Quantum key distribution and authentication: Separating facts from myths

Key exchange protocols and authentication mechanisms solve distinct problems and must be integrated in a secure communication system.

Quantum key distribution (QKD) is a technology that leverages the laws of quantum physics to securely share secret information between distant communicating parties. With QKD, quantum-mechanical properties ensure that if anyone tries to tamper with the secret-sharing process, the communicating parties will know. Keys established through QKD can then be used in traditional symmetric encryption or with other cryptographic technologies to secure communications.

“Record now, decrypt later" (RNDL) is a cybersecurity risk arising from advances in quantum computing. The term refers to the situation in which attackers record encrypted data today, even though they cannot decrypt it immediately. They store this data with the expectation that future quantum computers will be powerful enough to break the cryptographic algorithms currently securing it. Sensitive information such as financial records, healthcare data, or state secrets could be at risk, even years after it was transmitted.

Mitigating RNDL requires adopting quantum-resistant cryptographic methods, such as post-quantum cryptography (PQC) and/or quantum key distribution (QKD), to ensure confidentiality against future quantum advancements. AWS has invested in the migration to post-quantum cryptography to protect the confidentiality, integrity, and authenticity of customer data.

Quantum communication is important enough that in 2022, three of its pioneers won the Nobel Prize for physics. However, misconceptions about QKD’s role still persist. One of them is that QKD lacks practical value because it “doesn’t solve the authentication problem”. This view can obscure the broad benefits that QKD brings to secure communications when integrated properly into existing systems.

QKD should be viewed as a complement to — rather than a replacement for — existing cybersecurity frameworks. Functionally, QKD solves the same problem solved by other key establishment protocols, including the well-known Diffie-Hellman (DH) method and the module-lattice-based key encapsulation mechanism (ML-KEM), the standard recently ratified by the FIPS — but it does it in a fundamentally different way. Like those methods, QKD depends on strong authentication to defend against threats such as man-in-the-middle attacks, where an attacker poses as one of the communicating parties.

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In short, key exchange protocols and authentication mechanisms are different security primitives for solving distinct problems and must be integrated together in a secure communication system.

The challenge, then, is not to give QKD an authentication mechanism but to understand how it can be integrated with other established mechanisms to strengthen the overall security infrastructure. As quantum technologies continue to evolve, it’s important to shift the conversation from skepticism about authentication to consideration of how QKD can be thoughtfully and practically implemented to address today’s and tomorrow’s cybersecurity needs — such as the need to mitigating the “record now, decrypt later” (RNDL) attack (see sidebar).

Understanding the role of authentication in QKD

When discussing authentication in the context of QKD, we focus on the classical digital channel that the parties use to exchange information about their activities on the quantum channel. This isn’t about user authentication methods, such as logging in with passwords or biometrics, but rather about authenticating the communicating entities and the data exchanged. Entity authentication ensures that the parties are who they claim to be; data authentication guarantees that the information received is the same as what was sent by the claimed source. QKD protocols include a classical-communication component that uses both authentication methods to assure the overall security of the interaction.

Entity authentication

Entity authentication is the process by which one party (the "prover") asserts its identity, and another party (the "verifier") validates that assertion. This typically involves a registration step, in which the verifier obtains reliable identification information about the prover, as a prelude to any further authentication activity. The purpose of this step is to establish a “root of trust” or “trust anchor”, ensuring that the verifier has a trusted baseline for future authentications.

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Several entity authentication methods are in common use, each based on a different type of trust anchor:

  • Public-key-infrastructure (PKI) authentication: In this method, a prover’s certificate is issued by a trusted certificate authority (CA). The verifier relies on this CA, or the root CA in a certificate chain, to establish trust. The certificate acts as the trust anchor that links the prover’s identity to its public key.
  • PGP-/GPG-based (web of trust) authentication: Here, trust is decentralized. A prover’s public key is trusted if it has been vouched for by one or more trusted third parties, such as a mutual acquaintance or a public-key directory. These third parties serve as the trust anchors.
  • Pre-shared-key-based (PSK) authentication: In this case, both the prover and the verifier share a secret key that was exchanged via an offline or other secure out-of-band method. The trust anchor is the method of securely sharing this key a priori, such as a secure courier or another trusted channel.

