elena2.png
Elena Ehrlich, a principal data scientist at Amazon Web Services, works on, among other things, time-series modeling. It was her work in that area that caught the attention of the National Football League, which led to a new passing metric.

Using data science to help improve NFL quarterback passing scores

Principal data scientist Elena Ehrlich uses her skills to help a wide variety of customers — including the National Football League.

In any given month as a principal data scientist at Amazon Web Services (AWS), Elena Ehrlich might be working on time-series modeling, a computer vision project, a natural language processing problem, and more. Her work within the AWS Professional Services organization involves solving data problems for customers in fields ranging from media to energy to sports.

Spliced binned-Pareto distributions, developed in part by Elena Ehrlich, are flexible enough to handle symmetric, asymmetric, and multimodal distributions, offering a more consistent metric.

Sometimes a customer has a particular model in mind and will consult with Ehrlich's team to build or refine it. Often, though, they are not so far along. They simply have a business problem to solve. Ehrlich works with them anywhere from two months to three years to develop a solution that the client can then maintain going forward.

Ehrlich likes the ability to apply data science across a variety of verticals without having to switch jobs, or even teams.

"Amazon has a large and diverse landscape of customers, so I can gain a lot of different domain knowledge," Ehrlich says. "Each customer's needs are unique, which makes it very interesting, and the challenge is to come up with solutions that can be reused by other customers as well.”

Better predictions for spiky time series

Ehrlich's work with the NFL is one example of science applied to business challenges. Independent of her team's existing work with the league, she and colleagues developed a method for modeling heavy-tailed time series. In these sequences, one can have dramatic, unpredictable spikes: Think extreme rainfall events that shape totals over the course of a year, or a product suddenly going viral, increasing demand.

An NFL Next Gen Stats video screengrab shows Rams quarterback Matthew Stafford preparing to make a pass
The NFL's new Passing Score was developed using Ehrlich's Spliced Binned-Pareto method. It can place a quarterback's performance, such as that of Matthew Stafford, within the context of expected performance across the league.

Many statistical methods that would perform fine on more uniform curves falter when it comes to the noise of heavy-tailed time series. Yet being able to characterize these tails is important. On an EKG, for instance, you must be able to tell whether a peak in heart rate signals disease or simply the beginning of a workout.

Predictive models were not able to reliably pinpoint such anomalies. Over the course of a few months, Ehrlich and Amazon researchers Francois-Xavier Aubet and Laurent Callot developed a solution, which they presented at the 2021 International Conference on Learning Representations' RobustML Workshop.

"If you're seeing an issue from a few customers, then it's worth zooming out, solving it as a research problem, and then going back to examine to which customers this can apply," Ehrlich said.

Related content
In its collaboration with the NFL, AWS contributes cloud computing technology, machine learning services, business intelligence services — and, sometimes, the expertise of its scientists.

Their solution, the Spliced Binned-Pareto method, combines two statistical techniques, the binned and the Pareto distributions. The latter stems from Italian economist Vilfredo Pareto's 80/20 rule: The 1920s idea that 80% of outcomes emerge from 20% of the causes (most of a country's wealth attributable to a fifth of its population, for example). This power-law relationship, when generalized, delivered the Second Theorem of Extreme Value Theory in 1975, which states that any and all distribution tails can be well approximated by a Generalized Pareto Distribution.

The researchers combined this with binned distribution, which discretizes regions within a larger dataset. Their method effectively cordons off and zeroes in on the spikes within a time series, leading to an improved ability to accommodate these extremes and calibrate estimates of them over time, which in turn leads to more accurate heavy-tail predictions.

This work aligned with a request from the NFL. While quarterback ratings exist in various forms, the league wanted a metric to rate passing performance. But a meaningful passing metric had to extend beyond passing yards, touchdowns, and interceptions to reflect the degree of difficulty for those outcomes given the specific play’s circumstances, in order to evaluate the NFL quarterback’s performance.

In January, the National Football League announced its new QB passing score, which addressed the inconsistency across plays, games, weeks, and seasons found in previous scores. A method based on spliced binned-Pareto distributions, developed by Amazon researchers, led to the improved passing metric.

The resulting NFL Passing Score, developed using Ehrlich's Spliced Binned-Pareto method, can place a quarterback's performance within the context of expected performance across the league.

That's because it is capable of estimating that heavy tail — in this case, those exceptional moments in a quarterback's throw — and assign them the proper weight toward the overall score. The NFL debuted the Passing Score early this year, ahead of the Super Bowl. Perhaps not surprisingly, Green Bay Packers quarterback Aaron Rodgers had the highest score.

An early affinity for math

Though the Passing Score project is complete, Ehrlich continues to refine the Spliced Binned-Pareto distribution, among other facets of her work.

"You want to forge a team to be at the leading edge of industry," she says. "Leading edge is determined by how short the lag is between your academic progress and marketplace usage."

Ehrlich has bachelor's and master's degrees in mathematics and a doctorate degree in statistics, all from Imperial College London — a school she chose in part because its program allowed her to focus solely on mathematics from her first year of university.

Related content
Danielle Maddix Robinson's mathematics background helps inform robust models that can predict everything from retail demand to epidemiology.

The predilection toward math and science runs in the family: Ehrlich's father is a mathematician, her mother a physicist. Numbers were a part of childhood, growing up in New Jersey. On skiing trips, she recalls, her father would point out the numbers on chair lifts and challenge her to factor them or shout "prime." If she got them all right, she'd get a candy reward.

"I thought these were fun games," Ehrlich says.

Ehrlich is glad to have had a singular focus on mathematics from undergrad onward.

"My career is where I went to get breadth and width, but it was really helpful to have this depth," she says. "It helps me learn faster when I get to a new domain or application, just having the technical strength."

'Genuinely excited about the problems'

Ehrlich embarked on her PhD, which focused on state-space models with applications for aerospace and missile tracking, thinking she would stay in academia. But as she completed the work in 2014, she knew there was an emerging job market for skills like hers.

"The real world had an appreciation for methodologies that weren't exactly instant," she says. "It takes some time to research good solutions and test out their longevity and experiment their generalizability."

She held research scientist positions at companies including IBM and Winton Capital Management before joining Amazon in 2017. Heading to Seattle headquarters, she prepared for some housekeeping-type meetings. She was surprised to find opportunities to sign up for dev ops and other classes related to Amazon technology. It felt like being back at university, she says, in a good way. The culture reflects this drive to learn; in fact, one of the company’s leadership principles is “learn and be curious.”

Related content
Amazon's Daliana Liu helps others in the field chart their own paths.

"People that come to work here are genuinely excited about the problems," she says. "It makes for more data-driven conversation based on the problem at hand. That intersects with the fact that since Amazon is big with a wide-range of opportunities, it attracts a lot of top talent."

An early project for Ehrlich focused on 21st Century Fox. She developed and implemented an optimized Ad Sales Pricing platform for the company's advertising time spots, horizontally scaling to match potential advertisers with spots across millions of opportunities. Working with sales and engineering teams, she moved the algorithm into production, boosting revenue for Fox.

For students who are interested in a career like Ehrlich's, she recommends starting with first principles and then confirming that understanding with actual projects.

"You should have some corner of theory really well understood. Then iterate between knowing something and implementing it — it doesn't even matter how small," she says. "This is a very fast, but very solid, way to grow."

Research areas

Related content

US, CA, Santa Clara
Amazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Generative AI, Large Language Model (LLM), Natural Language Understanding (NLU), Machine Learning (ML), Retrieval-Augmented Generation, Responsible AI, Agent, Evaluation, and Model Adaptation. As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding. The Science team at AWS Bedrock builds science foundations of Bedrock, which is a fully managed service that makes high-performing foundation models available for use through a unified API. We are adamant about continuously learning state-of-the-art NLP/ML/LLM technology and exploring creative ways to delight our customers. In our daily job we are exposed to large scale NLP needs and we apply rigorous research methods to respond to them with efficient and scalable innovative solutions. At AWS Bedrock, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging AWS resources, one of the world’s leading cloud companies and you’ll be able to publish your work in top tier conferences and journals. We are building a brand new team to help develop a new NLP service for AWS. You will have the opportunity to conduct novel research and influence the science roadmap and direction of the team. Come join this greenfield opportunity! About the team AWS Bedrock Science Team is a part of AWS Utility Computing AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. About the team Diverse Experiences 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.
GB, MLN, Edinburgh
We’re looking for a Machine Learning Scientist in the Personalization team for our Edinburgh office experienced in generative AI and large models. You will be responsible for developing and disseminating customer-facing personalized recommendation models. This is a hands-on role with global impact working with a team of world-class engineers and scientists across the Edinburgh offices and wider organization. You will lead the design of machine learning models that scale to very large quantities of data, and serve high-scale low-latency recommendations to all customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside a science team to delight customers by aiding in recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization. Successful candidates will have strong technical ability, focus on customers by applying a customer-first approach, excellent teamwork and communication skills, and a motivation to achieve results in a fast-paced environment. Our position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. If you are selected, you have the opportunity to make a difference to our business by designing and building state of the art machine learning systems on big data, leveraging Amazon’s vast computing resources (AWS), working on exciting and challenging projects, and delivering meaningful results to customers world-wide. Key job responsibilities Develop machine learning algorithms for high-scale recommendations problems. Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement. Collaborate with software engineers to integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency. Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.
US, WA, Seattle
Here at Amazon, we embrace our differences. We are committed to furthering our culture of diversity and inclusion of our teams within the organization. We’re working on the future. If you are seeking an iterative fast-paced environment where you can drive innovation, apply state-of-the-art technologies to solve extreme-scale real world delivery challenges, and provide visible benefit to end-users, this is your opportunity. Come work on the Amazon Prime Air team We're looking for outstanding scientists and engineers who combine superb technical, research and analytical capabilities with a demonstrated ability architect complex hardware, software, embedded, mobile and mission-critical systems to ensure they can be found compliant to DO-178C. This person must be comfortable working with a team of top-notch software, hardware and applied science Engineers. We’re looking for people who innovate and love solving hard problems. You will work hard, have fun, and of course, make history! Export License Control This position may require a deemed export control license for compliance with applicable laws and regulations. Placement is contingent on Amazon’s ability to apply for and obtain an export control license on your behalf. Key job responsibilities The manager of the High Fidelity Modeling group will lead a group of engineers and scientists that provide computational fluid dynamics modeling, as well as aerodynamic and other surrogate models used in flight simulation of the Prime Air drones.
US, WA, Seattle
Amazon Advertising operates at the intersection of eCommerce and advertising, and is investing heavily in building a world-class advertising business. We are defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long-term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products to improve both shopper and advertiser experience. With a broad mandate to experiment and innovate, we grow at an unprecedented rate with a seemingly endless range of new opportunities. The Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning pipelines, and real-time serving infra to match shoppers’ intent to relevant ads on all devices, for all contexts and in all marketplaces. Through precise estimation of shoppers’ interaction with ads and their long-term value, we aim to drive optimal ads allocation and pricing, and help to deliver a relevant, engaging and delightful ads experience to Amazon shoppers. As the business and the complexity of various new initiatives we take continues to grow, we are looking for talented Applied Scientists to join the team. Key job responsibilities As a Applied Scientist II, you will: * Conduct hands-on data analysis, build large-scale machine-learning models and pipelines * Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production * Run regular A/B experiments, gather data, perform statistical analysis, and communicate the impact to senior management * Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving * Provide technical leadership, research new machine learning approaches to drive continued scientific innovation * Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! In Prime Video READI, our mission is to automate infrastructure scaling and operational readiness. We are growing a team specialized in time series modeling, forecasting, and release safety. This team will invent and develop algorithms for forecasting multi-dimensional related time series. The team will develop forecasts on key business dimensions with optimization recommendations related to performance and efficiency opportunities across our global software environment. As a founding member of the core team, you will apply your deep coding, modeling and statistical knowledge to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on retrieving, cleansing and preparing large scale datasets, training and evaluating models and deploying them to production where we continuously monitor and evaluate. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with complete independence and are often assigned to focus on areas where the business and/or architectural strategy has not yet been defined. You must be equally comfortable digging in to business requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than delivering for our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies to your solutions. If you crave a sense of ownership, this is the place to be.
IN, HR, Gurugram
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced ML systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data 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 like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning team for India Consumer Businesses. Machine Learning, Big Data and related quantitative sciences have been strategic to Amazon from the early years. Amazon has been a pioneer in areas such as recommendation engines, ecommerce fraud detection and large-scale optimization of fulfillment center operations. As Amazon has rapidly grown and diversified, the opportunity for applying machine learning has exploded. We have a very broad collection of practical problems where machine learning systems can dramatically improve the customer experience, reduce cost, and drive speed and automation. These include product bundle recommendations for millions of products, safeguarding financial transactions across by building the risk models, improving catalog quality via extracting product attribute values from structured/unstructured data for millions of products, enhancing address quality by powering customer suggestions We are developing state-of-the-art machine learning solutions to accelerate the Amazon India growth story. Amazon India is an exciting place to be at for a machine learning practitioner. We have the eagerness of a fresh startup to absorb machine learning solutions, and the scale of a mature firm to help support their development at the same time. As part of the India Machine Learning team, you will get to work alongside brilliant minds motivated to solve real-world machine learning problems that make a difference to millions of our customers. We encourage thought leadership and blue ocean thinking in ML. Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, engineers in the use of ML techniques About the team International Machine Learning Team is responsible for building novel ML solutions that attack India first (and other Emerging Markets across MENA and LatAm) problems and impact the bottom-line and top-line of India business. Learn more about our team from https://www.amazon.science/working-at-amazon/how-rajeev-rastogis-machine-learning-team-in-india-develops-innovations-for-customers-worldwide
US, WA, Seattle
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to lead the development of industry-leading technology with multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will lead the development of novel algorithms and modeling techniques to advance the state of the art with multimodal systems. Your work will directly impact our customers in the form of products and services that make use of vision and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate development with multimodal Large Language Models (LLMs) and Generative Artificial Intelligence (GenAI) in Computer Vision. About the team The AGI team has a mission to push the envelope with multimodal LLMs and GenAI in Computer Vision, in order to provide the best-possible experience for our customers.
BR, SP, Sao Paulo
Esta é uma posição de colaborador individual, com base em nosso escritório de São Paulo. Procuramos uma pessoa dinâmica, analítica, inovadora, orientada para a prática e com foco inabalável no cliente. Na Amazon, nosso objetivo é exceder as expectativas dos clientes, garantindo que seus pedidos sejam entregues com máxima rapidez, precisão e eficiência de custo. A determinação da rota de cada pacote é realizada por sistemas complexos, que precisam acompanhar o crescimento acelerado e a complexidade da malha logística no Brasil. Diante desse cenário, a equipe de Otimização de Supply Chain está à procura de um cientista de dados experiente, capaz de desenvolver modelos, ferramentas e processos para garantir confiabilidade, agilidade, eficiência de custos e a melhor utilização dos ativos. O candidato ideal terá sólidas habilidades quantitativas e experiência com conjuntos de dados complexos, sendo capaz de identificar tendências, inovar processos e tomar decisões baseadas em dados, considerando a cadeia de suprimentos de ponta a ponta. Key job responsibilities * Executar projetos de melhoria contínua na malha logística, aproveitando boas práticas de outros países e/ou desenvolvendo novos modelos. * Desenvolver modelos de