order groceries online, and more.

Related content
The SCOT science team used lessons from the past — and improved existing tools — to contend with “a peak that lasted two years”.

Amazon is continually expanding and evolving its selection of Prime benefits to enhance the value for members. As Bezos wrote in an earlier shareholder letter: "We want Prime to be such a good value, you'd be irresponsible not to be a member.”

To help deliver more value to Prime members, scientists within Amazon’s Prime organization develop methods to help consumers discover and utilize Prime benefits. Using techniques derived from machine learning, structural econometrics, and other disciplines, they also help Amazon decide how to evolve Prime benefit offerings around the world.

Surface the most relevant Prime benefits to customers

When shoppers visit the Amazon Store, they are presented with a variety of Prime callouts with relevant benefits and related product information. Callouts for non-Prime members might outline the wide variety of benefits available, while Prime members might see more options to utilize their Prime benefits. For example, a Prime member visiting the detail page for the movie Jane Eyre might see a callout saying that the title is available for free on Prime Reading.

We utilize recommender systems to engage shoppers with information about Prime benefits that they would find most interesting.
Houssam Nassif

“We utilize recommender systems to engage shoppers with information about Prime benefits that they would find most interesting,” says Houssam Nassif, a principal applied scientist within Amazon’s consumer organization.

To make predictions about the callout that will most excite customers, the system maps item attributes (like brand, color, price, title, and category) to how often items are selected by customers. The models embedded in the system use Bayesian recommenders to make decisions on the most relevant content to surface. Bayesian inferences are used to make predictions about future events by updating prior hypotheses as more information becomes available.

Related content
Dual embeddings of each node, as both source and target, and a novel loss function enable 30% to 160% improvements over predecessors.

However, there are limits to this approach. For example, relying exclusively on Bayesian methods to measure customer selections can bias results toward more popular items. For example, shoppers interested in Jane Eyre might also want to read new romance novels. The challenge: newer items have untrained model weights, which can cause the system to underestimate their true click probability.

“This experience would be similar to going to a music recommendation engine, and seeing only the chart toppers in your favorite categories,” Nassif explains. “To improve the diversity of recommendations, we have to overcome the classic exploitation-exploration dilemma by including relevant and popular items [exploitation] along with newer or long-tail items that scored higher than their expected value [exploration].”

To do this, the Prime ML team utilizes methods that allow the algorithm to update the “click-probability” score by using delayed feedback from customers.

Some of the recommender systems employed by the Prime team are captured in the paper "Bayesian meta-prior learning using Empirical Bayes".

“Adaptive systems allow us to focus the diversity of recommendations even further,” says Nassif.

Prime’s adaptive systems respond continually to evolving preferences across all Amazon customers. For example, classic-literature enthusiasts who read Jane Eyre will not see callouts for romance novels or romantic comedy movies unless they express some interest in other romance novels. Some of those recommender systems are captured in the paper "Bayesian meta-prior learning using Empirical Bayes".

Recommending content that customers love

Determining the most relevant Prime benefits to present to users is the first step. Prime’s scientists have also developed algorithms to determine which formats are most likely to appeal to customers.

“Every callout has multiple dimensions, which in turn presents a large number of decisions,” says Nassif. “Do customers like to see their name? Should the callout feature a single particular product? Or even a grouping of products? To make these decisions, we have to develop an accurate understanding of customer preferences.”

Related content
Learn how the Amazon Music Conversations team is using pioneering machine learning to make Alexa's discernment better than ever.

Callouts comprise multiple components: headline, body copy, an image (or images). They can also include other elements like customer reviews. Testing multiple variables is a combinatorial problem that can often cover a large decision space. This poses limitations on the speed of experiments designed to arrive at the layout customers prefer most.

To eliminate combinatorial explosions that can result from considering every possible combination, the models score a small subset of combinations before extrapolating their learnings to the larger universe of layouts that can be presented to customers. Conditioned by prior observations, the models are able to select the layout that has the highest probability of delivering the highest customer value.

