Social
Cloud and systems

Paper on graph database schemata wins best-industry-paper award

SIGMOD paper by Amazon researchers and collaborators presents flexible data definition language that enables rapid development of complex graph databases.

By Michael Schmidt, Filip Murlak
June 20, 2023
7 min read
Share

Where a standard relational database stores data in linked tables, graph databases store data in graphs, where the edges represent relationships between data items. Graph databases are popular with customers for use cases like single-customer view, fraud detection, recommendations, and security, where you need to create relationships between data and quickly navigate these connections. Amazon Neptune is AWS’s graph database service, which is designed for scalability and availability and allows our customers to query billions of relationships in milliseconds.

Tim Kraska.16x9.png
Related content
Building systems that automatically adjust to workloads and data
Tim Kraska, who joined Amazon this summer to build the new Learned Systems research group, explains the power of “instance optimization”.

In this blog post, we present joint work on a schema language for graph databases, which was carried out under the umbrella of the Linked Data Benchmarking Council (LDBC), a nonprofit organization that brings together leading organizations and academics from the graph database space. A schema is a way of defining the structure of a database — the data types permitted, the possible relationships between them, and the logical constraints upon them (such as uniqueness of entities).

This work is important to customers because it will allow them to describe and define the structures of their graphs in a way that is portable across vendors and makes building graph applications faster. We presented our work in a paper that won the best-industry-paper award at this year’s meeting of the Association for Computing Machinery's Special Interest Group on Management of Data (SIGMOD).

Labeled-property graphs

The labeled-property-graph (LPG) data model is a prominent choice for building graph applications. LPGs build upon three primitives to model graph-shaped data: nodes, edges, and properties. The figure below represents an excerpt from a labeled property graph in a financial-fraud scenario. Nodes are represented as green circles, edges are represented as directed arrows connecting nodes, and properties are enclosed in orange boxes.

The node with identifier 1, for instance, is labeled Customer and carries two properties, specifying the name with string value “Jane Doe” and a customerId. Both node 1 and 2 two are connected to node 3, which represents a shared account with a fixed iban number; the two edges are marked with the label Owns, which specifies the nature of the relationship. Just like vertices, edges can carry properties. In this example, the property since specifies 2021-03-05 as the start date of ownership.

Graph schemata 1.png
Sample graph representing two customers that own a shared account.

Relational vs. graph schema

 One property that differentiates graph databases from, for instance, relational databases — where the schema needs to be defined upfront and is often hard to change — is that graph databases do not require explicit schema definitions. To illustrate the difference, compare the graph data model from the figure above to a comparable relational-database schema, shown below, with the primary-key attributes underlined.

Relational database.png
A possible relational-database model for the scenario above.

Schema-level information of the relational model — tables and attribute names — are represented as part of the data itself in graphs. Said otherwise, by inserting or changing graph elements such as node labels, edge labels, and property names, one can extend or change the schema implicitly, without having to run (oftentimes tedious) schema manipulations such as ALTER TABLE commands.

DynamoDB 10 anniversary.png
Related content
Lessons learned from 10 years of DynamoDB
Prioritizing predictability over efficiency, adapting data partitioning to traffic, and continuous verification are a few of the principles that help ensure stability, availability, and efficiency.

As an example, in a graph database one can simply add an edge with the previously unseen label Knows to connect the two nodes representing Jane Doe and John Doe or introduce nodes with new labels (such as FinancialTransaction) at any time. Such extensions would require table manipulations in our relational sample schema.

The absence of an explicit schema is a key differentiator that lowers the burden of getting started with data modeling and application building in graphs: following a pay-as-you-go paradigm, graph application developers who build new applications can start out with a small portion of the data and insert new node types, properties, and interconnecting edges as their applications evolve, without having to maintain explicit schemata.

Schemata evolution

While this contributes to the initial velocity of building graph applications, what we often see is that — throughout the life cycle of graph applications — it becomes desirable to shift from implicit to explicit schemata. Once the database has been seeded with an initial (and typically yet-to-be-refined) version of the graph data, there is a demand for what we call flexible-schema support. 