These trust anchors form the technical backbones of all authentication systems. However, all entity authentication methods are based on a fundamental assumption: the prover is either the only party that holds the critical secret data (e.g., the prover’s private key in PKI or PGP) or the only other party that shares the secret with the verifier (PSK). If this assumption is broken — e.g., the prover's private key is stolen or compromised, or the PSK is leaked — the entire authentication process can fail.

Data authentication

Data authentication, also known as message authentication, ensures both the integrity and authenticity of the transmitted data. This means the data received by the verifier is exactly what the sender sent, and it came from a trusted source. As with entity authentication, the foundation of data authentication is the secure management of secret information shared by the communicating parties.

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The most common approach to data authentication is symmetric cryptography, where both parties share a secret key. A keyed message authentication code (MAC), such as HMAC or GMAC, is used to compute a unique tag for the transmitted data. This tag allows the receiver to verify that the data hasn’t been altered during transit. The security of this method depends on the collision resistance of the chosen MAC algorithm — that is, the computational infeasibility of finding two or more plaintexts that could yield the same tag — and the confidentiality of the shared key. The authentication tag ensures data integrity, while the secret key guarantees the authenticity of the data origin.

An alternative method uses asymmetric cryptography with digital signatures. In this approach, the sender generates a signature using a private key and the data itself. The receiver, or anyone else, can verify the signature’s authenticity using the sender’s public key. This method provides data integrity through the signature algorithm, and it assures data origin authenticity as long as only the sender holds the private key. In this case, the public key serves as a verifiable link to the sender, ensuring that the signature is valid.

In both the symmetric and the asymmetric approaches, successful data authentication depends on effective entity authentication. Without knowing and trusting the identity of the sender, the verification of the data’s authenticity is compromised. Therefore, the strength of data authentication is closely tied to the integrity of the underlying entity authentication process.

Authentication in QKD

The first quantum cryptography protocol, known as BB84, was developed by Bennett and Brassard in 1984. It remains foundational to many modern QKD technologies, although notable advancements have been made since then.

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QKD protocols are unique because they rely on the fundamental principles of quantum physics, which allow for “information-theoretic security.” This is distinct from the security provided by computational complexity. In the quantum model, any attempt to eavesdrop on the key exchange is detectable, providing a layer of security that classical cryptography cannot offer.

QKD relies on an authenticated classical communication channel to ensure the integrity of the data exchanged between parties, but it does not depend on the confidentiality of that classical channel. (This is why RNDL is not an effective attack against QKD). Authentication just guarantees that the entities establishing keys are legitimate, protecting against man-in-the-middle attacks.

Currently, several commercial QKD products are available, many of which implement the original BB84 protocol and its variants. These solutions offer secure key distribution in real-world applications, and they all pair with strong authentication processes to ensure the communication remains secure from start to finish. By integrating both technologies, organizations can build communication infrastructures capable of withstanding both classical and quantum threats.

Authentication in QKD bootstrap: A manageable issue

During the initial bootstrap phase of a QKD system, the authentic classical channel is established using traditional authentication methods based on PKI or PSK. As discussed earlier, all of these methods ultimately rely on the establishment of a trust anchor.

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While confidentiality may need to be maintained for an extended period (sometimes decades), authentication is a real-time process. It verifies identity claims and checks data integrity in the moment. Compromising an authentication mechanism at some future point will not affect past verifications. Once an authentication process is successfully completed, the opportunity for an adversary to tamper with it has passed. That is, even if, in the future, a specific authentication mechanism used in QKD is broken by a new technology, QKD keys generated prior to that point are still safe to use, because no adversary can go back in time to compromise past QKD key generation.

This means that the reliance on traditional, non-QKD authentication methods presents an attack opportunity only during the bootstrap phase, which typically lasts just a few minutes. Given that this phase is so short compared to the overall life cycle of a QKD deployment, the potential risks posed by using authentication mechanisms are relatively minor.

Authentication after QKD bootstrap: Not a new issue

Once the bootstrap phase is complete, the QKD devices will have securely established shared keys. These keys can then be used for PSK-based authentication in future communications. In essence, QKD systems can maintain the authenticated classical communication channel by utilizing a small portion of the very keys they generate, ensuring continued secure communication beyond the initial setup phase.

It is important to note that if one of the QKD devices is compromised locally for whatever reason, the entire system’s security could be at risk. However, this is not a unique vulnerability introduced by QKD. Any cryptographic system faces similar challenges when the integrity of an endpoint is compromised. In this respect, QKD is no more susceptible to it than any other cryptographic system.

Overcoming key challenges to QKD’s role in cybersecurity

Up to now we have focused on clarifying the myths about authentication needs in QKD. Next we will discuss several other challenges in using QKD in practice.

Bridging the gap between QKD theory and implementation

While QKD protocols are theoretically secure, there remains a significant gap between theory and real-world implementations. Unlike traditional cryptographic methods, which rely on well-understood algorithms that can be thoroughly reviewed and certified, QKD systems depend on specialized hardware. This introduces complexity, as the process of reviewing and certifying QKD hardware is not yet mature.

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In conventional cryptography, risks like side-channel attacks — which use runtime clues such as memory access patterns or data retrieval times to deduce secrets — are well understood and mitigated through certification processes. QKD systems are following a similar path. The European Telecommunications Standards Institute (ETSI) has made a significant move by introducing the Common Criteria Protection Profile for QKD, the first international effort to create a standardized certification framework for these systems. ISO/IEC has also published standards on security requirements and test and evaluation methods for QKD. These represent crucial steps in building the same level of trust that traditional cryptography enjoys.

Once the certification process is fully established, confidence in QKD’s hardware implementations will continue to grow, enabling the cybersecurity community to embrace QKD as a reliable, cutting-edge solution for secure communication. Until then, the focus remains on advancing the review and certification processes to ensure that these systems meet the highest security standards.

QKD deployment considerations

One of the key challenges in the practical deployment of QKD is securely transporting the keys generated by QKD devices to their intended users. While it’s accepted that QKD is a robust mechanism for distributing keys to the QKD devices themselves, it does not cover the secure delivery of keys from the QKD device to the end user (or key consumer).

QKD diagram.png
A schematic representation of two endpoints — site A and site B — that want to communicate safely. The top line represents the user traffic being protected, and the bottom lines are the channels required to establish secure communication. An important practical consideration is how to transmit a key between a QKD device and an end user within an endpoint.

This issue arises whether the QKD system is deployed within a large intranet or a small local-area network. In both cases, the keys must be transported over a non-QKD system. The standard deployment requirement is that the key delivery from the QKD system to the key consumer occurs “within the same secure site”, and the definition of a “secure site” is up to the system operator.

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The best practice is to make the boundary of the secure site as small as is practical. One extreme option is to remove the need for transporting keys over classical networks entirely, by putting the QKD device and the key user’s computing hardware in the same physical unit. This eliminates the need for traditional network protocols for key transport and realizes the full security benefits of QKD without external dependency. In cases where the extreme option is infeasible or impractical, the secure site should cover only the local QKD system and the intended key consumers.

Conclusion

QKD-generated keys will remain secure even when quantum computers emerge, and communications using these keys are not vulnerable to RNDL attacks. For QKD to reach its full potential, however, the community must collaborate closely with the broader cybersecurity ecosystem, particularly in areas like cryptography and governance, risk, and compliance (GRC). By integrating the insights and frameworks established in these fields, QKD can overcome its current challenges in trust and implementation.

This collective effort is essential to ensure that QKD becomes a reliable and integral part of secure communication systems. As these collaborations deepen, QKD will be well-positioned to enhance existing security frameworks, paving the way for its adoption across industries and applications.