Evolving the selection of Prime benefits

In addition to informing how customers receive recommendations about Prime as it exists today, scientists also influence how Prime will evolve as a membership. This work involves scientists from multiple disciplines collaborating closely to determine the best selection of benefits: from determining how best to reduce shipping speeds for Prime (including items eligible for the fastest speeds) to recommending which new podcasts Amazon Music should release.

Charlie Manzanares is a senior manager on the team that specializes in simulating how customers benefit from expansion of Prime benefits. Manzanares’s team comprises economists, applied scientists, research scientists, and business intelligence engineers who partner closely with product managers and software and data engineers.

Our team works at the scientific intersection of structural econometrics, machine learning, and causal inference. Building these tools often involves inventing new science.
Charlie Manzanares

“Our team works at the scientific intersection of structural econometrics, machine learning, and causal inference,” says Manzanares. “Building these tools often involves inventing new science, by involving scientists and engineers from a variety of backgrounds. We then utilize these tools to create scientific software at engineering scale. What’s exhilarating about this space is not just solving these scientific and technical challenges, but using these tools to make Prime better for members around the world. Moreover, the company relies on this information to make high-stakes investments. This adds an interesting layer of strategic management consulting to our work.”

Manzanares points to a recent innovation from Prime scientists that made modeling dynamic customer decisions easier.

“Prime members make ‘dynamic’ choices over whether, and when, to become and remain Prime members. Dynamic customer choices often involve tradeoffs between value and flexibility,” he explains.  “For example, in the US, most customers choose between joining Prime’s annual or monthly plans, or ending their membership or not joining Prime at all. Over time, this tradeoff results in many possible permutations of choices. For example, a member might choose monthly Prime for two months, then join annual Prime. Or they might choose monthly Prime for two months, remain non-Prime for three, then join monthly Prime for five more months, etc.”

Modeling the impact of these choice permutations in a way that is useful for counterfactual simulation is theoretically and computationally challenging.

The theoretical challenge is an “identification” problem, Manzanares explains. The identification problem makes it hard for scientists to determine which Prime feature caused members to make a particular choice.

“For example, did a member who engaged with Prime shipping and Prime Video choose to renew because they valued Prime shipping highly, but Prime Video less, or Prime Video highly, and Prime shipping less?” asks Manzanares. “This problem is common to both dynamic and ‘static’ choice problems (i.e., choice problems where choice values are not influenced by past choices). The computational problem — which is pervasive in dynamic choice settings — is generated by the sheer number of possible choices, which is labeled the ‘curse of dimensionality’ in dynamic programming literature.”

To overcome these challenges, the team combined new techniques from inverse reinforcement learning with an old assumption from structural econometrics. Inverse reinforcement learning is a machine learning paradigm popularized in the late 1990s and early 2000s.

As opposed to reinforcement learning, which learns behavioral “policies” through active experimentation, inverse reinforcement learning learns “reward” or “utility” functions from actual customer behavior. It then uses estimated utility functions to make choices in new settings. Structural econometrics is an older paradigm with a rich literature and has been used for these types of exercises since the 1940s.

"Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions” was published at the 2020 International Conference for Machine Learning.

“On the one hand, inverse reinforcement learning draws upon modern machine learning techniques. These techniques allow for rich approximations in complex settings,” says Manzanares. “On the other hand, structural econometrics has already solved many complex theoretical issues related to counterfactual simulation. These solutions often predate the development of modern machine learning and computation. This dichotomy creates opportunities for intellectual arbitrage between literatures.”

The team’s approach to the challenge is described in the paper “Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions,” which was published at the 2020 International Conference for Machine Learning.

“The findings presented in the paper are applicable across multiple fields,” says Manzanares. “That’s not surprising since the paper’s insights were made possible by collaboration across multiple disciplines.”

Prime scientists use inverse-reinforcement models to develop insights. These insights show how Prime should evolve to meet customer needs. For example, how should Prime evolve to best meet the needs of Gen Z, who engage more heavily with digital benefits (video, music, gaming)? How can grocery delivery and pickup maximize customer convenience?