Schema evolution.png
Evolution of schema requirements throughout the graph application life cycle.

In that stage, the schema primarily plays a descriptive role: knowing the most important node/edge labels and their properties tells application developers what to expect in the data and guides them in writing queries. As the application life cycle progresses, the graph data model stabilizes, and developers may benefit from a more rigorous, prescriptive schema approach that strongly asserts shapes and logical invariants in the graph.

PG-Schema

Motivated by these requirements, our SIGMOD publication proposes a data definition language (DDL) called PG-Schema, which aims to expose the full breadth of schema flexibility to users. The figure below shows a visual representation of such a graph schema, as well as the corresponding syntactical representation, as it could be provided by a data architect or application developer to formally define the schema of our fraud graph example.

Graph database schema.png
Schema for the graph data from the graph database above (left: graphical representation; right: corresponding data definition language).

In this example, the overall schema is composed of the six elements enclosed in the top-level GRAPH TYPE definition:

  • The first three lines of the GRAPH TYPE definition introduce so-called node types: person, customer, and account; they describe structural constraints on the nodes in the graph data. The customer node type, for instance, tells us that there can be nodes with label Customer, which carry a property customerId and are derived from a more general person node type. Concretely, this means that nodes with the label Customer inherit the properties name and birthDate defined in node type person. Note that properties also specify a data type (such as string, date, or numerical values) and may be marked as optional.
  • Edge types build upon node types and specify the type and structure of edges that connect nodes. Our example defines a single edge type connecting nodes of node type customer with nodes of type account. Informally speaking, this tells us that Customer-labeled nodes in our data graph can be connected to Account-labeled nodes via an edge labeled Owns, which is annotated with a property since, pointing to a date value.
  • The last two lines specify additional constraints that go beyond the mere structure of our graph. The KEY constraint demands that the value of the iban property uniquely identifies an account, i.e., no two Account-labeled nodes can share the same IBAN number. This can be thought of as the equivalent of primary keys in relational databases, which enforce the uniqueness of one or more attributes within the scope of a given table. The second constraint enforces that every account has at least one owner, which is reminiscent of a foreign-key constraint in relational databases.

Also note the keyword STRICT in the graph type definition: it enforces that all elements in the graph obey one of the types defined in the graph type body, and that all constraints are satisfied. Concretely, it implies that our graph can contain onlyPerson-, Customer-, and Account-labeled nodes with the respective sets of properties that the only possible edge type is between customers and accounts with label Owns and that the key and foreign constraints must be satisfied. Hence, the STRICT keyword can be understood as a mechanism to implement the schema-first paradigm, as it is maximally prescriptive and strongly constrains the graph structure.

Physalia cell
Related content
Amazon EBS addresses the challenge of the CAP Theorem at scale
Optimizing placement of configuration data ensures that it’s available and consistent during “network partitions”.

To account for flexible- and partial-schema use cases, PG-Schema offers a LOOSE keyword as an alternative to STRICT, which comes with a more relaxed interpretation: graph types that are defined as LOOSE allow for node and edge types that are not explicitly listed in the graph type definition. Mechanisms similar to STRICT vs. LOOSE keywords at graph type level can be found at different levels of the language.

For instance, keywords such as OPEN (vs. the implicit default, CLOSED) can be used to either partially or fully specify the set of properties that can be carried by vertices with a given vertex label (e.g., expressing that a Person-labeled node must have a name but may have an arbitrary set of other (unknown) properties, without requiring enumeration of the entire set). The flexibility arising from these mechanisms makes it easy to define partial schemata that can be adjusted and refined incrementally, to capture the schema evolution requirements sketched above.

Not only does PG-Schema provide a concrete proposal for a graph schema and constraint language, but it also aims to raise awareness of the importance of a standardized approach to graph schemata. The concepts and ideas in the paper were codeveloped by major companies and academics in the graph space, and there are ongoing initiatives within the LDBC that aim toward a standardization of these concepts.

In particular, the LDBC has close ties with the ISO committee that is currently in the process of standardizing a new graph query language (GQL). As some GQL ISO committee members are coauthors of the PG-Schema paper, there has been a continuous bilateral exchange, and it is anticipated that future versions of the GQL standard will include a rich DDL, which may pick up concepts and ideas presented in the paper.

Research areas
Tags
About the Author
Michael Schmidt
Michael Schmidt is a principal engineer with Amazon Neptune.
Filip Murlak
Filip Murlak is an assistant professor of mathematics, informatics, and mechanics at the University of Warsaw.

Related content

Work with us

See more jobs See more jobs
Data Scientist, Traffic Quality
IN, KA, Bengaluru
Amazon Ads is a multi-billion dollar global business that delivers advertising experiences across Amazon's owned-and-operated properties (including Prime Video, Twitch, Fire TV, and Amazon.com), third-party publisher networks, and emerging channels like generative AI-powered shopping experiences. As one of the fastest-growing segments of Amazon, we operate at unprecedented scale across desktop, mobile, connected TV, and emerging surfaces. Within Amazon Ads, Traffic Quality is a critical pillar of advertiser trust and marketplace integrity. Our mission is to build advanced capabilities that work at petabyte scale to detect sophisticated invalid traffic (IVT) which includes sophisticated non-human traffic, bot networks, and fraudulent engagement patterns across programmatic advertising. We are on a journey to establish Amazon Ads as an industry leader in traffic quality standards and transparency. Our research agenda focuses on staying ahead of adversarial actors through continuous innovation in detection methodologies, leveraging state-of-the-art techniques in deep learning and generative modeling, user behavior and multi-modal representation learning, anomaly detection, time-series analysis, and sparse labeling methods. We process billions of ad events daily, developing novel algorithms that balance precision and recall while operating under strict latency constraints. Our work directly protects hundreds of millions of dollars in advertiser spend annually while maintaining a seamless user experience. Key job responsibilities As a Data Scientist II in Traffic Quality, you will solve inherently hard problems in advertising fraud detection by applying advanced statistical techniques and machine learning. You'll work on systems that process billions of ad impressions and clicks per day, using Amazon's cloud services including EC2, S3, EMR, Sagemaker, and RedShift. - Define and frame new research problems in fraud detection where neither problem nor solution is well-defined. - Apply new machine learning approaches, models, and algorithms to detect sophisticated invalid traffic. - Apply domain knowledge to perform broad data analysis as a precursor to modeling and build business insights. - Work with unstructured and massive datasets to deliver results. - Produce research reports meeting top-tier external publication standards. - Mentor and develop junior scientists on the team. About the team Here are a few papers published by the team: 1/ [Scaling Generative Pre-training for User Ad Activity Sequences. AdKDD 2023.](https://assets.amazon.science/b7/42/03be071743d5a57cb1656e6caa34/scaling-generative-pre-training-for-user-ad-activity-sequences.pdf) 2/ [SLIDR: Real-time Robot Detection On Online Ads, IAAI 2023, Deployed Highly Innovative Applications of AI Track (AAAI 2023)](https://assets.amazon.science/75/2f/3b7106b143f38f7f4d2806388ace/real-time-detection-of-robotic-traffic-in-online-advertising.pdf) 3/ [Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers, NeurIPS 2022, First Table Representation Learning Workshop](https://openreview.net/forum?id=wIIJlmr1Dsk)
Applied Scientist, Fauna
US, CA, Sunnyvale
We are seeking an Applied Scientist to focus on Robot Navigation. In this role, you'll research and develop advanced navigation systems that enable robots to move reliably and safely through complex, dynamic environments. You'll work across a broad spectrum of navigation approaches—from classical methods to learning-based techniques and foundation models—to build robust solutions for autonomous robot navigation. Key job responsibilities - Develop and implement robust navigation systems that enable reliable autonomous operation in complex, dynamic indoor environments with static and dynamic obstacles - Build simulation-based and on-device evaluation frameworks with comprehensive benchmarks and metrics for systematic comparison of navigation methods - Conduct sim-to-real transfer experiments, analyzing performance gaps and developing techniques to ensure reliable real-world navigation performance - Collaborate with world model, manipulation, and other teams to ensure seamless integration of navigation capabilities into the full robot system - Stay current with the latest advances in robot navigation, spatial reasoning, and related fields, and apply relevant findings to improve system performance - Mentor fellow scientists and engineers while maintaining strong individual technical contributions About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products.
Applied Scientist, Pricing Science
US, WA, Seattle
Pricing is one of the most consequential decisions Amazon makes — and the science behind it needs to be causally rigorous, not just predictive. The P2 Optimization Science (P2OS) team builds the machine learning systems that power Amazon's pricing decisions at scale: demand lift models, customer lifetime value frameworks, and the experimentation infrastructure that validates whether our pricing changes actually work. We're hiring an Applied Scientist to own causal inference at the intersection of ML and pricing experimentation. This role exists because our team has identified a real gap: the methodological bridge between econometric analysis (owned by our economists) and production-scale ML pipelines (owned by our engineers) needs a practitioner who lives in both worlds. You'll build CATE estimation models, design analysis workflows for pricing weblabs, and develop the reusable causal ML infrastructure that the broader team — including non-ML scientists — can rely on. This is not a research role. The bias here is toward shipping production-quality causal pipelines with real downstream business impact. You'll measure success by what changes in LTV estimates, what pricing errors your models help avoid, and whether the economists on your team can actually use what you build. If you're a scientist who wants to work on hard causal identification problems in a high-stakes production environment — and who finds satisfaction in making rigorous methods accessible to a broader team — this role is for you. Key job responsibilities * Build causal ML pipelines for pricing — Design, train, evaluate, and deploy end-to-end causal estimation models for pricing use cases. * Own the science on heterogeneous treatment effects — Be the team SME on causal ML methodology: identification strategies, model selection, evaluation standards, and the tradeoffs between econometric and ML approaches to causal estimation. * Support pricing experiment analysis — Contribute causal analysis methodology to pricing weblab and A/B test post-analysis; build reusable tooling that economists can use without requiring ML expertise * Connect model outputs to business outcomes — Define, before writing code, what business metric each model moves; deliver model evaluation reports framed around pricing errors avoided and LTV estimate changes. * Evaluate and adopt novel techniques — Assess applicability of emerging causal inference methods (synthetic DiD, generalized random forests, causal representation learning) to Amazon's pricing context; write internal methodology proposals for adoption * Write internal documentation and methodology papers — Produce at least one internal write-up per half that connects a causal ML technique to a concrete pricing use case; make pipelines extensible and well-documented so other scientists can build on them. * Collaborate across disciplines — Partner closely with the Sr. Economist on identification strategy and causal assumptions; work with SDE and DE partners on production deployment; align with PMs on experiment design requirements A day in the life As an Applied Scientist on the P2OS team, your work directly shapes the prices customers see on hundreds of millions of Amazon products. In a given workweek, you might: * Investigate an optimization anomaly in simulation and trace it back to a model input gap or an unmodeled market dynamic * Design an offline evaluation framework to benchmark competing optimization approaches before committing to online testing * Collaborate with Sr. Economists on the identification strategy for the model you're building for a pricing lab * Present a science proposal for incorporating a new competitiveness or inventory signal into an optimization system * Work cross-team with the experimentation platform team on randomization design. * Develop and write up a novel scientific finding — preparing a paper or technical report for submission to a top-tier venue such as KDD, NeurIPS, or the ACM Conference on Economics and Computation
Applied Scientist II, Text-to-Speech on Device team
PL, Gdansk
Have you ever wondered how we give voice to devices — even when they're offline? The Text-to-Speech on Device team at Amazon builds AI-powered voice models that run locally on hardware with limited resources, serving customers across Alexa, automotive, and accessibility experiences for visually impaired users. We sit at the intersection of speech generation, generative AI, and on-device machine learning, and we're looking for a curious, collaborative Applied Scientist to help us push what's possible. In this role, you will research and develop production-ready speech generation models optimized for constrained environments. You will work across the full model lifecycle — from early experimentation and prototyping through to integration on real devices. If you're excited about solving hard scientific problems that directly improve how millions of people interact with technology, we'd love to hear from you. Key job responsibilities - Design and develop end-to-end machine learning models for on-device speech generation, from early research and experimentation through production-ready deployment. - Research and apply advanced techniques in generative AI, model compression, and knowledge distillation to deliver high-quality voice models within tight hardware constraints. - Propose and validate novel scientific approaches by authoring detailed technical specifications and contributing to peer-reviewed publications when appropriate. - Evaluate model performance rigorously, identify improvement opportunities, and iterate on training and inference pipelines to optimize quality and efficiency. - Collaborate with science and engineering teams across cloud and device platforms to bring speech generation capabilities from research prototypes to integrated product experiences. About the team The Text-to-Speech on Device team builds low-footprint AI models for speech generation that run locally on devices such as Android and FireOS platforms. Our models require significantly less computation than cloud-hosted alternatives, enabling offline voice experiences for Alexa, automotive partners, and accessibility solutions. We work closely with device engineering teams and cloud-based speech science teams to deliver the best possible experience for our customers. Our focus in the coming years is expanding the range of voices and languages we support while continuing to improve naturalness and efficiency on constrained hardware.
Applied Scientist II, Translation Services
IN, TS, Hyderabad
Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Hyderabad office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.
Applied Scientist, Navigation
US, CA, Sunnyvale
Amazon is on a mission to redefine the future of automation — and we're looking for exceptional talent to help lead the way. We are building the next generation of advanced robotic systems that seamlessly blend cutting-edge AI, sophisticated control systems, and novel mechanical design to create adaptable, intelligent automation solutions capable of operating safely alongside humans in dynamic, real-world environments. At Amazon, we leverage the power of machine learning, artificial intelligence, and advanced robotics to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence — and we're just getting started. As a Sr. Scientist in Robot Navigation, you will be at the forefront of this transformation — architecting and delivering navigation systems that are intelligent, safe, and scalable. You will bring deep expertise in learning-based planning and control, a strong understanding of foundation models and their application to embodied agents, and as well as have in-depth understanding of control-theoretic approaches such as model predictive control (MPC)-based trajectory planning. You will develop navigation solutions that seamlessly blend data-driven intelligence with principled control-theoretic guarantees. Our vision is bold: to build navigation systems that allow robots to move fluidly and safely through dynamic environments — understanding context, anticipating change, and adapting in real time. You will lead research that bridges the gap between cutting-edge academic advances and production grade deployment, collaborating with world-class teams pushing the boundaries of robotic autonomy, manipulation, and human-robot interaction. Join us in building the next generation of intelligent navigation systems that will define the future of autonomous robotics at scale. Key job responsibilities - Design, develop, and deploy perception algorithms for robotics systems, including object detection, segmentation, tracking, depth estimation, and scene understanding - Lead research initiatives in computer vision, sensor fusion and 3D perception - Collaborate with cross-functional teams including robotics engineers, software engineers, and product managers to define and deliver perception capabilities - Drive end-to-end ownership of ML models — from data collection and labeling strategy to training, evaluation, and deployment - Mentor junior scientists and engineers; contribute to a culture of technical excellence - Define and track key metrics to measure perception system performance in real-world environments - Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Our team is a group is a diverse group of scientists and engineers passionate about building intelligent machines. We value curiosity, rigor, and a bias for action. We believe in learning from failure and iterating quickly toward solutions that matter.
Applied Scientist II, CMT
IN, KA, Bengaluru
As a member of the CMT team, you'll play a key role in the evolution of our Competitive Monitoring systems to solve significantly complex and interesting technical challenges in machine learning, large language models in production, and recommender systems to name a few. The team's work directly impacts customer experience at a worldwide scale. Key job responsibilities Thought leader on the team and help set team directions Research multiple problem domains, suggest various approaches to try and be as hands-on as needed while providing more junior scientists with critical mentorship Collaborate with engineers to come up with the right LLD and HLD to solve key business problems Strong emphasis on communication via writing, internal and external talks, and being able to align with multiple stakeholders A day in the life As an Applied scientist II, a typical day will involve aligning with key product, engineering and business stakeholders ; advising junior scientists on the work they are doing ; reading current research papers and staying up-to-date on AI research ; diving deep as needed to improve CMT models and addressing stakeholders from the science perspective ; writing python code
Applied Scientist, CMT
IN, KA, Bengaluru
As a member of the CMT team, you'll play a key role in the evolution of our Competitive Monitoring systems to solve significantly complex and interesting technical challenges in machine learning, large language models in production, and recommender systems to name a few. The team's work directly impacts customer experience at a worldwide scale. Key job responsibilities 1. Research the problem domain and come up with various approaches to solve the problem. 2. Be willing to experiment quickly and fail fast. 3. Collaborate with engineers to come up with the right end to end solution to the business problems. 4. Ideate on future roadmap for science in CMT 5. Be willing to roll up your sleeves and learn core topics outside applied science, for example ML engineering A day in the life A typical day might involve (a) working on ideas for improving models around product similarity or price recommendations, (b) working closely with other scientists and our ML engineers to ensure that the best models are in production, (c) writing good maintainable code that can be reused and reproduced, (d) sharing your work across CMT and beyond via technical writings and presentations
Senior Data Scientist, Customer Experience and Business Trends
US, WA, Seattle
Amazon's Customer Experience and Business Trends (CXBT) organization is hiring a Senior Data Scientist for its Benchmarking, Economics, Analytics and Measurement (BEAM) team. BEAM's mission is to improve customer experience across every Amazon business by turning fragmented internal and external data into decision-grade intelligence for senior leaders. This role anchors our Topline View of Retail initiative: an always-on competitive intelligence capability that fuses third-party transaction panels with Amazon's internal signals to answer how Amazon is winning or losing share of wallet, across segments, categories, and time. You will not just analyze metrics — you will decide which metrics should exist, then build the statistical and machine learning models that generate, forecast, and explain them. Given a new business question and a pile of raw panel and internal data, you are the person who defines what "competitiveness" means, translates it into a defensible, reproducible metric and model, and defends the methodology to skeptical senior stakeholders. We are looking for someone with exceptional business judgment and metric intuition, paired with deep hands-on modeling skills: a strong point of view on what to measure, and the technical ability to build the models that measure it at scale. You should be equally comfortable pressure-testing a model's assumptions and explaining its "so what" to a VP in two sentences. Key job responsibilities - Own the definition, methodology, and evolution of BEAM's competitiveness metrics (e.g., share-of-wallet, segment-level penetration, price/selection/fulfillment competitiveness), including the trade-offs behind each definition. - Design, build, and validate statistical and machine learning models — forecasting, causal inference, segmentation, and anomaly detection — that power those metrics and surface competitive shifts. - Pull together disparate third-party transaction-panel data and internal Amazon signals into coherent, reproducible modeling pipelines that leadership can trust for recurring reporting. - Translate ambiguous business questions ("are we losing ground in grocery?") into the right metric, the right model, the right cut of data, and a clear, defensible answer. - Productionize models and analyses so they run reliably as recurring mechanisms, partnering with data engineers to scale and automate. - Produce monthly flash reports and deep-dive narratives that turn models and metrics into decisions for CXBT and business-line leadership. - Set the standard for analytical and modeling rigor on the team — methodology reviews, documentation, and guardrails against misleading cuts. - Mentor and