Advances in trustworthy machine learning at Alexa AI

The team’s latest research on privacy-preserving machine learning, federated learning, and bias mitigation.

At Amazon, we take the protection of customer data very seriously. We are also committed to eliminating the biases that can exist in off-the-shelf language models — such as GPT-3 and RoBERTa — that are the basis of most modern natural-language processing. Trained on public texts, these language models are known to reflect the biases implicit in those texts.

Related content
Calibrating noise addition to word density in the embedding space improves utility of privacy-protected text.

These two topics — privacy protection and fairness — are at the core of trustworthy machine learning, an important area of research at Alexa AI. In 2021, we made contributions in the following areas:

  • Privacy-preserving machine learningDifferential privacy provides a rigorous way to quantify the privacy of machine learning models. We investigated vulnerabilities presented in the differential-privacy literature and propose computationally efficient mechanisms for protecting against them.
  • Federated learning: Federated learning (FL) is a distributed-training technique that keeps customer data on-device. Devices send only model parameter updates to the cloud, not raw data. We studied several FL challenges arising in an industrial setting.
  • Fairness in machine learning: Machine learning (ML) models should perform equally well regardless of who’s using them. But even knowing how to quantify fairness is a challenge. We introduced measures of fairness and methods to mitigate bias in ML models.
Counterfactuals.png
To reduce binary-gender disparity in a distilled GPT-2 language model, we introduce counterfactual examples, in which binary genders in real-world training examples are swapped.

Below, we summarize our research in these areas, which will be presented at ACL and ICASSP later this year. We also invite readers to participate in workshops and sessions we are organizing at NAACL 2022 and Interspeech 2022.

1. Privacy-preserving ML

The intuition behind differential privacy (DP) is that access to the outputs of a model should not provide any hint about what inputs were used to train the model. DP quantifies that intuition as a difference (in probabilities) between the outputs of a model trained on a given dataset and the outputs of the same model trained on the same dataset after a single input is removed.

One way to meet a DP privacy guarantee is to add some noise to the model parameters during training in order to obfuscate their relationship to training data. But this can compromise accuracy. The so-called privacy/utility tradeoff appears in every DP application.

Another side effect of adding a DP mechanism is increased training time. Given that training natural-language-understanding (NLU) models with large volumes of data can be prohibitively slow and that industry standards require fast training and deployment — e.g., when new features are being released — we developed a training method that meets DP requirements but remains efficient. We describe the method in a paper we’re presenting at this year’s ICASSP, “An efficient DP-SGD mechanism for large scale NLP models”.

In this work, we study the most popular DP mechanism for deep neural networks, DP-SGD, and build a computationally efficient alternative, eDP-SGD, in which we use a batch-processing scheme that leverages the GPU architecture and automates part of the hyperparameter-tuning process. While both DP-SGD and eDP-SGD provide the same privacy guarantees, we show that the training time for our mechanism is very similar to its non-DP counterpart’s. The original DP-SGD extends training time as much as 130-fold.

Related content
ADePT model transforms the texts used to train natural-language-understanding models while preserving semantic coherence.

Since we did our study, researchers have developed methods with stronger theoretical DP guarantees than the ones we impose in our paper, but our approach is consistent with those methods. Overall, this work makes DP more generally accessible and helps us integrate NLU models with DP guarantees into our production systems, where new models are frequently released, and a significant increase in training time is prohibitive.

While DP provides theoretical privacy guarantees, we are also interested in practical guarantees, i.e., measuring the amount of information that could potentially leak from a given model. In addition to the performance and training time of eDP-SGD, we also studied the correlation between theoretical and practical privacy guarantees. We measured practical privacy leakage using the most common method in the field, the success rate of membership inference attacks on a given model. Our experiments provide a general picture of how to optimize the privacy/utility trade-off using DP techniques for NLU models.

We also expanded the set of mechanisms for protecting NLU models against other types of attacks. In “Canary extraction in natural language understanding models”, which we will present at ACL 2022, we study the vulnerability of text classification models to a certain kind of white-box attack called a model inversion attack (ModIvA), where a fictional attack has access to the entire set of model parameters and intends to retrieve examples used during training. Existing model inversion techniques are applied to models with either continuous inputs or continuous outputs. In our work, we adopt a similar approach to text classification tasks where both inputs and outputs are discrete.

