https://www.amazon.science/blog/new-method-identifies-the-root-causes-of-statistical-outliers * Research areas + Automated reasoning + Cloud and systems + Computer vision + Conversational AI / Natural-language processing + Economics + Information and knowledge management + Machine learning + Operations research and optimization + Quantum technologies + Robotics + Search and information retrieval + Security, privacy, and abuse prevention + Sustainability + Automated reasoning + Cloud and systems + Computer vision + Conversational AI / Natural-language processing + Economics + Information and knowledge management + Machine learning + Operations research and optimization + Quantum technologies + Robotics + Search and information retrieval + Security, privacy, and abuse prevention + Sustainability * Blog * News and features + Awards and recognitions + Awards and recognitions * Publications * Conferences * Collaborations + Academics at Amazon + Alexa Prize + Amazon Research Awards + Amazon SURE + Academics at Amazon + Alexa Prize + Amazon Research Awards + Amazon SURE * Careers + Internships + Working at Amazon + Internships + Working at Amazon [] Feedback Follow Us * twitter * instagram * youtube * facebook * linkedin Menu amazon-science-logo.svg * Research areas + Automated reasoning + Cloud and systems + Computer vision + Conversational AI / Natural-language processing + Economics + Information and knowledge management + Machine learning + Operations research and optimization + Quantum technologies + Robotics + Search and information retrieval + Security, privacy, and abuse prevention + Sustainability + Automated reasoning + Cloud and systems + Computer vision + Conversational AI / Natural-language processing + Economics + Information and knowledge management + Machine learning + Operations research and optimization + Quantum technologies + Robotics + Search and information retrieval + Security, privacy, and abuse prevention + Sustainability * Blog * News and features + Awards and recognitions + Awards and recognitions * Publications * Conferences * Collaborations + Academics at Amazon + Alexa Prize + Amazon Research Awards + Amazon SURE + Academics at Amazon + Alexa Prize + Amazon Research Awards + Amazon SURE * Careers + Internships + Working at Amazon + Internships + Working at Amazon [] Feedback Search [ ] Submit Search Machine learning New method identifies the root causes of statistical outliers Amazon ICML paper proposes information-theoretic measurement of quantitative causal contribution. By Kailash Budhathoki, Patrick Blobaum July 19, 2022 Share Share * Copy link * Email * Twitter * LinkedIn * Facebook * WhatsApp * Reddit * QZone * Sina Weibo Conference * ICML 2022 Related publications * Causal structure-based root cause analysis of outliers Outliers are rare observations where a system deviates from its usual behavior. They arise in many real-world applications (e.g., medicine, finance) and present a greater demand for explanation than ordinary events. How can we identify the "root causes" of outliers once they are detected? The problem of outliers is one of the oldest problems in statistics. It has been the subject of academic investigation for more than a century. Although a lot has been done on detecting outliers, a formal way to define the "root causes" of outliers has been lacking. This week, at the International Conference on Machine Learning (ICML ), we are presenting our work on identifying the root causes of outliers. Our first task was to introduce a formal definition of "root cause", because we were not able to find one in the academic literature. Root-cause analysis.png Related content AWS contributes novel causal machine learning algorithms to DoWhy New features go beyond conventional effect estimation by attributing events to individual components of complex systems. Our definition includes a formalization of the quantitative causal contribution of each of the root causes of an observed outlier. In other words, the contribution describes the degree to which a variable is responsible for the outlier event. This also relates to philosophical questions; even the purely qualitative question of whether an event is an "actual cause" of others is an ongoing debate among philosophers. Our approach is based on graphical causal models, a formal framework developed by Turing Award winner Judea Pearl to model cause-effect relationships between variables in a system. It has two key ingredients. The first is a causal diagram, which visually represents the cause-effect relationships among observed variables, with arrows from the nodes representing causes to the nodes representing effects. The second is a set of causal mechanisms, which describe how the values of each node are generated from the values of its parents (i.e., direct causes) in the causal diagram. Imagine, for instance, a retail website powered by distributed web services. A customer experiences an unusually slow loading time. Why? Is it a slow database in the back end? A malfunctioning buying service? Dependencies, latency, questions.png At left, we have the dependencies between the distributed web services that power a simple hypothetical retail website. In the middle, a customer (with ID 5) experiences a very slow loading time. Our goal is to identify its root causes among the distributed services (right). There exist many outlier detection algorithms. To identify the root causes of outliers detected by one of these algorithms, we first introduce an information-theoretic (IT) outlier score, which probabilistically calibrates existing outlier scores. Our outlier score relies on the notion of the tail probability -- the probability that a random variable exceeds a threshold value. The IT outlier score of an event is the negative logarithm of the event's tail probability under some transformation. It is inspired by Claude Shannon's definition of the information content of a random event in information theory. The lower the likelihood of observing events more extreme than the event in question, the more information that event carries, and the larger its IT outlier score. Probabilistic calibration also renders IT outlier scores comparable across variables with different dimension, range, and scaling. Counterfactuals To attribute the outlier event to a variable, we ask the counterfactual question "Would the event not have been an outlier had the causal mechanism of that variable been normal?" The counterfactuals are the third rung on Pearl's ladder of causation and hence require functional causal models (FCMs) as the causal mechanisms of variables. Conditioning set.gif Related content Determining causality in correlated time series New method goes beyond Granger causality to identify only the true causes of a target time series, given some graph constraints. In an FCM, each variable X[j] is a function of its observed parents PA[j] (with direct arrows to X[j]) in the causal diagram and an unobserved noise variable N[j]. As root nodes -- those without observed parents -- have only noise variables, the joint distribution of noise variables gives rise to the stochastic properties of observed variables. The unobserved noise variables play a special role: we can think of N [j] as a random switch that selects a deterministic function (or mechanism) from a set of functions F[j] defined from direct causes PA [j] to their effect X[j]. If, instead of fixing the value of the noise term N[j], we set it to random values drawn from some distribution, then the functions from the set F[j][ ]are also selected at random, and we can use this procedure to assign normal deterministic mechanisms to Xj. Although this randomization operation might seem infeasible if we think of the noise variable as something not under our control -- and even worse, not even observable -- we can interpret it as an intervention on the observed variable. Causal circuits.png On the left, for the observed pair (x[j], pa[j]) of variable X[j] and its parents PA[j], the deterministic mechanism f[j]^(1) of variable X [j] is identified by the noise value (N[j] = 1) corresponding to the pair (x[j], pa[j]). In the middle, a different value of noise (N[j ]= n) identifies a counterfactual deterministic mechanism f[j](n). On the right, by drawing random samples of the noise term N[j] according to some distribution, we assign "normal" deterministic mechanisms to X[j]. Normal causal replacement.png To attribute the outlier event x[n] of target variable X[n] to a variable X[j], we first replace the deterministic mechanism of X[j][ ]by normal causal mechanisms (the orange background indicates the replacement). Then we measure the impact of this replacement on the log tail probability of the outlier event. To attribute the outlier event x[n] (of target variable X[n]) to a variable X[j], we first replace the deterministic mechanism corresponding to its observation x[j] by normal mechanisms. The impact of this replacement on the log tail probability defines the contribution of X[j] to the outlier event. In particular, the contribution measures the factor by which replacing the causal mechanism of X[j] with normal mechanisms (by drawing random samples of the noise N[j]) decreases the likelihood of the outlier event. But the contribution computed this way depends on the order in which we replace the causal mechanisms. This dependence on ordering introduces arbitrariness into the attribution procedure. To get rid of the dependence on the ordering of variables, we take the average contribution over all orderings, which is also the idea behind the Shapley value approach in game theory. The Shapley contributions sum up to the IT outlier score of the outlier event. To get a high-level idea of how our approach works, consider again the retail-website example mentioned above. Dependencies between web services are typically available as a dependency graph. By inverting the arrows in the dependency graph, we obtain the causal graph of latencies of services. From training samples of observed latencies, we learn the causal mechanisms. The causal mechanisms may also be established directly using subject matter expertise. Our approach uses those to attribute the slow loading time for the specific client to its most likely root causes among the web services. Causal graph_contributions.png On the left, we have the causal graph of latencies of services, which is obtained by inverting the arrows of the dependency graph of services. By learning the causal mechanisms of nodes from training data, our approach yields the contributions of each node to the outlier event -- here, the unusually high latency of the web service. As the Shapley contributions sum up to the IT outlier score of the outlier event, we are able to show the relative contribution of ancestors -- here, the services. If you would like to apply our approach to your use case, the implementation is available in the "gcm" package in the Python DoWhy library. To get started quickly, you can check out our example notebook. Research areas * Machine learning Tags * Causal inference * Causal analysis * ICML Conference * ICML 2022 Related publications * Causal structure-based root cause analysis of outliers About the Author Kailash Budhathoki Kailash Budhathoki is an applied scientist at Amazon. Patrick Blobaum Patrick Blobaum is a senior applied scientist with Amazon Web Services. Related content * Root-cause analysis.png AWS contributes novel causal machine learning algorithms to DoWhy Peter Gotz, Kailash Budhathoki May 31, 2022 New features go beyond conventional effect estimation by attributing events to individual components of complex systems. Machine learning * Demand contribution.png Explaining changes in real-world data Kailash Budhathoki April 26, 2021 New method identifies which causal factors contribute most to observed changes in probability distributions. Operations research and optimization * Conditioning set.gif Determining causality in correlated time series Atalanti Mastakouri, Dominik Janzing August 18, 2021 New method goes beyond Granger causality to identify only the true causes of a target time series, given some graph constraints. Machine learning Work with us See more jobs See more jobs Applied