These questions multiply as Prime expands globally. In international marketplaces — especially emerging ones — customer needs vary widely. For example, how might Prime serve both rural and urban customers in a marketplace like India, where needs among rural and urban customers might be very different? Experimentation, Manzanares notes, becomes critical.

 “The process of discovering what customers want across the world is a lot of fun,” he says. “Combine that with building cutting-edge science in partnership with extremely talented science, engineering, and business professionals, and this makes Prime a really rewarding place to be a scientist.”

Research areas

Related content

US, WA, Bellevue
Conversational AI ModEling and Learning (CAMEL) team is part of Amazon Devices organization where our mission is to build a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful Senior Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As a Senior Applied Scientist, you will leverage your technical expertise and experience to demonstrate leadership in tackling large complex problems, setting the direction and collaborating with other talented applied scientists and engineers to research and develop LLM modeling and engineering techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Prompt Engineering, Model Fine-Tuning, Reinforcement Learning from Human Feedback (RLHF), Evaluation, etc. Your work will directly impact our customers in the form of novel products and services .
US, CA, Pasadena
The Amazon Web Services (AWS) Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. We are looking to hire a Research Scientist with fabrication and data analysis experience working on all elements of a superconducting circuit. The position is on-site at our lab, located on the in Pasadena, CA. The ideal candidate will have had prior experience building software tools for data analysis and visualization to enable deep diving into fabrication details, electrical test data. We are looking for candidates with strong engineering principles, resourcefulness and data science experience. Organization and communication skills are essential. Key job responsibilities * Develop and automate data pipeline pertinent to superconducting device fabrication. * Develop analytical tools to uncover new information about established and new processes. * Develop new or contribute to modifying existing data visualization tools. * Utilize machine learning to enable better deeper dives into fabrication and related data. * Interface with various software, design, fabrication and electrical test teams to enable new functionalities. A day in the life The role will be vital to the fabrication team and quantum computing device integration mechanism. The candidate will develop software based analytical tools to enable data driven decisions across projects related to fabrication and supporting infrastructure. Each fabrication run delivers additional data. The candidate will stay close to the details of fabrication providing data analysis and quick feedback to key stakeholders. At the end of fabrication runs custom and standardized reports will be generated by the candidate to provide insights into data generated from the run. This position may require occasional weekend work. About the team AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches
CA, ON, Toronto
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! As an Applied Scientist on this team, you will: - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Run A/B experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Research new and innovative machine learning approaches. - Recruit Applied Scientists to the team and provide mentorship. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! As the Data Science Manager on this team, you will: - Lead of team of scientists, business intelligence engineers, etc., on solving science problems with a high degree of complexity and ambiguity. - Develop science roadmaps, run annual planning, and foster cross-team collaboration to execute complex projects. - Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management. - Hire and develop top talent, provide technical and career development guidance to scientists and engineers in the organization. - Analyze historical data to identify trends and support optimal decision making. - Apply statistical and machine learning knowledge to specific business problems and data. - Formalize assumptions about how our systems should work, create statistical definitions of outliers, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed. - Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes. - Build decision-making models and propose effective solutions for the business problems you define. - Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Why you will love this opportunity: Amazon has invested heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video ~ https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! As an Applied Science Manager in Machine Learning, you will: - Directly manage and lead a cross-functional team of Applied Scientists, Data Scientists, Economists, and Business Intelligence Engineers. - Develop and manage a research agenda that balances short term deliverables with measurable business impact as well as long term investments. - Lead marketplace design and development based on economic theory and data analysis. - Provide technical and scientific guidance to team members. - Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment - Advance the team's engineering craftsmanship and drive continued scientific innovation as a thought leader and practitioner. - Develop science and engineering roadmaps, run annual planning, and foster cross-team collaboration to execute complex projects. - Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management. - Collaborate with business and software teams across Amazon Ads. - Stay up to date with recent scientific publications relevant to the team. - Hire and develop top talent, provide technical and career development guidance to scientists and engineers within and across the organization. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Team video ~ https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches
US, CA, San Diego
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches
US, CA, San Diego
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches