How to make AI better at reading comprehension

AI models exceed human performance on public data sets; modified training and testing could help ensure that they aren’t exploiting short cuts.

Question answering through reading comprehension is a popular task in natural-language processing. It’s a task many people know from standardized tests: a student is given a passage and questions based on the passage — say, an article on William the Conqueror and the question “When did William invade England?” The student reads the passage and learns that the answer is 1066. In natural-language processing, we aim to teach machine learning models to do the same thing.

Historical documentation on a computer screen with the date highlighted
In natural-language understanding, reading comprehension involves finding an excerpt from a text that can serve as an answer to a question about that text.
Credit: Glynis Condon

In recent years, question-answering models have made a lot of progress. In fact, models have started outperforming human baselines on public leaderboards such as SQuAD 2.0.

Are the models really learning question answering, or are they learning heuristics that work only in some circumstances? We investigate this question in our paper “What do models learn from question answering datasets?”, which we’re presenting at the Conference on Empirical Methods in Natural Language Processing (EMNLP).

In this paper, we subject question-answering models built atop the popular BERT linguistic model to a variety of simple yet informative attacks. We identify shortcomings that cast doubt on the idea that models are really outperforming humans. In particular, we find that

(1) Models don’t generalize well

A student who is a good critical reader should be able to answer questions about a variety of articles. A student who can answer questions about William the Conqueror but not Julius Caesar may not have learned reading comprehension —just information about William the Conqueror.

Graph that shows the performance of a question-answering model trained on SQuAD evaluated across multiple datasets.
This graph shows the performance of a question-answering model trained on SQuAD and evaluated across five other datasets. While the model does well on its own test set (75.6), its performance is lower on other data sets. 

Question-answering models do not generalize well across data sets. A model that does well on the SQuAD data set doesn’t do well on the Natural Questions data set, even though both contain questions about Wikipedia articles. This suggests that models can solve individual data sets without necessarily learning reading comprehension more generally.

(2) Models take short cuts

When testing question-answering models, we assume that high performance means good understanding of the subject. But tests can be flawed. If a student takes a multiple-choice test where every answer is “C”, it’s hard to judge whether the student really understood the material or exploited the flaw. Similarly, models may be picking up on biases in test questions that let them arrive at the correct answer without doing reading comprehension. 

To probe this, we conducted three experiments. The first was a modification at training time: we corrupted training sets by replacing correct answers with incorrect answers — for instance, “Q: ‘When did William invade England?’ A: ‘William is buried in Caen’”. 

The other two were modifications at test time. In one, we shuffled the sentences in the input articles so that they no longer formed coherent paragraphs. In the other, we gave models incomplete questions (“When did William?”, “When?”, or no words at all). 

In all these experiments, the models were suspiciously robust, continuing to return correct answers. This means that they didn’t need to do reading comprehension at training time or at test time to understand the structure of the articles or be asked the full question.

How can this be? It turns out that some questions in some data sets can be answered trivially. In our experiments, for example, one model was just answering all “who” questions with the first proper name in the passage. Simple rules like this can get us to almost 40% of current model baselines.

(3) Models aren’t prepared to handle variations

Graph that shows the performance of a Natural Questions model against various attacks.
This graph shows the performance of a Natural Questions model against various attacks: 50% corrupt, in which half the labeled answers in the training data are wrong; shuffled context, in which the sentences of the test excerpts are out of order; no question, in which the questions in the test data are incomplete; filler words, in which fillers such as “really” and “actually” are added in a syntactically correct way; and negation, in which the negative of the test question is substituted for the positive (“When didn’t William invade England?”). Where we would expect much lower performance in the first three cases, we instead see surprising robustness. Where we would expect to see little change with filler words, we see a drop of almost 7 F1 points. On the negation task, the model answers 94% of questions the same way it did when they were positively framed.

A student should understand that “When did William invade England?”, “When did William march his army into England?”, and “When was England invaded by William?” are all asking the same question. But models can still struggle with this.

We conducted two experiments where we ran variations of questions through reading comprehension models. First, we tried the very simple change of adding filler words to questions (“When did William really invade England?”). In principle, this should have no effect on performance, but we found that it reduces the model’s F1 score — a metric that factors in both false positives and false negatives — by up to 8%. 

Next, we added negation (“When didn’t William invade England?”) to see if models understood the difference between positive and negative questions. We found that models ignore negation up to 94% of the time and return the same answers they would to positive questions.

Conclusions

Our experiments suggest that models are learning short cuts rather than performing reading comprehension. While this is disappointing, it can be fixed. We believe that following these five suggestions can lead to better question-answering data sets and evaluation methods in the future:

  • Test for generalizability: Report performance across multiple relevant data sets to make sure a model is not just solving a single data set;
  • Challenge the models: Discard questions that can be solved trivially — for example, by always returning the first proper noun;
  • Good performance does not guarantee understanding: Probe data sets to ensure models are not taking short cuts;
  • Include variations: Add variations to existing questions to check model flexibility;
  • Standardize data set formats: Consider following a standard format when releasing new data sets, as this makes cross-data-set experimentation easier. We offer some help in this regard by releasing code that converts the five data sets in our experiments into a shared format.

Related content

US, CA, Santa Clara
Job summaryAmazon 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), Natural Language Understanding (NLU), Dialog management, conversational AI and Machine Learning (ML).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.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, NY, New York
Job summaryAmazon 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), Natural Language Understanding (NLU), Dialog management, conversational AI and Machine Learning (ML).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.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, CA, Santa Clara
Job summaryAWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building automated ML solutions for planetary-scale sustainability and geospatial applications. Our team's mission is to develop ready-to-use and automated solutions that solve important sustainability and geospatial problems. We live in a time wherein geospatial data, such as climate, agricultural crop yield, weather, landcover, etc., has become ubiquitous. Cloud computing has made it easy to gather and process the data that describes the earth system and are generated by satellites, mobile devices, and IoT devices. Our vision is to bring the best ML/AI algorithms to solve practical environmental and sustainability-related R&D problems at scale. Building these solutions require a solid foundation in machine learning infrastructure and deep learning technologies. The team specializes in developing popular open source software libraries like AutoGluon, GluonCV, GluonNLP, DGL, Apache/MXNet (incubating). Our strategy is to bring the best of ML based automation to the geospatial and sustainability area.We are seeking an experienced Applied Scientist for the team. This is a role that combines science knowledge (around machine learning, computer vision, earth science), technical strength, and product focus. It will be your job to develop ML system and solutions and work closely with the engineering team to ship them to our customers. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. You are also expected to work closely with other applied scientists and demonstrate Amazon Leadership Principles (https://www.amazon.jobs/en/principles). Strong technical skills and experience with machine learning and computer vision are required. Experience working with earth science, mapping, and geospatial data is a plus. Our customers are extremely technical and the solutions we build for them are strongly coupled to technical feasibility.About the teamInclusive Team CultureAt AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.Interested in this role? Reach out to the recruiting team with questions or apply directly via amazon.jobs.
US, CA, Santa Clara
Job summaryAWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building automated ML solutions for planetary-scale sustainability and geospatial applications. Our team's mission is to develop ready-to-use and automated solutions that solve important sustainability and geospatial problems. We live in a time wherein geospatial data, such as climate, agricultural crop yield, weather, landcover, etc., has become ubiquitous. Cloud computing has made it easy to gather and process the data that describes the earth system and are generated by satellites, mobile devices, and IoT devices. Our vision is to bring the best ML/AI algorithms to solve practical environmental and sustainability-related R&D problems at scale. Building these solutions require a solid foundation in machine learning infrastructure and deep learning technologies. The team specializes in developing popular open source software libraries like AutoGluon, GluonCV, GluonNLP, DGL, Apache/MXNet (incubating). Our strategy is to bring the best of ML based automation to the geospatial and sustainability area.We are seeking an experienced Applied Scientist for the team. This is a role that combines science knowledge (around machine learning, computer vision, earth science), technical strength, and product focus. It will be your job to develop ML system and solutions and work closely with the engineering team to ship them to our customers. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. You are also expected to work closely with other applied scientists and demonstrate Amazon Leadership Principles (https://www.amazon.jobs/en/principles). Strong technical skills and experience with machine learning and computer vision are required. Experience working with earth science, mapping, and geospatial data is a plus. Our customers are extremely technical and the solutions we build for them are strongly coupled to technical feasibility.About the teamInclusive Team CultureAt AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.Interested in this role? Reach out to the recruiting team with questions or apply directly via amazon.jobs.
US, WA, Seattle
Job summaryHow can we create a rich, data-driven shopping experience on Amazon? How do we build data models that helps us innovate different ways to enhance customer experience? How do we combine the world's greatest online shopping dataset with Amazon's computing power to create models that deeply understand our customers? Recommendations at Amazon is a way to help customers discover products. Our team's stated mission is to "grow each customer’s relationship with Amazon by leveraging our deep understanding of them to provide relevant and timely product, program, and content recommendations". We strive to better understand how customers shop on Amazon (and elsewhere) and build recommendations models to streamline customers' shopping experience by showing the right products at the right time. Understanding the complexities of customers' shopping needs and helping them explore the depth and breadth of Amazon's catalog is a challenge we take on every day. Using Amazon’s large-scale computing resources you will ask research questions about customer behavior, build models to generate recommendations, and run these models directly on the retail website. You will participate in the Amazon ML community and mentor Applied Scientists and software development engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and the retail business and you will measure the impact using scientific tools. We are looking for passionate, hard-working, and talented Applied scientist who have experience building mission critical, high volume applications that customers love. You will have an enormous opportunity to make a large impact on the design, architecture, and implementation of cutting edge products used every day, by people you know.Key job responsibilitiesScaling state of the art techniques to Amazon-scaleWorking independently and collaborating with SDEs to deploy models to productionDeveloping long-term roadmaps for the team's scientific agendaDesigning experiments to measure business impact of the team's effortsMentoring scientists in the departmentContributing back to the machine learning science community
US, NY, New York
Job summaryAmazon Web Services is looking for world class scientists to join the Security Analytics and AI Research team within AWS Security Services. This group is entrusted with researching and developing core data mining and machine learning algorithms for various AWS security services like GuardDuty (https://aws.amazon.com/guardduty/) and Macie (https://aws.amazon.com/macie/). In this group, you will invent and implement innovative solutions for never-before-solved problems. If you have passion for security and experience with large scale machine learning problems, this will be an exciting opportunity.The AWS Security Services team builds technologies that help customers strengthen their security posture and better meet security requirements in the AWS Cloud. The team interacts with security researchers to codify our own learnings and best practices and make them available for customers. We are building massively scalable and globally distributed security systems to power next generation services.Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop and enable them to take on more complex tasks in the future.A day in the lifeAbout the hiring groupJob responsibilities* Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment.* Collaborate with software engineering teams to integrate successful experiments into large scale, highly complex production services.* Report results in a scientifically rigorous way.* Interact with security engineers, product managers and related domain experts to dive deep into the types of challenges that we need innovative solutions for.
US, MA, North Reading
Job summaryAre you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers who work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling, and fun.Amazon.com empowers a smarter, faster, more consistent customer experience through automation. Amazon Robotics automates fulfillment center operations using various methods of robotic technology including autonomous mobile robots, sophisticated control software, language perception, power management, computer vision, depth sensing, machine learning, object recognition, and semantic understanding of commands. Amazon Robotics has a dedicated focus on research and development to continuously explore new opportunities to extend its product lines into new areas.This role is a 3-month internship to join AR full-time (40 hours/week) from May 2023 to August 2023. Amazon Robotics internships opportunities will be based out of the Greater Boston Area in our two state-of-the-art facilities in Westborough, MA and North Reading, MA. Both campuses provide a unique opportunity to have direct access to robotics testing labs and manufacturing facilities.About the teamWe are seeking data scientist interns to help us analyze data, quantify uncertainty, and build machine learning models to make quick prediction.
US, WA, Seattle
Job summaryThis role may be located in Seattle, Irvine, San Francisco or New York City and will require a minimum of 10% travel for in office or offsite meetings.The Games Growth Adverting team is seeking an exceptional Applied Scientist to lead the foundation of a disruptive advertising system set to revolutionize customer acquisition for Game Developers. This is a great opportunity to innovate on the entire breadth of the AdTech funnel, working in close collaboration with multiple science teams across Amazon Ads. You will lead the building of a platform that delivers world-class optimization for price, relevance, and reach; enabling marketers to drive user acquisition for console, mobile, and PC games. The ideal candidate will have a background in NLP, IR, Personalization or AdTech in production. Key job responsibilitiesLead the design and development of large scale, performant machine learning systems in production for various AdTech use casesInfluence the product roadmap using data backed experimentsUse prior background in NLP/IR/large scale deep learning systems to explore the frontiers of supervised/semi-supervised learning enabling generalization across multiple use casesEstablish scalable, efficient, and automated processes for large scale model development, validation, and implementationMentor junior scientists on the teamHave fun working on ground breaking technology with people just as passionate about their work as you!A day in the lifeWhen you join The Games Growth Advertising Team, your creative partners will be some of the best from the games industry. They have built and published hundreds of the most successful video games in history. Your game studio partners are excited to build the next hit games, but they need your help. We are an Amazon team that helps game developers reach more customers who will love their games. It’s always Day-1 at Amazon, but it’s particularly Day-1 in our game growth business, and we’re excited to see what you can do. Inclusive Culture, Work/Life Balance, & Career GrowthWe embrace our differences and are committed to furthering our culture of inclusion. We offer ten employee-led affinity groups with 190 global chapters, innovative benefits, and annual and ongoing learning experiences (including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences). Our team also puts a high value on work-life balance and offers flexible working hours. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. Additionally, our team is dedicated to supporting you with mentorship and pathways for ongoing development. We have a broad mix of experience levels and tenures, and are building an environment that celebrates knowledge sharing and promotes career choice.About the teamGame Growth Advertising applies the principles of Amazonian culture to the world of gaming user acquisition. We believe in a future where everyone is a gamer and everyone can create, compete, collaborate and connect through games, and we are looking for the right people to help us build that future. We want to be the user acquisition tool of choice for game developers across hardware platforms and gaming genres at scale. Using large scale data and state-of-the-art machine learning techniques, we are excited about shaping the future of programmatic advertising.
DE, BW, Tuebingen
Job summaryAre you passionate about solving real-world challenges using cutting-edge artificial intelligence (AI) and machine learning (ML) technology? Would you like to work with some of the best scientists in the field to transition new AI/ML technologies from the research stage into products?As a Data Scientist in our Lablet, you will be working on cutting edge projects in the intersection of computer vision, graphs, fairness, and causal inference. You will be part of an ambitious team of scientists and software engineers with the goal of solving customer problems at scale. You and your team will work backwards from customer problems, and collaborate with other AWS service teams to develop proof of concepts and transition promising new technology into products.Key job responsibilitiesAs a Data Scientist in the AWS Lablet, you will work backwards from real-world customer problems and prototype, develop and productionize innovative ML techniques to help add business value. You will build tools that accelerate development cycles and facilitate maintenance. You will stay up to date with the state-of-the-art ML research and continuously experiment with new techniques in order to keep pushing the boundaries of what is possible. You will advise scientists on architecture, design and technical choices, and promote engineering excellence within the research team.A day in the lifeWe at AWS value individual expression, respect different opinions, and work together to create a culture where each of us is able to contribute fully. Our unique backgrounds and perspectives strengthen our ability to achieve Amazon's mission of being Earth's most customer-centric company.About the teamThe AWS Lablet team and its scientific head Bernhard Schoelkopf are located at the Tubingen site in Germany. Lablets We decided to start Lablets in a meeting with AWS leadership in late 2018 in order to address two needs: (1) tackle hard AI science problems that do not have an immediate product impact yet may present big opportunities for our business in the future, and (2) display AI leadership by top publications visible to AWS customers whose choice of cloud platform is influenced by perceived AI strength as a way to futureproof their choice. We did so with the expectation that (1) and (2) will both require and enable us to attract talent that significantly raises the bar in terms of AI strength. Lablets provide the environment that top AI/ML talent expects while avoiding the trap of a large centralized research lab. By co-locating with outstanding academic ML centers, they enable the type of transformational work and academic visibility that top scientists deliver without copying large industrial research labs that try to build critical academic mass in a less frugal manner, often have limited impact or get academically stale over time once the influx of new top talent stops.Lablets attract top ML talent and enable some of our strongest scientists to do long-term science projects. We aspire to couple the possibility to have real-world customer impact, where Amazon excels, with the academic reputation of Google Brain/Deepmind/FAIR. We recruit scientists who are free in their research within the (well chosen) fields for which we recruit them. We seek to attract and empower scientists who want to shape the ongoing AI revolution. For those scientists, customer impact is a strong benefit.Lablets Solution LabTo further strengthen the transition mechanism over the next two years, we will equip our lablets with LSL (Lablets Solution Lab) teams. We will staff each of these teams with 2-3 AS/MLE/DS and a PMT. Each LSL team is close to its Lablet’s research activities and deeply understands its potential. Its mission is to work closely with customer-facing organizations like SA, MLSL, PMs from other organizations, and customer teams to propose and prototype solutions to customer problems, leveraging Lablets research. To deliver on its mission, the LSL team acts like a specialized ML Solutions Lab (MLSL) to develop small PoCs with representative customers, mainly with internal customers. In addition, the LSL team is responsible for producing PRFAQs with the goal of starting technology deployment projects for the most promising solutions.If you are interested in joining our team please contact yjadidi@.
US, CA, Cupertino
Job summaryThe retail pricing science and research group is a team of scientists who design and implement the analytics powering pricing for Amazon’s on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon’s goods and services are aligned with Amazon’s corporate goals.We are seeking an applied scientist to help envision, design and build the next generation of retail pricing capabilities. You will work at the intersection of economic theory, statistical inference, machine learning and optimization theory to design new methods and pricing strategies to deliver game changing value to our customers. Key job responsibilitiesThe Applied Scientist will partner with senior scientists on the team, the product managers, and the engineers to develop ML models and solutions for our business problems. They build scalable prototypes and design the right simulation and metrics to examine their efficacy. They will represent and advocate their models to the leaders in our organization. A day in the lifeDiscussions with other scientists, as well as with product managers and tech leaders to understand the business problemBrainstorming with other scientists to design the right model for the problem in handDeep dive into the data and find efficient ways to collect and use itModeling and creating working prototypesAnalyze the results and review with partnersPresent journal quality research in Internal and External science forumsAbout the teamThe pricing research group is a team of ML scientists and economists who design and implement the analytics powering pricing for Amazon’s on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon’s goods and services are aligned with Amazon’s corporate goals.