Scientist- AWS AI 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. Senior Data Scientist IN, TS, Hyderabad Job summaryAre you excited about driving business growth for millions of sellers by applying Machine Learning? Do you thrive in a fast-moving, large-scale environment that values data-driven decision making and sound scientific practices? We are looking for experienced data scientists to build sophisticated decision making systems that help Amazon Marketplace Sellers to grow their businesses.Amazon Marketplace enables sellers to reach hundreds of millions of customers and provides sellers the tools and services needed to make e-commerce simple, efficient and successful. Our team builds the core intelligence, insights, and algorithms that power a range of products used by millions of sellers. We are tackling large-scale, challenging problems such as helping sellers to prioritise business tasks by bringing together petabytes of data from sources across Amazon.You will be proficient with creating value out of data by formulating questions, analysing vast amounts of data, and communicating insights effectively to audience of varied backgrounds. In addition, you'll contribute to online experiments, build machine learning pipelines and personalised data products.To know more about Amazon science, Please visit https://www.amazon.scienceKey job responsibilities* Collaborate with domain experts, formulate questions, gather, process and analyse petabytes of data to unearth reliable insights* Design & execute experiments and analyze experimental results* Communicate insights effectively to audience of a wide range of backgrounds* Formulate relevant prediction problems and solve them by developing machine learning models* Partner with data engineering teams to improve quality of data assets, metrics and insights* Leverage industry best practices to establish repeatable science practices, principles & processes Sr. Applied Science Manager - Seattle/NY/HQ2, Sponsored Product Search Sourcing and Relevance US, VA, Arlington Job summaryAmazon 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. 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. 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.Sponsored Products helps merchants, retail vendors, and brand owners succeed via native advertising that grows incremental sales of their products sold through Amazon. The Sponsored Products Ad Marketplace organization optimizes the systems and ad placements to match advertiser demand with publisher supply using a combination of machine learning, big data analytics, ultra-low latency high-volume engineering systems, and quantitative product focus. Our goals are to help buyers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and to build a major, sustainable business that helps Amazon continuously innovate on behalf of all customers.We are seeking a Sr. Applied Science Manager who has a solid background in applied Machine Learning and AI, deep passion for building data-driven products, ability to communicate data insights and scientific vision, and has a proven track record of leading both applied scientists and software engineers to execute complex projects and deliver business impacts.In this team, Machine Learning and Deep Learning technologies including Semantic Retrieval, Natural Language Processing (NLP), Information Extraction, Image Understanding, Learning to Rank are used to match shoppers' search queries to ads with per impression prediction models that run in real-time with tight latency budgets. Models are trained using self-supervised techniques, transfer learning, and supervised training using labeled datasets. Knowledge distillation and model compression techniques are used to optimize model performance for production serving.The Senior Manager role will lead science and engineering efforts in these areas for Amazon Search pages WW. The person in this role is responsible for: maintaining the consistent and long term reliability for the models and the delivery services that power them, managing diverse teams across multiple domains, and collaborating cross-functional with other senior decision makers. Our critical LPs for this role are Think Big, Are Right A lot, and Earns Trust. What is key is that the leader will need a dynamic mindset to build systems that are flexible and will scale.In this role, you will:* Lead a group of both applied scientists and software engineers to deliver machine-learning and AI solutions to production.* Advance team's engineering craftsmanship and drive continued scientific innovation as a thought leader and practitioner.* Develop science and engineering roadmap, run Sprint/quarter and annual planning, and foster cross-team collaboration to execute complex projects.* Perform hands-on data analysis, build machine-learning models, run regular A/ B tests, and communicate the impact to senior management.* Hire and develop top talents, provide technical and career development guidance to both scientists and engineers in the organization.Locations: Seattle, WA; New York, NY; Arlington, VA Senior Data Scientist US, WA, Seattle Job summaryAmazon Sub-Same-Day Supply Chain team is looking for an experienced and motivated Senior Data Scientist to generate data-driven insights influencing the long term SSD supply chain strategy, build the necessary predictive models, optimization algorithms and customer behavioral segments allowing us to discover and build the roadmap for SSD to enable operational efficiency and scale.Key job responsibilitiesWork with product managers, engineers, other scientists, and leadership to identify and prioritize complex problems.Translate business problems into specific analytical questions and form hypotheses that can be answered with available data using scientific methods or identify additional data needed in the master datasets to fill any gapsDesign, develop, and evaluate highly innovative statistics and ML modelsGuide and establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementationProactively seek to identify business opportunities and insights and provide solutions to