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This position requires that the candidate selected be a US Citizen and currently possess and maintain an active Top Secret security clearance. The Amazon Web Services Professional Services (ProServe) team seeks an experienced Principal Data Scientist to join our ProServe Shared Delivery Team (SDT). In this role, you will serve as a technical leader and strategic advisor to AWS enterprise customers, partners, and internal AWS teams on transformative AI/ML projects. You will leverage your deep technical expertise to architect and implement innovative machine learning and generative AI solutions that drive significant business outcomes. As a Principal Data Scientist, you will lead complex, high-impact AI/ML initiatives across multiple customer engagements. You will collaborate with Director and C-level executives to translate business challenges into technical solutions. You will drive innovation through thought leadership, establish technical standards, and develop reusable solution frameworks that accelerate customer adoption of AWS AI/ML services. Your work will directly influence the strategic direction of AWS Professional Services AI/ML offerings and delivery approaches. Your extensive experience in designing and implementing sophisticated AI/ML solutions will enable you to tackle the most challenging customer problems. You will provide technical mentorship to other data scientists, establish best practices, and represent AWS as a subject matter expert in customer-facing engagements. You will build trusted advisor relationships with customers and partners, helping them achieve their business outcomes through innovative applications of AWS AI/ML services. The AWS Professional Services organization is a global team of experts that help customers realize their desired business outcomes when using the AWS Cloud. We work together with customer teams and the AWS Partner Network (APN) to execute enterprise cloud computing initiatives. Our team provides a collection of offerings which help customers achieve specific outcomes related to enterprise cloud adoption. We also deliver focused guidance through our global specialty practices, which cover a variety of solutions, technologies, and industries. Key job responsibilities Architecting and implementing complex, enterprise-scale AI/ML solutions that solve critical customer business challenges Providing technical leadership across multiple customer engagements, establishing best practices and driving innovation Collaborating with Delivery Consultants, Engagement Managers, Account Executives, and Cloud Architects to design and deploy AI/ML solutions Developing reusable solution frameworks, reference architectures, and technical assets that accelerate customer adoption of AWS AI/ML services Representing AWS as a subject matter expert in customer-facing engagements, including executive briefings and technical workshops Identifying and driving new business opportunities through technical innovation and thought leadership Mentoring junior data scientists and contributing to the growth of AI/ML capabilities within AWS Professional Services
US, WA, Seattle
Amazon Advertising is one of Amazon's fastest growing businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Creative X team within Amazon Advertising time aims to democratize access to high-quality creatives (audio, images, videos, text) by building AI-driven solutions for advertisers. To accomplish this, we are investing in understanding how best users can leverage Generative AI methods such as latent-diffusion models, large language models (LLM), generative audio (music and speech synthesis), computer vision (CV), reinforced learning (RL) and related. As an Applied Scientist you will be part of a close-knit team of other applied scientists and product managers, UX and engineers who are highly collaborative and at the top of their respective fields. We are looking for talented Applied Scientists who are adept at a variety of skills, especially at the development and use of multi-modal Generative AI and can use state-of-the-art generative music and audio, computer vision, latent diffusion or related foundational models that will accelerate our plans to generate high-quality creatives on behalf of advertisers. Every member of the team is expected to build customer (advertiser) facing features, contribute to the collaborative spirit within the team, publish, patent, and bring SOTA research to raise the bar within the team. As an Applied Scientist on this team, you will: - Drive the invention and development of novel multi-modal agentic architectures and models for the use of Generative AI methods in advertising. - Work closely and integrate end-to-end proof-of-concept Machine Learning projects that have a high degree of ambiguity, scale and complexity. - Build interface-oriented systems that use Machine Learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Curate relevant multi-modal datasets. - Perform hands-on analysis and modeling of experiments with human-in-the-loop that eg increase traffic monetization and merchandise sales, without compromising the shopper experience. - Run A/B experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Mentor and help recruit Applied Scientists to the team. - Present results and explain methods to senior leadership. - Willingness to publish research at internal and external top scientific venues. - Write and pursue IP submissions. Key job responsibilities This role is focused on developing new multi-modal Generative AI methods to augment generative imagery and videos. You will develop new multi-modal paradigms, models, datasets and agentic architectures that will be at the core of advertising-facing tools that we are launching. You may also work on development of ML and GenAI models suitable for advertising. You will conduct literature reviews to stay on the SOTA of the field. You will regularly engage with product managers, UX designers and engineers who will partner with you to productize your work. For reference see our products: Enhanced Video Generator, Creative Agent and Creative Studio. A day in the life On a day-to-day basis, you will be doing your independent research and work to develop models, you will participate in sprint planning, collaborative sessions with your peers, and demo new models and share results with peers, other partner teams and leadership. About the team The team is a dynamic team of applied scientists, UX researchers, engineers and product leaders. We reside in the Creative X organization, which focuses on creating products for advertisers that will improve the quality of the creatives within Amazon Ads. We are open to hiring candidates to work out of one of the following locations: UK (London), USA (Seattle).