otimização e cenários para planejamentos logísticos. * Criar modelos de otimização voltados para a execução de eventos e períodos de alta demanda. Automatizar processos manuais para melhorar a produtividade da equipe. * Auditar operações, configurações sistêmicas e processos que possam impactar custos, produtividade e velocidade de entregas. * Realizar benchmarks com outros países para identificar melhores práticas e processos avançados, conectando-os às operações no Brasil. About the team Nosso time é composto por engenheiros de dados, gerentes de projetos e cientistas de dados, todos dedicados a criar soluções escaláveis e inovadoras que suportem e otimizem as operações logísticas da Amazon no Brasil. Nossa missão é garantir a eficiência de todas as etapas da cadeia de suprimentos, desde a primeira até a última milha, ajudando a Amazon a entregar resultados com agilidade, precisão e a um custo competitivo, especialmente em um ambiente de rápido crescimento e complexidade.
IN, KA, Bengaluru
Alexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot, Echo Show, and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Sensitive Content Intelligence (ASCI) team creates policies and builds services and tools through Machine Learning techniques to detect and mitigate sensitive content across Alexa. We are looking for an experienced Senior Applied Scientist to build industry-leading technologies in attribute extraction and sensitive content detection across all languages and countries. A Senior Applied Scientist will be a tech lead for a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in NLP or CV related tasks. You will work in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. We are looking for a leader with strong technical experiences a passion for building scientific driven solutions in a fast-paced environment. You should have good understanding of Artificial Intelligence (AI), Natural Language Understanding (NLU), Machine Learning (ML), Dialog Management, Automatic Speech Recognition (ASR), and Audio Signal Processing where to apply them in different business cases. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience of building large-scale distributed systems to creating reliable, scalable, and high-performance products. In addition to technical depth, you must possess exceptional communication skills and understand how to influence key stakeholders. You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing as a leader, this may be the place for you. Key job responsibilities You'll lead the science solution design, run experiments, research new algorithms, and find new ways of optimizing customer experience. You set examples for the team on good science practice and standards. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. Your work will directly impact the trust customers place in Alexa, globally. You contribute directly to our growth by hiring smart and motivated Scientists to establish teams that can deliver swiftly and predictably, adjusting in an agile fashion to deliver what our customers need. A day in the life You will be working with a group of talented scientists on researching algorithm and running experiments to test scientific proposal/solutions to improve our sensitive contents detection and mitigation. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership. About the hiring group About the team The mission of the Alexa Sensitive Content Intelligence (ASCI) team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, and (3) build customer trust through appropriate interactions on sensitive topics. The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video.
US, WA, Seattle
"We see our customers as invited guests to a party, and we are the hosts. It's our job every day to make every important aspect of the customer experience a little bit better." - Jeff Bezos, Founder & CEO. We didn’t make Amazon a trillion-dollar company, our customers did and we want to ensure that our customers always have a positive experience that keeps them coming back to Amazon. To help achieve this, the Worldwide Defect Elimination (WWDE) team, within Amazon Customer Service, relentlessly focuses on maintaining customer trust by building products that offer appropriate resolutions to resolve issues faced by our customers. WWDE scientists solve complex problems and build scalable solutions to help our customers navigate through issues and eliminate systemic defects to prevent future issues. As an Applied Scientist, your role is pivotal in leveraging advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques to address customer issues at scale. You'll develop innovative solutions that summarize and detect issues, organize them using taxonomy, and pinpoint root causes within Amazon systems. Your expertise will drive the identification of responsible stakeholders and enable swift resolution. Utilizing the latest techniques, you will build an AI ecosystem that can efficiently comb over our billions of customer interactions (using a combination of media). As a part of this role, you will collaborate with a large team of experts in the field and move the state of defect elimination research forward. You should have a knack for leveraging AI to translate complex data insights into actionable strategies and can communicate these effectively to both technical and non-technical audiences. Key job responsibilities - Develop ML/GenAI-powered solutions for automating defect elimination workflows - Implement scalable and efficient scientific solutions in production environments - Design and implement robust metrics to evaluate the effectiveness of ML/AI models - Perform statistical analyses and statistical tests, including hypothesis testing and A/B testing - Recognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: - Medical, Dental, and Vision Coverage - Maternity and Parental Leave Options - Paid Time Off (PTO) - 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply