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Amazon Advertising call for proposals — Spring 2022

Breakthroughs in online advertising

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About this CFP

Amazon Ads helps grows businesses and brand of all sizes. This mission includes (1) managing advertising for long term growth, (2) measuring incrementality and spillover effects in marketing, (3) helping advertisers take action to leverage online assets, (4) behavioral economics approaches to online bidding, and (5) detection of bias, fraud and collusion in advertising. We welcome proposals related to these themes and offer the following examples of research questions within each theme:

Theme 1: Managing advertising for long term growth 

  • How do sellers measure their business growth and how does the marketing mix fit in?
  • How do advertisers evaluate currently available funnel metrics and how can they be improved?
  • How large is the interaction (synergy) between ads focused on different funnel stages?
  • How do proposed funnel metrics react to marketing and convert into sales?

Theme 2: Measuring incrementality and spillover effects in marketing 

  • How to measure the incremental, causal effect of ads on performance? 
  • How to combine randomized (experiment) with observational data?
  • How to scale causal Measurement at Scale?
  • How do actions taken in one campaign affect other campaigns?
  • What are the spillover and long-term effects of online ads, either within-online (eg online retailers and social media) or with offline?

Theme 3: Helping advertisers take action to leverage online assets

  • How to derive insights at scale from unstructured data?
  • Audio Ads and podcasts: how to shape advertising in audio?
  • When and for which consumers is online video more effective?
  • How does giving broader education on ad products increase advertiser performance?

Theme 4: Behavioral economics approaches to online bidding

  • How to improve allocation and pricing in bidding auctions recognizing bidders may not adhere to risk-adjusted utility?
  • Competitive dynamics: how do payoffs change when competing advertisers adopt advice?
  • Should different advertisers get different advice to reduce this competitive cancellation?
  • How do U.S. based findings hold up in international markets?

Theme 5: Detection of bias, fraud and collusion in advertising

  • How to mitigate bias/ensure fairness in algorithms and decision support
  • How to detect highly distributed fraud from relationships between customer accounts
  • How to detect suspicious activity masked behind genuine users and IPs
  • How to label for human/bot activity using semi-supervised and adversarial techniques

Timeline

Submission period: June 3 - July 8, 2022

Decision letters will be sent out December 2022

Award details

Selected Principal Investigators (PIs) may receive the following:

  • Unrestricted funds, no more than $50,000 USD on average
  • AWS Promotional Credits, no more than $20,000 USD on average
  • Training resources, including Amazon advertising tutorials and hands-on sessions with Amazon scientists and engineers

Awards are structured as one-year unrestricted gifts. The budget should include a list of expected costs specified in USD, and should not include administrative overhead costs. The final award amount will be determined by the awards panel.

Eligibility requirements

Please refer to the ARA Program rules on the FAQ page.

Proposal requirements

Proposals should be prepared according to the proposal template. In addition, to submit a proposal for this CFP, please also include the following information:

  1. Please list the open-source tools you plan to contribute to.
  2. Please list the AWS ML tools you plan to use and data you plan to obtain.

Selection criteria

ARA will make the funding decisions based on the potential impact to the research community, quality of the scientific content, and relevance for Amazon Advertising customers.

Expectations from recipients

To the extent deemed reasonable, Award recipients should acknowledge the support from ARA. Award recipients will inform ARA of publications, presentations, code and data releases, blogs/social media posts, and other speaking engagements referencing the results of the supported research or the Award. Award recipients are expected to provide updates and feedback to ARA via surveys or reports on the status of their research. Award recipients will have an opportunity to work with ARA on an informational statement about the awarded project that may be used to generate visibility for their institutions and ARA.