shape key business processes and policies based on a broad and deep knowledge of Amazon data, industry best-practices, and work done by other teams.A day in the lifeIn this role, you will be a technical expert with significant scope and impact. You will work with Product Managers, Business Engineers, and other Scientists, to deeply understand SSDs current optimization strategy while benchmarking against industry best practices and standards to gain insights that will drive our roadmap. A successful Data Scientist will have extreme bias for action needed in a startup environment, with outstanding leadership skills, proven ability to build and manage medium-scale modeling projects, identify data requirements, build methodology and tools that are statistically grounded. It will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term scientific solutions. We are seeking someone who can thrive in a fast-paced, high-energy and fun work environment where we deliver value incrementally and frequently. We value highly technical people who know their subject matter deeply and are willing to learn new areas. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career.About the teamAmazon's Sub-Same Day (SSD) delivery program is designed to get customers their items as fast as possible - currently in as quickly as five hours. With ultra-fast delivery becoming increasingly important, we are looking for an experienced Senior Data Scientist to help us benchmark against industry standards to uncover insights to improve and optimize the long term supply chain strategy for Amazons Sub-Same-Day business. Research Scientist II, Funnel Science and Analytics US, WA, Seattle Job summaryWorkforce Staffing (WFS) brings together the workforce powering Amazon's ability to delight customers: the Amazon Associate. With over 1M hires, WFS supports sourcing, hiring, and developing the best talent to work in our fulfillment centers, sortation centers, delivery stations, shopping sites, Prime Air locations, and more.WFS' Funnel Science and Analytics team is looking for a Research Scientist. This individual will be responsible for conducting experiments and evaluating the impact of interventions when conducting experiments is not feasible. The perfect candidate will have the applied experience and the theoretical knowledge of policy evaluation and conducting field studies.Key job responsibilitiesAs a Research Scientist (RS), you will do causal inference, design studies and experiments, leverage data science workflows, build predictive models, conduct simulations, create visualizations, and influence science and analytics practice across the organization.Provide insights by analyzing historical data from databases (Redshift, SQL Server, Oracle DW, and Salesforce).Identify useful research avenues for increasing candidate conversion, test, and create well written documents to communicate to technical and non-technical audiences.About the teamFunnel Science and Analytics team finds ways to maximize the conversion and early retention of every candidate who wants to be an Amazon Associate. By focusing on our candidates, we improve candidate and business outcomes, and Amazon takes a step closer to being Earth's Best Employer. Applied Scientist- AWS AI 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. Applied Scientist US, WA, Seattle Job summaryAmazon's Weblab team enables experimentation at massive scale to help Amazon build better products for customers. A/B testing is in Amazon's DNA and we're at the core of how Amazon innovates on behalf of customers. We are seeking a skilled Applied Scientist to help us build the future of experimentation systems at Amazon.About you:You have an entrepreneurial spirit and want to make a big impact on Amazon and its customers. You are excited about cutting-edge research on unsupervised learning, graph algorithms, and causal inference in the intersection between Machine Learning, Statistics, and Econometrics. You enjoy building massive scale and high performance systems but also have a bias for delivering simple solutions to complex problems. You're looking for a career where you'll be able to build, to deliver, and to impress. You challenge yourself and others to come up with better solutions. You develop strong working relationships and thrive in a collaborative team environment.About us together:We're going to help Amazon make better long term decisions by designing and delivering A/B-testing systems for long-term experiments, and by using these systems to figure out how near term behavior impacts long term growth and profitability. Our work will inform some of the biggest decisions at Amazon. Along the way, we're going to face seemingly insurmountable challenges. We're going to argue about how to solve them, and we'll work together to find a solution that is better than each of the proposals we came in with. We'll make tough decisions, but we'll all understand why. We'll be the dream team.We have decades of combined experience on the team in many areas science and engineering so it's a great environment in which to learn and grow. A/B testing is one of the hottest areas of research and development in the world today and this is a chance to learn how it works in the company known for pioneering its use. Applied Scientist, AI Research & Education 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. Applied Scientist, AI Research & Education 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. Senior Applied Scientist - Machine Learning, Personalization, Recommendations, Machine Learning, Causal Inference, Personalization 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 [amazon-science-logo-whi] * About * Research areas * Blog * News and features * Publications * Conferences * Collaborations * Careers * Alexa Prize * Academics * Research Awards * Amazon Developer * Amazon Web Services * About Amazon * Newsletter * RSS View from space of a connected network around planet Earth representing the Internet of Things. Have feedback for Amazon Science? We want to hear from you. 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