Why a lack of diversity hurts economics—and economists

Four economists from diverse backgrounds explain why diversity is essential, and what needs to happen to achieve it.

Howard University’s recent announcement that it will host the American Economic Association Summer Training and Scholarship Program (AEASP) comes at a time when the economics profession finds itself grappling with a decades-old problem: a lack of diversity.

Four economists from diverse and underrepresented backgrounds, two each from Amazon and Howard University, recently shared their perspectives on the challenges and potential solutions.

Headshots of Gerald E. Daniels Jr., Jevay Grooms, Muthoni Ngatia and Henrique Romero.
From top left, clockwise: Gerald E. Daniels Jr., associate professor of economics and associate director of undergraduate studies in the Department of Economics at Howard; Jevay Grooms, assistant professor in the Department of Economics at Howard; Muthoni Ngatia, a current Amazon and former World Bank economist; and Henrique Romero, a senior economist with Amazon’s Supply Chain Optimization Technologies division.

Gerald E. Daniels Jr., associate professor of economics and associate director of undergraduate studies in the Department of Economics at Howard University; Jevay Grooms, assistant professor in the Department of Economics at Howard University; Muthoni Ngatia, a current Amazon and former World Bank economist; and Henrique Romero, a senior economist with Amazon’s Supply Chain Optimization Technologies division, addressed questions on why the problem of diversity within the economics field persists, what can be done, and how their life experiences have influenced their work as economists.

Why does the economics profession still struggle with diversity?

Muthoni Ngatia: The lack of diversity in economics becomes self-reinforcing when potential students don’t see themselves represented in the profession. It’s difficult to imagine yourself succeeding in a profession when you can’t find models in people with your lived experience.

There’s also an information barrier about what economics is, the type of work economists do, and how to prepare for a career in economics. Even in college, many of my classmates saw economics as a pathway to a career in investment banking or management consulting, which it certainly can be, however, there many more career opportunities available.

Jevay Grooms: There are many reasons. One that I found challenging, and almost prevented me from pursuing it, is very few people in the profession look like me. If you don't see anyone who looks like you in the profession, it could be difficult to imagine yourself in the domain. I also think there is a misconception about the scope of work economists do. As an undergraduate student, I didn't realize economists did work on social policies and disparities, the work that I do now.

Henrique Romero: The process of becoming an economist is long, arduous and rife with potential barriers to diversity. It takes a conscious effort to fight the inertia of the status quo at all steps along the way. For instance, economics departments mostly recruit talent from top PhD programs, which in turn tend to recruit PhD students from elite undergraduate institutions, which already suffer from a lack of representation. A forthcoming paper by Chetty et al. shows that at top-ranked universities, more students come from families in the top 1% than the bottom half of the income distribution.

What needs to happen to get individuals from more diverse backgrounds both interested in—and working—in economics?

Gerald E. Daniels Jr.: Outreach at every level of a student’s education is required to get more folks interested and working in economics. If a company, think tank, university, etc. has a sincere interest in diverse candidates, they should make a conscious effort to speak with and train the various candidates that they need. Therefore, if a company is searching for economists from underrepresented groups, create internships dedicated to universities that actively recruit and support these students. This approach can be extended to any diverse group. I would encourage anyone who needs help, to ask or hire folks like myself to support you.

Ngatia: More information about what economics is, the type of work economists do, and how to prepare for a career in economics. I think there’s a lot to be learned from programs working to get more diversity in STEM. Nurturing programs starting in high school and continuing into college can introduce a more diverse set of students to economics and how economists use data to understand and find potential solutions for social problems. Mentoring programs are also incredibly important to help students and economists throughout the pipeline advance.

Founders Library at Howard University on a sunny day
Founders Library at Howard University is seen on a sunny day. Howard recently announced it will host the AEASP “in support of increasing the pipeline of underrepresented minority economists.”
Oscar Merrida IV

Grooms: I think the onus needs to be on the entire profession. It cannot just be underrepresented groups pushing for more diversity. All economists need to acknowledge the importance of diverse backgrounds and a diversity of thought, and see it as a benefit and not a threat. Race disparities, racial inequality, systemic racism, and systems of oppression need not be taboo words but rather, words that we, as economists, acknowledge play a role in societal outcomes. For too long, economists have taken the "I don't see race" approach in research and this undermines the fight to address systemic racism.