As new model architectures are developed that might display new types of vulnerabilities, we will continue innovating efficient ways of protecting our customers’ privacy.

Upcoming activities

2. Federated Learning

The idea behind federated learning (FL) is that, during the training of an ML model, part of the computation is delegated to customers’ devices, leveraging the processing power of those devices while avoiding the centralization of privacy-sensitive datasets. Each device modifies a common, shared model according to locally stored data, then sends an updated model to a central server that aggregates model updates and sends a new shared model to all the devices. At each round, the central server randomly selects a subset of active devices and requests that they perform updates.

Federated Learning Animation.gif
With federated learning, devices send model updates, not data, to a central server.

In the past year, we have made progress toward more-efficient FL and adapted common FL techniques to the industrial setting. For instance, in “Learnings from federated learning in the real world”, which we will present at ICASSP this year, we explore device selection strategies that differ from the standard uniform selection. In particular, we present the first study of device selection based on device “activity” — i.e., the number of available training samples.

These simple selection strategies are lightweight compared to existing methods, which require heavy computation from all the devices. They are thus more suitable to industrial applications, where millions of devices are involved. We study two different settings: the standard “static” setting, where all the data are available at once, and the more realistic “continual” setting, where customers generate new data over time, and past examples might have to be deleted to save storage space. Our experiments on training a language model with FL show that non-uniform sampling outperforms uniform sampling when applied to real-world data, for both the static and continual settings.

Related content
Amazon researchers optimize the distributed-training tool to run efficiently on the Elastic Fabric Adapter network interface.

We also expanded our understanding of FL for natural-language processing (NLP) and, in the process, made FL more accessible to the NLP community. In “FedNLP: A research platform for federated learning in natural language processing”, which will be presented later this year at NAACL, we and our colleagues at the University of Southern California and FedML systematically compare the most popular FL algorithms for four mainstream NLP tasks. We also present different methods to generate dataset partitions that are not independent and identically distributed (IID), as real-world FL methods must be robust against shifts in the distributions of the data used to train ML models.

Our analysis reveals that there is still a large gap between centralized and decentralized training under various settings, and we highlight several directions in which FL for NLP can advance. The paper represents Amazon’s contribution to the open-source framework FedNLP, which is capable of evaluating, analyzing, and developing FL methods for NLP. The codebase contains non-IID partitioning methods, enabling easy experimentation to advance the state of FL research for NLP.

We also designed methods to account for the naturally heterogeneous character of customer-generated data and applied FL to a wide variety of NLP tasks. We are aware that FL still presents many challenges, such as how to do evaluation when access to data is removed, on-device label generation for supervised tasks, and privacy-preserving communication between the server and the different devices. We are actively addressing each of these and plan to leverage our findings to improve FL-based model training and enhance associated capabilities such as analytics and model evaluation.

Upcoming activities

3. Fairness in ML

Natural-language-processing applications’ increased reliance on large language models trained on intrinsically biased web-scale corpora has amplified the importance of accurate fairness metrics and procedures for building more robust models.

In “On the intrinsic and extrinsic fairness evaluation metrics for contextualized language representations”, which we are presenting at ACL 2022, we compare two families of fairness metrics — namely extrinsic and intrinsic — that are widely used for language models. Intrinsic metrics directly probe into the fairness of language models, while extrinsic metrics evaluate the fairness of a whole system through predictions on downstream tasks.

Related content
Method significantly reduces bias while maintaining comparable performance on machine learning tasks.

For example, the contextualized embedding association test (CEAT), an intrinsic metric, measures bias through word embedding distances in semantic vector spaces, and the extrinsic metric HateXPlain measures the bias in a downstream hate speech detection system.

Our experiments show that inconsistencies between intrinsic and extrinsic metrics often reflect inconsistencies between the datasets used to evaluate them, and a clear understanding of bias in ML models requires more careful alignment of evaluation data. The results we report in the paper can help guide the NLP community as to how to best conduct fairness evaluations.

We have also designed new measures of fairness that are adapted to language-processing applications. In “Measuring fairness of text classifiers via prediction sensitivity”, which we will present at ACL 2022, we looked at sensitivity to perturbations of input as a way to measure fairness in ML models. The metric attempts to quantify the extent to which a single prediction depends on an input feature that encodes membership in an underrepresented group.

Accumulated prediction sensitivity.png
Our new bias measure, accumulated prediction sensitivity, combines the outputs of tow models, a task classifier (TC) and a protected status model (PSM).

We provide a theoretical analysis of our formulation and show a statistically significant difference between our metric’s correlation with the human notion of fairness and the existing counterfactual fairness metric’s.

Finally, we proposed a method to mitigate the biases of large language models during knowledge distillation, in which a smaller, more efficient model is trained to match the language model’s output on a particular task. Because large language models are trained on public texts, they can be biased in multiple ways, including the unfounded association of male or female genders with gender-neutral professions.

Distillation examples.png
Examples of texts generated by language models in response to gendered prompts before and after the application of our distillation method.

In another ACL paper, “Mitigating gender bias in distilled language models via counterfactual role reversal”, we introduce two modifications to the standard distillation mechanisms: data augmentation and teacher prediction perturbation.

We use our method to distill a GPT-2 language model for a text-generation task and demonstrate a substantial reduction in gender disparity, with only a minor reduction in utility. Interestingly, we find that reduced disparity in open-ended text generation may not necessarily lead to fairness on other downstream tasks. This finding underscores the importance of evaluating language model fairness along multiple metrics and tasks.

Our work on fairness in ML for NLP applications should help enable models that are more robust against the inherent biases of text datasets. There remain plenty of challenges in this field, but we strive to build models that offer the same experience to any customer, wherever and however they choose to interact with Alexa.