technically uplift other scientists and BIEs on the team — reviewing modeling approaches and raising the bar on rigor (e.g., partnering with our Data Scientist on the MatchIQ model to strengthen methodology and validation). About the team CXBT is a group of diverse functions dedicated to understanding, improving, and influencing customer experience globally, across all of Amazon. We are builders who develop products, services, and data-driven approaches that shape offerings for nearly every Amazon business and customer type — consumers, developers, sellers, brands, employees, investors, streamers, and gamers. We work backwards from customer needs, combine technical and non-technical methods, and track industry and business trends. Our roles span Product Managers, Data Scientists, Economists, Business Intelligence Engineers, Data Engine, Applied Scentist, and Product Manager.
Applied Scientist-II, Amazon Music - Search Science, Amazon Music – Search Science
IN, KA, Bengaluru
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. The Amazon Music Search Science team is seeking an innovative and driven Applied Scientist to join our engineering and science hub in Bangalore. You will work alongside a world-class team of machine learning experts to break new ground in understanding user intent, classifying complex audio and musical forms, and creating next-generation interactive search experiences that help users find the exact music, podcasts, and audio content they are in the mood for. In this role, you will own the design, development, and deployment of end-to-end machine learning systems. You will balance execution on core search and discovery priorities—such as improving retrieval accuracy, latency, and relevance for millions of daily queries—while laying the foundational modeling capabilities for broader semantic understanding and advanced conversational search experiences across mobile, web, and voice-forward devices (like Alexa and Echo). Key job responsibilities - Core Search & Execution: Collaborate with scientists, software engineers, and product managers to define, frame, and solve complex business and ranking problems as machine learning, information retrieval, or optimization tasks. - Advanced AI & Modeling: Design, build, train, and evaluate production-grade ML models using classical machine learning, deep learning, Large Language Models (LLMs), and Agentic AI techniques to scale music discovery and intent resolution. - End-to-End Production Ownership: Take algorithms from research ideation to production deployment. Build scalable data pipelines, efficient model-serving systems, and robust offline/online evaluation frameworks. - Experimentation & Iteration: Design and analyze large-scale A/B experiments across millions of customers to measure impact on search relevance, engagement, and customer satisfaction, refining models for continuous improvement. - Forward-Looking Innovation: Research and implement novel statistical and machine learning approaches, exploring multi-modal understanding, rich content semantics, and advanced retrieval mechanisms that extend beyond traditional search boundaries. - Technical Communication: Communicate findings, architectural decisions, and technical roadmaps clearly to both technical peers and executive stakeholders, authoring robust design documents and contributing to team standards. Basic Qualifications - PhD, or Master’s degree and 4+ years of relevant experience in Computer Science, Computer Engineering, Machine Learning, Statistics, or a related quantitative field. - 3+ years of hands-on experience building machine learning models or algorithms for business applications and deploying them into production. - Strong programming skills in Python, Java, C++, or related languages, with a solid foundation in data structures, algorithms, and object-oriented design. - Experience in one or more of the following areas: Information Retrieval, Natural Language Processing (NLP), Recommender Systems, Deep Learning, or Numerical Optimization. - Demonstrated ability to work effectively with cross-functional teams in a fast-paced environment. Preferred Qualifications - Experience with large-scale distributed computing frameworks and big data systems (e.g., Spark, Hadoop, AWS infrastructure). - Experience building search ranking, query understanding, or semantic retrieval systems for high-scale consumer applications. - Familiarity with modern foundation models, LLMs, fine-tuning techniques, and efficient inference optimization for production services. - Track record of peer-reviewed publications or patents at top-tier machine learning/AI conferences (e.g., NeurIPS, KDD, ACL, SIGIR, ICML). - Experience in designing, executing, and evaluating rigorous online A/B experiments.
See more jobs
View from space of a connected network around planet Earth representing the Internet of Things.
Get more from Amazon Science
Subscribe to our newsletter

Amazon.com | Conditions of Use | Privacy | © 1996-2026 Amazon.com, Inc. or its affiliates

Social