US, WA, Bellevue
The Amazon Fulfillment Technologies (AFT) Science team is seeking an exceptional Applied Scientist with strong operations research and optimization expertise to develop production solutions for one of the most complex systems in the world: Amazon's Fulfillment Network. At AFT Science, we design, build, and deploy optimization, statistics, machine learning, and GenAI/LLM solutions that power production systems running across Amazon Fulfillment Centers worldwide. We tackle a wide range of challenges throughout the network, including labor planning and staffing, pick scheduling, stow guidance, and capacity risk management. Our mission is to develop innovative, scalable, and reliable science-driven production solutions that exceed the published state of the art, enabling systems to run optimally and continuously (from every few minutes to every few hours) across our large-scale network. Key job responsibilities As an Applied Scientist, you will collaborate with scientists, software engineers, product managers, and operations leaders to develop optimization-driven solutions that directly impact process efficiency and associate experience in the fulfillment network. Your key responsibilities include: - Develop deep understanding and domain knowledge of operational processes, system architecture, and business requirements - Dive deep into data and code to identify opportunities for continuous improvement and disruptive new approaches - Design and develop scalable mathematical models for production systems to derive optimal or near-optimal solutions for existing and emerging challenges - Create prototypes and simulations for agile experimentation of proposed solutions - Advocate for technical solutions with business stakeholders, engineering teams, and senior leadership - Partner with software engineers to integrate prototypes into production systems - Design and execute experiments to test new or incremental solutions launched in production - Build and monitor metrics to track solution performance and business impact About the team Amazon Fulfillment Technology (AFT) designs, develops, and operates end-to-end fulfillment technology solutions for all Amazon Fulfillment Centers (FCs). We harmonize the physical and virtual worlds so Amazon customers can get what they want, when they want it. The AFT Science team brings expertise in operations research, optimization, statistics, machine learning, and GenAI/LLM, combined with deep domain knowledge of operational processes within FCs and their unique challenges. We prioritize advancements that support AFT tech teams and focus areas rather than specific fields of research or individual business partners. We influence each stage of innovation from inception to deployment, which includes both developing novel solutions and improving existing approaches. Our production systems rely on a diverse set of technologies, and our teams invest in multiple specialties as the needs of each focus area evolve.
US, WA, Seattle
Have you ever wondered what it takes to transform millions of manual network planning decisions into AI-powered precision? Network Planning Solutions is looking for scientific innovators obsessed with building the AI/ML intelligence that makes orchestrating complex global operations feel effortless. Here, you'll do more than just build models; you'll create 'delight' by discovering and deploying the science that delivers exactly what our customers need, right when they need it. If you're ready to transform complex data patterns into breakthrough AI capabilities that power intuitive human experiences, you've found your team. Network Planning Solutions architects and orchestrates Amazon's customer service network of the future. By building AI-native solutions that continuously learn, predict and optimize, we deliver seamless customer experiences and empower associates with high-value work—driving measurable business impact at a global scale. As a Sr. Manager, Applied Science, you will own the scientific innovation and research initiatives that make this vision possible. You will lead a team of applied scientists and collaborate with cross-functional partners to develop and implement breakthrough scientific solutions that redefine our global network. Key job responsibilities Lead AI/ML Innovation for Network Planning Solutions: - Develop and deploy production-ready demand forecasting algorithms that continuously sense and predict customer demand using real-time signals - Build network optimization algorithms that automatically adjust staffing as conditions evolve across the service network - Architect scalable AI/ML infrastructure supporting automated forecasting and network optimization capabilities across the system Drive Scientific Excellence: - Build and mentor a team of applied scientists to deliver breakthrough AI/ML solutions - Design rigorous experiments to validate hypotheses and quantify business impact - Establish scientific excellence mechanisms including evaluation metrics and peer review processes Enable Strategic Transformation: - Drive scientific innovation from research to production - Design and validate next-generation AI-native models while ensuring robust performance, explainability, and seamless integration with existing systems. - Partner with Engineering, Product, and Operations teams to translate AI/ML capabilities into measurable business outcomes - Navigate ambiguity through experimentation while balancing innovation with operational constraints - Influence senior leadership through scientific rigor, translating complex algorithms into clear business value A day in the life Your day will be a dynamic blend of scientific innovation and strategic problem-solving. You'll collaborate with cross-functional teams, design AI algorithms, and translate complex data patterns into intuitive solutions that drive meaningful business impact. About the team We are Network Planning Solutions, a team of scientific innovators dedicated to reshaping how global service networks operate. Our mission is to create AI-native solutions that continuously learn, predict, and optimize customer experiences. We empower our associates to tackle high-value challenges and drive transformative change at a global scale.
US, CA, Palo Alto
Sponsored Products and Brands (SPB) is at the heart of Amazon Advertising, helping millions of advertisers—from small businesses to global brands—connect with customers at the moments that matter most. Our advertising solutions enable sellers, vendors, and brand owners to grow their businesses by reaching shoppers with relevant, engaging ads across Amazon's store and beyond. We're obsessed with delivering measurable results for advertisers while creating a delightful shopping experience for customers. Are you interested in defining the science behind the future of advertising? Sponsored Products and Brands science teams are pioneering breakthrough agentic AI systems—pushing the boundaries of large language models, autonomous reasoning, planning, and decision-making to build intelligent agents that fundamentally transform how advertisers succeed on Amazon. As an SPB applied science leader, you'll have end-to-end ownership of the product and scientific vision, research agenda, model architectures, and evaluation frameworks required to deliver state-of-the-art agentic AI solutions for our advertising customers. You'll get to work on problems that are fast-paced, scientifically rich, and deeply consequential. You'll also be able to explore novel research directions, take bold bets, and collaborate with remarkable scientists, engineers, and product leaders. We'll look for you to bring your diverse perspectives, deep technical expertise, and scientific rigor to make Amazon Advertising even better for our advertisers and customers. With global opportunities for talented scientists and science leaders, you can decide where a career in Amazon Ads Science takes you! We are kicking off a new initiative within SPB to leverage agentic AI solutions to revolutionize how advertisers create, manage, and optimize their advertising campaigns. This is a unique opportunity to lead a business-critical applied science initiative from its inception—defining the scientific charter, establishing foundational research pillars, and building a multi-year science roadmap for transformative impact. As the single-threaded applied science leader, you will build and guide a dedicated team of applied scientists, research scientists, and machine learning engineers, working closely with cross-functional engineering and product partners, to research, develop, and deploy agentic AI systems that fundamentally reimagine the advertiser journey. Your charter will begin with advancing the science behind intelligent agents that simplify campaign creation, automate optimization decisions through autonomous reasoning and planning, and deliver personalized advertising strategies at scale. You will pioneer novel approaches in areas such as LLM-based agent architectures, multi-step planning and tool use, retrieval-augmented generation, reinforcement learning from human and business feedback, and robust evaluation methodologies for agentic systems. You will expand to proactively identify and tackle the next generation of AI-powered advertising experiences across the entire SPB portfolio. This high-visibility role places you as the science leader driving our strategy to democratize advertising success—making it effortless for advertisers of all sizes to achieve their business goals while delivering relevant experiences for Amazon customers. Key job responsibilities Build, mentor, and lead a new, high-performing applied science organization of applied scientists, research scientists, and engineers, fostering a culture of scientific excellence, innovation, customer obsession, and ownership. Define, own, and drive the long-term scientific and product vision and research strategy for agentic AI-powered advertising experiences across Sponsored Products and Brands—identifying the highest-impact research problems and charting a path from exploration to production. Lead the research, design, and development of novel agentic AI models and systems—including LLM-based agent architectures, multi-agent orchestration, planning and reasoning frameworks, tool-use mechanisms, and retrieval-augmented generation pipelines—that deliver measurable value for advertisers and create delightful, intuitive experiences. Establish rigorous scientific methodology and evaluation frameworks for assessing agent performance, reliability, safety, and advertiser outcomes, setting a high bar for experimentation, reproducibility, and offline-to-online consistency. Partner closely with senior business, engineering, and product leaders across Amazon Advertising to translate advertiser pain points and business opportunities into well-defined science problems, and deliver cohesive, production-ready solutions that drive advertiser success. Drive execution from research to production at scale, ensuring models and agentic systems meet high standards for quality, robustness, latency, safety, and reliability for mission-critical advertising services operating at Amazon scale. Champion a culture of scientific inquiry and technical depth that encourages bold experimentation, publication of novel research, relentless simplification, and continuous improvement. Communicate your team's scientific vision, research breakthroughs, strategy, and progress to senior leadership and key stakeholders, ensuring alignment with broader Amazon Advertising objectives and contributing to Amazon's position at the forefront of applied AI. Develop a science roadmap directly tied to advertiser outcomes, revenue growth, and business plans, delivering on commitments for high-impact research and modeling initiatives that shape the future of AI-powered digital advertising.