Upcoming activities

Related content

US, CA, Sunnyvale
At Amazon Fashion, we are obsessed with making Amazon Fashion the most loved fashion destinations globally. We're searching for Computer Vision pioneers who are passionate about technology, innovation, and customer experience, and who are enthusiastic about making a lasting impact on the industry. You'll be working with talented scientists, engineers, and product managers to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey and change the world of eCommerce forever Key job responsibilities As a Applied Scientist, you will be at the forefront to define, own and drive the science that span multiple machine learning models and enabling multiple product/engineering teams and organizations. You will partner with product management and technical leadership to identify opportunities to innovate customer facing experiences. You will identify new areas of investment and work to align product roadmaps to deliver on these opportunities. As a science leader, you will not only develop unique scientific solutions, but more importantly influence strategy and outcomes across different Amazon organizations such as Search, Personalization and more. This role is inherently cross-functional and requires a strong ability to communicate, influence and earn the trust of software engineers, technical and business leadership. We are open to hiring candidates to work out of one of the following locations: Sunnyvale, CA, USA
GB, Cambridge
Our team undertakes research together with multiple organizations to advance the state-of-the-art in speech technologies. We not only work on giving Alexa, the ground-breaking service that powers Echo, her voice, but we also develop cutting-edge technologies with Amazon Studios, the provider of original content for Prime Video. Do you want to be part of the team developing the latest technology that impacts the customer experience of ground-breaking products? Then come join us and make history. We are looking for a passionate, talented, and inventive Senior Applied Scientist with a background in Machine Learning to help build industry-leading Speech, Language and Video technology. As a Senior Applied Scientist at Amazon you will work with talented peers to develop novel algorithms and modelling techniques to drive the state of the art in speech and vocal arts synthesis. Position Responsibilities: - Participate in the design, development, evaluation, deployment and updating of data-driven models for digital vocal arts applications. - Participate in research activities including the application and evaluation and digital vocal and video arts techniques for novel applications. - Research and implement novel ML and statistical approaches to add value to the business. - Mentor junior engineers and scientists. We are open to hiring candidates to work out of one of the following locations: Cambridge, GBR
US, WA, Seattle
The Amazon Economics Team is hiring Economist Interns. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets to solve real-world business problems. Some knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL, UNIX, Sawtooth, and Spark would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, data scientists and MBAʼs. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with future job market placement. Roughly 85% of interns from previous cohorts have converted to full-time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Seattle
Amazon Advertising Impact Team is looking for a Senior Economist to help translate cutting-edge causal inference and machine learning research into production solutions. The individual will have the opportunity to shape the technical and strategic vision of a highly ambiguous problem space, and deliver measurable business impacts via cross-team and cross-functional collaboration. Amazon is investing heavily in building a world class advertising business. Our advertising products are strategically important to Amazon’s Retail and Marketplace businesses for driving long-term growth. The mission of the Advertising Impact Team is to make our advertising products the most customer-centric in the world. We specialize in measuring and modeling the short- and long-term customer behavior in relation to advertising, using state of the art econometrics and machine learning techniques. With a broad mandate to experiment and innovate, we are constantly advancing our experimentation methodology and infrastructure to accelerate learning and scale impacts. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. Key job responsibilities • Function as a technical leader to shape the strategic vision and the science roadmap of a highly ambiguous problem space • Develop economic theory and deliver econometrics and machine learning models to optimize advertising strategies on behalf of our customers • Design, execute, and analyze experiments to verify the efficacy of different scientific solutions in production • Partner with cross-team technical contributors (scientists, software engineers, product managers) to implement the solution in production • Write effective business narratives and scientific papers to communicate to both business and technical audience, including the most senior leaders of the company We are open to hiring candidates to work out of one of the following locations: New York, NY, USA | Seattle, WA, USA
US, NY, New York
Amazon is investing heavily in building a world-class advertising business, and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. We deliver billions of ad impressions and millions of clicks daily and break fresh ground to create world-class products. We are highly motivated, collaborative, and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. Our systems and algorithms operate on one of the world's largest product catalogs, matching shoppers with advertised products with a high relevance bar and strict latency constraints. Sponsored Products Detail Page Blended Widgets team is chartered with building novel product recommendation experiences. We push the innovation frontiers for our hundreds of millions of customers WW to aid product discovery while helping shoppers to find relevant products easily. Our team is building differentiated recommendations that highlight specific characteristics of products (either direct attributes, inferred or machine learned), and leveraging generative AI to provide interactive shopping experiences. We are looking for a Senior Applied Scientist who can delight our customers by continually learning and inventing. Our ideal candidate is an experienced Applied Scientist who has a track-record of performing deep analysis and is passionate about applying advanced ML and statistical techniques to solve real-world, ambiguous and complex challenges to optimize and improve the product performance, and who is motivated to achieve results in a fast-paced environment. The position offers an exceptional opportunity to grow your technical and non-technical skills and make a real difference to the Amazon Advertising business. As a Senior Applied Scientist on this team, you will: * Be the technical leader in Machine Learning; lead efforts within this team and collaborate across teams * Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, perform hands-on analysis and modeling of enormous data sets to develop insights that improve shopper experiences and merchandise sales * Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. * 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. * 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. * Promote the culture of experimentation and applied science at Amazon Team video https://youtu.be/zD_6Lzw8raE We are also open to consider the candidate in Seattle, or Palo Alto. We are open to hiring candidates to work out of one of the following locations: New York, NY, USA