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Sr. Manager, Applied Science, Prime Video Personalization & Discovery
US, WA, Seattle
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 add-on subscriptions such as Apple TV+, Max, 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. Key job responsibilities As the Sr. Manager, Applied Science in the Prime Video Personalization and Discovery organization, you will be responsible for optimizing the complete customer experience, across the touch points throughout customers’ discovery journey. This includes building AI and optimization solutions, working with the business, product, engineering teams to deliver the optimal balance of customer delight and business outcomes. Responsibilities include direct management of senior engineers and scientists, setting vision and long-range technical strategy, product definition, roadmap planning, driving cross-functional execution, developing and maintaining experimentation and production services, owning ML and engineering excellence quality bar, and customer and stakeholder communication. Additionally, as our organization is growing, hiring top-notch engineers and scientists will be a key focus. About the team Prime Video Personalization and Discovery (PVPD) is dedicated to creating a highly personalized content discovery experience that not only delights our customers but also drives both short-and long-term business goals. Our scope includes personalized recommendations, search, marketing, and the advanced machine learning technology and infrastructure that underpins these experiences. Our mission is to automate and enhance customer engagement through personalization, using ML and Generative AI. To drive these efforts, Prime Video is seeking a visionary science leader to spearhead our investments in machine learning (ML) and artificial intelligence (AI) to reimagine the next-generation search experience on Prime Video. You will oversee a large team delivering against our ML strategy, overseeing the design of our ML stack, and ensuring the quality of our models. Your success in this role will depend on your deep expertise in search, personalization, discovery, AI/ML, Generative AI, and your passion for entertainment. This is a unique opportunity to influence the future of television for billions of viewers worldwide. As a center of excellence in machine learning, we are committed to leading the way in adopting and advancing cutting-edge technologies. We publish our research internally and externally, and this role will place you at the forefront of applying Generative AI at scale, using Amazon’s rich datasets. You will have a direct impact on shaping the future of entertainment, driving massive customer experience improvements, and achieving critical business KPIs.
Applied Scientist, Customer360
US, WA, Seattle
Are you a scientist interested in pushing the state of the art in Information Retrieval, NLP, Large Language Models and fine-tuning LLMs? Are you interested in innovating on behalf of millions of customers, helping them accomplish their every day goals? Do you wish you had access to large datasets and tremendous computational resources? Do you want to join a team of capable scientist and engineers, building the future of e-commerce? Answer yes to any of these questions, and you will be a great fit for our team at Amazon. Our team is part of Amazon’s Personalization organization, a high-performing group that leverages Amazon’s expertise in machine learning, generative AI, large-scale data systems, and user experience design to deliver the best shopping experiences for our customers. Our team builds large-scale machine-learning solutions that delight customers with personalized and up-to-date recommendations that are related to their interests. We are a team uniquely placed within Amazon, to have a direct window of opportunity to influence how customers will think about their shopping journey in the future. Key job responsibilities As an Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for personalization. You will adopt or invent new machine learning and analytical techniques in the realm of recommendations, information retrieval and large language models and fine-tuning models. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include inventing, experimenting with, and launching new features, products and systems. Please visit https://www.amazon.science for more information. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, execute experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the organization. You get to influence stakeholders with clear communication skills. You innovate on behalf of the customer and strategically build features. You will mentor junior members and help them grow. About the team The team values innovations and offers a safe place to try, fail and learn while fostering a culture of continuous improvement. Everyone is a leader and owner for everything we do as a team. Our team offers creative space with entrepreneurial work environment focusing on customer obsession.
Applied Scientist , Rufus Experience Science
US, CA, Palo Alto
Amazon Rufus Experience Science team is seeking a highly motivated Scientist who is passionate about building next-generation shopping experiences. In this role, you will help create conversational shopping journeys where customers can express any shopping need—discovering products, comparing options, finding inspiration, or resolving post-purchase issues. You will collaborate closely with a multidisciplinary team of scientists, engineers, product managers, and designers to deliver these experiences across multiple Rufus customer-facing features.
You will thrive in this role if you enjoy bringing cutting-edge research into everyday life—both for customers and for yourself. There’s nothing quite like realizing that a model you deployed yesterday is already improving your own shopping experience today. You will work side by side with scientists and engineers in a fast-paced environment, driving rapid model development and experimentation. You’ll also have access to Amazon’s rich datasets, AWS’s massive computational resources, and a network of world-class science and engineering leaders across the company. Key job responsibilities Execute the science vision and roadmap.

Develop data-driven solutions for the real-world, large scale problems.

Deliver and maintain software and models in the production environment.