Romero: I was the direct beneficiary of AEASP, which provides intensive training in microeconomics, math, econometrics, and research methods with the explicit goal of increasing racial and ethnic diversity. The program seems to be effective at increasing the likelihood of participants to apply to —and attend — a PhD program in economics, complete such programs, and work in an economics-related academic job (Becker et al., 2016). Although I will forever be indebted to this program, I must recognize that programs like this are difficult and costly to scale and can only be one part of a much broader solution. Bayer and Rouse (2016) provide an excellent overview of the state of diversity in the profession and discuss some promising initiatives. A full solution will likely require more equal access to education, from pre-k to PhD.

Why are diverse perspectives important to economics? How does the lack of them hinder economics?

Ngatia: Economic models require assumptions about how individuals and communities behave, those models are only as valid as they are representative of the diversity of human experience. The way we think about how humans behave necessarily depends on the humans we interact with, so a lack of diversity limits perspectives that could inform economic models. Advances in economics, or in any science really, are made by challenging existing modes of thinking. Having the same group of people in the profession limits the set problems they look at, and the tools they use.

Grooms: We have seen what a lack of diverse perspectives results in. To name a few: mass incarceration, the over-criminalization of crack cocaine relative to powdered cocaine, redlining, the overrepresentation of Black children in the foster care system, segregated schooling well after Brown v. Board of Education, and the Tuskegee experiment. Economists help shape social policy, and if we strive to be inclusive in our policymaking, we much also be inclusive in our research.

Daniels: If we allow ourselves to assume that policymakers and researchers prioritize topics that relate to their lived experience, then a lack of diversity inherently produces a lack of prioritization of research areas impacting those not represented. This is clear in the lack of research on a host of topics related to racial, gender, and LGBTQIA+ inequities. Diversity is our vehicle for producing innovative work, and a lack of diversity hinders our ability to innovate efficiently. In addition, innovating with a diverse group of people helps us to address issues that affect everyone.

How has your work as an economist been influenced by your life experiences?

Ngatia: Growing up in Kenya, I always knew I wanted to work in a capacity that would improve people’s livelihoods. When I came to college in the US, I wanted to pursue computer science. I was convinced (and still am) that the digital revolution would change the lives of many in Africa and I wanted to be a part of bringing that about. However, the first time I’d ever used a computer was when I was 16 and came to the US on an exchange program. My first semester in college, I felt completely out of my depth in my computer class.

Economics helped give me a framework to understand some of the development challenges facing Kenya and much of sub-Saharan Africa.
Muthoni Ngatia

My economics class was the complete opposite. Whereas my CS class seemed abstract, economics—with a focus on decision making with scarce resources—made more sense to me. Economics helped give me a framework to understand some of the development challenges facing Kenya and much of sub-Saharan Africa. Perhaps most importantly, I found an incredible mentor in Michael Kremer, one of my professors (and recent Nobel Laureate in Economics) who took a personal interest in me. Much of his research was in Kenya, and his work inspired me to see how economics could be a force for improving people’s lives.

Grooms: My work as an economist has always been motivated by racial and ethnic equality. I imagine a lot of this has to do with growing up a Black girl in a predominantly white society. As an undergraduate, I learned that economics could help solve — or bring to light — issues that impact vulnerable and historically oppressed communities, and I think that has been the underlining theme in my work today.

Romero: Having grown up in one of the most unequal countries in the world (Brazil), equity concerns are always salient to me. Luckily, this aligns very well with Amazon’s Customer Obsession and Earns Trust leadership principles. For instance, we will refrain from enacting changes that would improve the customer experiences of a group of customers at the cost of disproportionally degrading the experience of another group, even if the changes result in an overall improvement in short-term metrics.

In an effort to be a small part of the solution in increasing diversity, I have also devoted time to being involved in the hiring, recruiting, and development process at Amazon, from participating in over 150 interviews, becoming a Bar Raiser in Training, managing the Economist Mentoring program, to representing Amazon at university campus visits and diversity-focused conferences such as SACNAS.

Research areas

Related content

US, NY, New York
Are you a passionate Applied Scientist (AS) ready to shape the future of digital content creation? At Amazon, we're building Earth's most desired destination for creators to monetize their unique skills, inspire the next generation of customers, and help brands expand their reach. We build innovative products and experiences that drive growth for creators across Amazon's ecosystem. Our team owns the entire Creator product suite, ensuring a cohesive experience, optimizing compensation structures, and launching features that help creators achieve both monetary and non-monetary goals. Key job responsibilities As an AS on our team, you will: - Handle challenging problems that directly impact millions of creators and customers - Independently collect and analyze data - Develop and deliver scalable predictive models, using any necessary programming, machine learning, and statistical analysis software - Collaborate with other scientists, engineers, product managers, and business teams to creatively solve problems, measure and estimate risks, and constructively critique peer research - Consult with engineering teams to design data and modeling pipelines which successfully interface with new and existing software - Participate in design and implementation across teams to contribute to initiatives and develop optimal solutions that benefit the creators organization The successful candidate is a self-starter, comfortable with a dynamic, fast-paced environment, and able to think big while paying careful attention to detail. You have deep knowledge of an area/multiple areas of science, with a track record of applying this knowledge to deliver science solutions in a business setting and a demonstrated ability to operate at scale. You excel in a culture of invention and collaboration.
US, CA, San Francisco
Amazon has launched a new research lab in San Francisco to develop foundational capabilities for useful AI agents. We’re enabling practical AI to make our customers more productive, empowered, and fulfilled. In particular, our work combines large language models (LLMs) with reinforcement learning (RL) to solve reasoning, planning, and world modeling in both virtual and physical environments. Our research builds on that of Amazon’s broader AGI organization, which recently introduced Amazon Nova, a new generation of state-of-the-art foundation models (FMs). Key job responsibilities You will contribute directly to AI agent development in an engineering management role: leading a software development team focused on our internal platform for acquiring agentic experience at large scale. You will help set direction, align the team’s goals with the broader lab, mentor team members, recruit great people, and stay technically involved. You will be hired as a Member of Technical Staff. About the team Our lab is a small, talent-dense team with the resources and scale of Amazon. We’re entering an exciting new era where agents can redefine what AI makes possible. We’d love for you to join our lab and build it from the ground up!
GB, MLN, Edinburgh
Do you want a role with deep meaning and the ability to make a major impact? As part of Intelligent Talent Acquisition (ITA), you'll have the opportunity to reinvent the hiring process and deliver unprecedented scale, sophistication, and accuracy for Amazon Talent Acquisition operations. ITA is an industry-leading people science and technology organization made up of scientists, engineers, analysts, product professionals and more, all with the shared goal of connecting the right people to the right jobs in a way that is fair and precise. Last year we delivered over 6 million online candidate assessments, and helped Amazon deliver billions of packages around the world by making it possible to hire hundreds of thousands of workers in the right quantity, at the right location and at exactly the right time. You’ll work on state-of-the-art research, advanced software tools, new AI systems, and machine learning algorithms, leveraging Amazon's in-house tech stack to bring innovative solutions to life. Join ITA in using technologies to transform the hiring landscape and make a meaningful difference in people's lives. Together, we can solve the world's toughest hiring problems. Key job responsibilities As an Applied Scientist, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create technical roadmaps and drive production level projects that will support Amazon Science. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. About the team The Automated Performance Evaluation (APE) team is a hybrid team of Applied Scientists and Software Development Engineers who develop, deploy and own end-to-end machine learning services for use in the HR and Recruiting functions at Amazon.
US, CA, Sunnyvale
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video subscriptions such as Apple TV+, HBO Max, Peacock, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities As an Applied Scientist at Prime Video, you will have end-to-end ownership of the product, related research and experimentation, applying advanced machine learning techniques in computer vision (CV), Generative AI, multimedia understanding and so on. You’ll work on diverse projects that enhance Prime Video’s content localization, image/video understanding, and content personalization, driving impactful innovations for our global audience. Other responsibilities include: - Research and develop generative models for controllable synthesis across images, video, vector graphics, and multimedia - Innovate in advanced diffusion and flow-based methods (e.g., inverse flow matching, parameter efficient training, guided sampling, test-time adaptation) to improve efficiency, controllability, and scalability. - Advance visual grounding, depth and 3D estimation, segmentation, and matting for integration into pre-visualization, compositing, VFX, and post-production pipelines. - Design multimodal GenAI workflows including visual-language model tooling, structured prompt orchestration, agentic pipelines. A day in the life Prime Video is pioneering the use of Generative AI to empower the next generation of creatives. Our mission is to make world-class media creation accessible, scalable, and efficient. We are seeking an Applied Scientist to advance the state of the art in Generative AI and to deliver these innovations as production-ready systems at Amazon scale. Your work will give creators unprecedented freedom and control while driving new efficiencies across Prime Video’s global content and marketing pipelines. This is a newly formed team within Prime Video Science!
IN, KA, Bengaluru
Amazon Devices is an inventive research and development company that designs and engineer high-profile devices like the Kindle family of products, Fire Tablets, Fire TV, Health Wellness, Amazon Echo & Astro products. This is an exciting opportunity to join Amazon in developing state-of-the-art techniques that bring Gen AI on edge for our consumer products. We are looking for exceptional early career research scientists to join our Applied Science team and help develop the next generation of edge models, and optimize them while doing co-designed with custom ML HW based on a revolutionary architecture. Work hard. Have Fun. Make History. Key job responsibilities Key Job Responsibilities: • Understand and contribute to model compression techniques (quantization, pruning, distillation, etc.) while developing theoretical understanding of Information Theory and Deep Learning fundamentals • Work with senior researchers to optimize Gen AI models for edge platforms using Amazon's Neural Edge Engine • Study and apply first principles of Information Theory, Scientific Computing, and Non-Equilibrium Thermodynamics to model optimization problems • Assist in research projects involving custom Gen AI model development, aiming to improve SOTA under mentorship • Co-author research papers for top-tier conferences (NeurIPS, ICLR, MLSys) and present at internal research meetings • Collaborate with compiler engineers, Applied Scientists, and Hardware Architects while learning about production ML systems • Participate in reading groups and research discussions to build expertise in efficient AI and edge computing
US, NY, New York
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist to work on pre-training methodologies for Generative Artificial Intelligence (GenAI) models. You will interact closely with our customers and with the academic and research communities. Key job responsibilities Join us to work as an integral part of a team that has experience with GenAI models in this space. We work on these areas: - Scaling laws - Hardware-informed efficient model architecture, low-precision training - Optimization methods, learning objectives, curriculum design - Deep learning theories on efficient hyperparameter search and self-supervised learning - Learning objectives and reinforcement learning methods - Distributed training methods and solutions - AI-assisted research About the team The AGI team has a mission to push the envelope in GenAI with Large Language Models (LLMs) and multimodal systems, in order to provide the best-possible experience for our customers.
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As an Applied Scientist with the AGI team, you will work with talented peers to support the development of algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of GenAI technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in LLMs. About the team The AGI team has a mission to push the envelope in GenAI with LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
US, CA, Pasadena
The Amazon Web Services (AWS) Center for Quantum Computing in Pasadena, CA, is looking to hire a Principal Quantum Research Scientist. You will join a multi-disciplinary team of theoretical and experimental physicists, materials scientists, and hardware and software engineers working at the forefront of quantum computing. You should have a deep and broad knowledge of experimental quantum computing and a track record of original scientific contributions. We are looking for candidates with strong engineering principles, resourcefulness and a bias for action, superior problem solving, and excellent communication skills. Working effectively within a team environment is essential. As principal research scientist you will be expected to lead new ideas and stay abreast of the field of experimental quantum computation. Key job responsibilities Key job responsibilities In this role, you will work on improvements in all components of SC qubits quantum hardware, from qubits and resonators to quantum-limited amplifiers. You will also work on their integration into multiqubit chips. This will require designing new experiments, collecting statistically significant data through automation, analyzing the results, and summarizing conclusions in written form. Finally, you will work with hardware engineers, material scientists, and circuit designers to advance the state of the art of SC qubits hardware. About the team About the team Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS. Within AWS UC, Amazon Dedicated Cloud (ADC) roles engage with AWS customers who require specialized security solutions for their cloud services. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility.
US, CA, Palo Alto
We are looking for a passionate Applied Scientist to help pioneer the next generation of agentic AI applications for Amazon advertisers. In this role, you will design agentic architectures, develop tools and datasets, and contribute to building systems that can reason, plan, and act autonomously across complex advertiser workflows. You will work at the forefront of applied AI, developing methods for fine-tuning, reinforcement learning, and preference optimization, while helping create evaluation frameworks that ensure safety, reliability, and trust at scale. You will work backwards from the needs of advertisers—delivering customer-facing products that directly help them create, optimize, and grow their campaigns. Beyond building models, you will advance the agent ecosystem by experimenting with and applying core primitives such as tool orchestration, multi-step reasoning, and adaptive preference-driven behavior. This role requires working independently on ambiguous technical problems, collaborating closely with scientists, engineers, and product managers to bring innovative solutions into production. Key job responsibilities - Lead business, science and engineering strategy and roadmap for Sponsored Products Agentic Advertiser Guidance. - Design and build agents to guide advertisers in conversational and non-conversational experience. - Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization (e.g., DPO/IPO). - Curate datasets and tools for MCP. - Build evaluation pipelines for agent workflows, including automated benchmarks, multi-step reasoning tests, and safety guardrails. - Develop agentic architectures (e.g., CoT, ToT, ReAct) that integrate planning, tool use, and long-horizon reasoning. - Prototype and iterate on multi-agent orchestration frameworks and workflows. - Collaborate with peers across engineering and product to bring scientific innovations into production. - Stay current with the latest research in LLMs, RL, and agent-based AI, and translate findings into practical applications. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Advertiser Guidance team within Sponsored Products and Brands is focused on guiding and supporting 1.6MM advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in guidance systems can have outsized impact on advertiser success and Amazon’s retail ecosystem. Our vision is to build a highly personalized, context-aware agentic advertiser guidance system that leverages LLMs together with tools such as auction simulations, ML models, and optimization algorithms. This agentic framework, will operate across both chat and non-chat experiences in the ad console, scaling to natural language queries as well as proactively delivering guidance based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities—from high-level product guidance down to granular keyword recommendations—and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning, MCP, and preference optimization), ensuring our systems are both scalable and adaptive.
US, NY, New York
We are looking for a passionate Applied Scientist to help pioneer the next generation of agentic AI applications for Amazon advertisers. In this role, you will design agentic architectures, develop tools and datasets, and contribute to building systems that can reason, plan, and act autonomously across complex advertiser workflows. You will work at the forefront of applied AI, developing methods for fine-tuning, reinforcement learning, and preference optimization, while helping create evaluation frameworks that ensure safety, reliability, and trust at scale. You will work backwards from the needs of advertisers—delivering customer-facing products that directly help them create, optimize, and grow their campaigns. Beyond building models, you will advance the agent ecosystem by experimenting with and applying core primitives such as tool orchestration, multi-step reasoning, and adaptive preference-driven behavior. This role requires working independently on ambiguous technical problems, collaborating closely with scientists, engineers, and product managers to bring innovative solutions into production. Key job responsibilities - Design and build agents to guide advertisers in conversational and non-conversational experience. - Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization (e.g., DPO/IPO). - Curate datasets and tools for MCP. - Build evaluation pipelines for agent workflows, including automated benchmarks, multi-step reasoning tests, and safety guardrails. - Develop agentic architectures (e.g., CoT, ToT, ReAct) that integrate planning, tool use, and long-horizon reasoning. - Prototype and iterate on multi-agent orchestration frameworks and workflows. - Collaborate with peers across engineering and product to bring scientific innovations into production. - Stay current with the latest research in LLMs, RL, and agent-based AI, and translate findings into practical applications. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Advertiser Guidance team within Sponsored Products and Brands is focused on guiding and supporting 1.6MM advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in guidance systems can have outsized impact on advertiser success and Amazon’s retail ecosystem. Our vision is to build a highly personalized, context-aware agentic advertiser guidance system that leverages LLMs together with tools such as auction simulations, ML models, and optimization algorithms. This agentic framework, will operate across both chat and non-chat experiences in the ad console, scaling to natural language queries as well as proactively delivering guidance based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities—from high-level product guidance down to granular keyword recommendations—and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning, MCP, and preference optimization), ensuring our systems are both scalable and adaptive.