US, VA, Arlington
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. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. 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! The Search Sourcing and Relevance team parses billions of ads to surface the best ad to show to Amazon shoppers. The team strives to understand customer intent and identify relevant ads that enable them to discover new and alternate products. This also enables sellers on Amazon to showcase their products to customers, which may, at times, be buried deeper in the search results. By showing the right ads to customers at the right time, this team improves the shopper experience, increase advertiser ROI, and improves long-term monetization. This is a talented team of machine learning scientists and software engineers working on complex solutions to understand the customer intent and present them with ads that are not only relevant to their actual shopping experience but also non-obtrusive. This area is of strategic importance to Amazon Retail and Marketplace business, driving long term growth. Key job responsibilities As a Senior Applied Scientist on this team, you will: - Be the technical leader in Machine Learning; lead efforts within this team and across other teams. - 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. - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - 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. About the team 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. 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. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA
US, WA, Seattle
Interested in using the latest, cutting edge machine learning and science to improve the Amazon employee experience? This role provides applied science leadership to the organization that develops and delivers data-driven insights, personalization, and nudges into Amazon's suite of talent management products to help managers, employees, and organizational leaders make better decisions and have better, more equitable outcomes. Key job responsibilities As the Principal Applied Scientist for GTMC SIERRA, you will be responsible for providing scientific thought leadership over multiple applied science and engineering teams. Each of these teams has rapidly evolving and complex demands to define, develop, and deliver scalable products that make work easier, more efficient, and more rewarding for Amazonians. These are some of Amazon’s most strategic technical investments in the people space and support Amazon’s efforts to be Earth’s Best Employer. In this role you will have a significant impact on 1.5 million Amazonians and the communities Amazon serves. You will also play a critical role in the organization's business planning, work closely with senior executives to develop goals and resource requirements, influence our long-term technical and business strategy, and help hire and develop engineering and science talent. You will provide science thought leadership and support to business partners, helping them use the best scientific methods and science-driven tools to solve current and upcoming challenges and deliver efficiency gains in a changing market. About the team Global Talent Management & Compensation (GTMC) SIERRA (Science, Insights, Experience, Research, Reporting & Analytics) is a horizontal, multi-disciplinary organization whose mission is to be a force multiplier for the broader GTMC organization and our key customer cohorts. We accomplish this by using our expertise in data analytics and science, economics, machine learning (ML), UX, I/O psychology, and engineering to build insights and experiences that raise the bar in understanding and shaping decision making at scale by integrating within and across talent journeys as well as through self-service tools and closed loop mechanisms outside of those journeys. Our portfolio of products spans foundational data sources, metrics, and research through to finished features and products that our end-customers interact with on a daily basis. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, VA, Arlington
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to apply economic methods to address business problems. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work in a team setting with individuals from diverse disciplines and backgrounds. They will work with teammates to develop scientific models and conduct the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use reduced-form causal analysis and/or structural economic modeling methods to evaluate the impact of policies on employee outcomes, and examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA
US, WA, Seattle
We are expanding our Global Risk Management & Claims team and insurance program support for Amazon’s growing risk portfolio. This role will partner with our risk managers to develop pricing models, determine rate adequacy, build underwriting and claims dashboards, estimate reserves, and provide other analytical support for financially prudent decision making. As a member of the Global Risk Management team, this role will provide actuarial support for Amazon’s worldwide operation. Key job responsibilities ● Collaborate with risk management and claims team to identify insurance gaps, propose solutions, and measure impacts insurance brings to the business ● Develop pricing mechanisms for new and existing insurance programs utilizing actuarial skills and training in innovative ways ● Build actuarial forecasts and analyses for businesses under rapid growth, including trend studies, loss distribution analysis, ILF development, and industry benchmarks ● Design actual vs expected and other metrics dashboards to assist decision makings in pricing analysis ● Create processes to monitor loss cost and trends ● Propose and implement loss prevention initiatives with impact on insurance pricing in mind ● Advise underwriting decisions with analysis on driver risk profile ● Support insurance cost budgeting activities ● Collaborate with external vendors and other internal analytics teams to extract insurance insight ● Conduct other ad hoc pricing analyses and risk modeling as needed We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | New York, NY, USA | Seattle, WA, USA
US, WA, Seattle
The economics team within Recruiting Engine uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which uses a range of approaches to develop and deliver solutions that measurably achieve this goal. We are looking for an Economist who is able to provide structure around complex business problems, hone those complex problems into specific, scientific questions, and test those questions to generate insights. The ideal candidate will work with various science, engineering, operations and analytics teams to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. She/He/They will produce robust, objective research results and insights which can be communicated to a broad audience inside and outside of Amazon. Ideal candidates will work closely with business partners to develop science that solves the most important business challenges. She/He/They will work well in a team setting with individuals from diverse disciplines and backgrounds. She/He/They will serve as an ambassador for science and a scientific resource for business teams. Ideal candidates will own the development of scientific models and manage the data analysis, modeling, and experimentation that is necessary for estimating and validating the model. They will be customer-centric – clearly communicating scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Seattle, WA, USA