Collaborate cross-functionally between product, design, and engineering.
Applied Scientist, Customer360
US, WA, Seattle
Are you a scientist who wants to define how AI remembers people, their loved ones, their unique preferences, and the moments that matter? Are you passionate about NLP, large language models, information retrieval, and entity understanding? Do you want to build systems that learn who the people in a customer's life are, what each of them cares about, and retrieve the right knowledge at the right moment? Do you want access to massive datasets, world-class compute, and the freedom to reason from first principles on novel problems? If any of this excites you, we'd love to talk. Our team is part of Amazon's Personalization organization, building the memory layer that powers how Amazon understands and personalizes for individual customers and their household members. We work at the intersection of NLP, LLMs, entity resolution, and retrieval — disaggregating preferences for each and every customer and their loved ones, and surfacing the most relevant knowledge to power experiences across Amazon that personalize more deeply than ever before. We are a central personalization team, partnering directly with organizations across Amazon to shape how personalization works at scale for years to come. Key job responsibilities As an Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for personalization. You will adopt or invent new machine learning and analytical techniques in the realm of recommendations, information retrieval and large language models. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include inventing, experimenting with, and launching new features, products and systems. Please visit https://www.amazon.science for more information.
Applied Scientist, AWS Automated Reasoning
US, WA, Seattle
Applied Scientists in AWS Automated Reasoning are dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for automated reasoning, privacy, and sovereignty. Key job responsibilities The successful candidate will: - Solve large or significantly complex problems that require deep knowledge and understanding of your domain and scientific innovation. - Own strategic problem solving, and take the lead on the design, implementation, and delivery for solutions that have a long-term quantifiable impact. - Provide cross-organizational technical influence, increasing productivity and effectiveness by sharing your deep knowledge and experience. - Develop strategic plans to identify fundamentally new solutions for business problems. - Assist in the career development of others, actively mentoring individuals and the community on advanced technical issues. A day in the life This is a unique and rare opportunity to get in early on a fast-growing segment of AWS and help shape the technology, product and the business. You will have a chance to utilize your deep technical experience within a fast moving, start-up environment and make a large business and customer impact. About the team Diverse Experiences Amazon Automated Reasoning 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. Why Amazon Automated Reasoning? At Amazon, automated reasoning is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for automated reasoning across all of Amazon's products and services. We offer talented automated reasoning professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Automated Reasoning, it's in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest automated reasoning challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & 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, training, 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve.
Applied Scientist I, Amazon Shipping
IN, HR, Gurugram
Building large-scale forecasting and optimization systems that power Amazon’s global transportation network and directly impact customer experience and cost. Key job responsibilities 1. Guide model and system design across a range of techniques, including tree-based models, deep learning (LSTMs, transformers), LLMs, and reinforcement learning. 2. Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. 3. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions. 4 Own end-to-end business metrics, directly influencing customer experience, cost optimization, and network reliability. 5. Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing.
Economist, Pricing Science
US, WA, Seattle
Estimating the demand response of a pricing decision is genuinely hard. The causal effects are delayed, noisy, and confounded by factors that standard experiment analysis wasn't designed to handle. Most pricing teams default to heuristics not because they don't care about customer responses, but because measuring them rigorously is an unsolved problem. P2OS is building the science to solve it. We're hiring an Economist to own that work — defining how we estimate digital demand response in a pricing context, building the identification strategies that make those estimates credible, and translating outputs into something pricing teams can use to make better decisions. The role sits at the intersection of econometric methodology and production-quality analysis, and requires someone who can operate independently in both. As science lead, you'll own the digital pricing methodology domain, and be the internal authority on causal inference for pricing across P2OS and partner teams. Key job responsibilities * Own the end-to-end digital pricing methodology for pricing — identification strategy, modeling choices, validation approach, and business use cases — and drive adoption across pricing contexts * Deliver high-stakes analyses connecting digital pricing estimates to a concrete pricing decision and strategy change at VP+ level * Apply advanced causal methods to live pricing problems; document approaches so the team can build on and extend them. * Provide causal inference guidance on pricing experiment questions as they arise — being the methodology resource when experiments generate relevant questions * Serve as cross-team economic advisor to Digital Finance, Customer Behavior, and Demand Science on assumptions and causal identification * Actively mentor junior scientists, earn trust of cross-functional tech and product partners. A day in the life In a typical day, you'll move between methodology work and stakeholder-facing analysis. - On the science side, that means reviewing identification assumptions with the Causal AS, validating estimation choices for the LTV framework, and documenting methodology decisions in ways that non-economists can act on. - On the applied side, you'll be in rooms with Finance, Pricing PMs, and other science teams: aligning on LTV definitions, resolving disagreements between competing metrics, and translating causal findings into recommendations that land in strategy reviews. - As tech lead, you need to work to develop the economists and scientists on your scrum: structured reviews, identification strategy feedback, and raising the quality of analyses before they reach stakeholders. The mix shifts, but the through-line is to progress the LTV methodology from open questions to shipped frameworks, and making sure the team's causal work is rigorous enough to hold up when it counts. About the team P2Optimization Science (P2OS) is responsible for the ML models and analytical frameworks that drive pricing decisions at scale. The team spans demand lift modeling, pricing error detection, customer lifetime value, and experimentation. Our small team of specialized applied scientists and economists works closely alongside engineers, and pricing product managers.
Applied Scientist II, Amazon, Amazon
US, WA, Seattle
We’re working to improve shopping on Amazon using the conversational capabilities of large language models, and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You'll be working with talented scientists, engineers, and technical program managers (TPM) to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey!
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