A city crew truck is seen driving down a flooded street in a downpour
Lise St. Denis, a research scientist at the University of Colorado’s Earth Lab, has spent the past half-decade figuring out how to find useful information on social media in the wake of natural disasters like the flooding seen here.
Mario Beauregard/Adobe

Finding critical information during disasters

Lise St. Denis, a research scientist at the University of Colorado, says social media can be useful for responders. Now she's helping them separate truly useful info from the noise.

Twitter, apart from being a place to catch up on niche topics and post personal takes on the latest news, can be a useful source of vital information during disasters.

Lise St. Denis, a research scientist at the University of Colorado’s Earth Lab, notes social media sites of all stripes can be useful in storms, but also in wildfires, floods, hurricanes, and other natural disasters — because fast, local information is essential in these situations. However, separating truly useful info from the noise is key, which is what St. Denis has been working on for the past half-decade.

“My big vision is that emergency response teams and communities impacted by disasters could get the best possible information out in real time so communities can be optimally informed about what's happening,” she says.

This kind of work requires a marriage of creative thinking and technology, something St. Denis, a 2019 AWS Machine Learning Research Award recipient, has pursued since the beginning of her career.

Lise St. Denis is seen wearing a mask and standing, on the left, while teaching a recent graduate seminar. There is a display screen behind her and two students, also masked, are seen sitting.
Lise St. Denis is seen standing while teaching a recent graduate seminar. After earning her PhD at the University of Colorado in 2016, St. Denis stayed on and is now a research scientist at Earth Lab
Courtesy of Lise St. Denis

At least as far back as college, St. Denis has had a variety of interests she took seriously, despite their seeming disparity. Her undergrad degrees from Colorado State University are in fine arts and computer science. That brought her to illustration and software engineering in her early working life, first at Hewlett Packard. HP supported her graduate work in human factors engineering at the University of Idaho.

She took a break when she had children in the early 2000s, and when she was ready to return to the workforce, she realized she wanted to refine her skills. “I still had a lot of the same interests, but with a different life perspective — I was older. I wanted to do something that I felt like I was making a difference,” says St. Denis. So she went back to graduate school in 2011 initially for a masters in computer science, which led her to the University of Colorado where she discovered Project EPIC (Empowering the Public with Information in Crisis) where she decided to pursue an interdisciplinary doctorate in crisis informatics.

As part of the work for her degree, she met a group of emergency responders, became fascinated by their work, and set out to learn more. She realized that one big challenge they faced was getting the word out to the public. Could, she wondered, social media sites help gather and distribute information?

So when she heard about a plan in New Mexico to adapt the idea of digital volunteerism to emergency risk response — the volunteers in this case would be emergency responders — she went to learn from them.

At the time, social media wasn’t widely embraced within the emergency response field; St. Denis even knew government officials who risked their jobs using social media at work. “A lot of emergency response organizations just saw social media, not as useful, but as more of a hotbed for misinformation and rumor,” says St. Denis.

Even in light of that, some emergency managers remained interested: “As social media gained popularity, they knew this is where they needed to provide updates, engage with a growing audience, and look for breaking information,” recalls St. Denis.

“They formed this network of teams that were called Virtual Operational Support Teams. These teams are known ahead of time and activated through formal emergency protocols and procedures. The first emergency trial of the concept was during the 2011 Shadow Lake Fire in Eastern Oregon. I ended up studying the innovations of this network of teams, and I worked within this community, alongside them, to understand what they were doing,” she explained.

Their work made sense to St. Denis, and so, instead of getting that master’s in computer science, she ended up using what she had learned in New Mexico as a basis for her cross-disciplinary PhD, which included computer science, but also incorporated classes in communication and sociology of disaster.

In 2014, St. Denis was asked to bring her reporting and analysis social media skills to the Carlton Complex fire in Eastern Washington. That fire burned through several communities with a high number of structures lost and very short evacuation windows. Unable to keep up with the speed of the fire’s impact, locals had no way to get their questions answered and there was, understandably, a lot of frustration.

“That convinced me that there had to be a better strategy for filtering and getting to the most relevant information needed during these events,” she says.

She was also wrangling data and doing analysis, and consolidating that information for the teams she was supporting. As part of her research, St. Denis was a part of close to 100 emergency response activations. “I studied the integration of social media into emergency response through virtual teams,” she explains. “And I kept asking myself, ‘What does it mean to integrate them?’”

Fast forward to today and she’s still researching that basic question. After earning her PhD at the University of Colorado in 2016, St. Denis stayed on at the university and is now a research scientist at Earth Lab. “We have all this existing information from all these different sources, and we want to do a better job of making it available so scientists can leverage it and make use of it for hazards analysis.”

Thus far, Twitter has shown the most promise for what St. Denis hopes to implement. The idea is that an emergency manager would receive a live stream of truly useful content, including selected tweets from reliable sources. “The managers could keep an eye on that as part of their emergency management response,” says St. Denis.

This is extremely practical, real-world information, that can help save lives because it is personalized, says St. Denis. The information is coming from community members who are directly impacted by these disasters. “It's not the media coverage or the broad outside information,” says St. Denis. “It contains new information such as what roads are passable or where fuel outages exist” or where information gaps exist such as, ‘I don't know where to evacuate my livestock,’ or ‘I need to know who has gas,’ or ‘Is my water supply safe to drink?’”

And while her research hasn’t yet translated into an actual tool for emergencies, St. Denis sees the light around the corner. She recently became part of the Pandemic Hyper-Accelerator for Science and Technology (PHAST). “As part of the PHAST program I have been paired with skilled entrepreneurs who are helping me to look at my problem from a systematic, opportunity-driven perspective,” she explains. “We’ve been interviewing emergency response and crisis response professionals across different contexts to understand specifics about the tools they are using, as well as the specific values of or consequences for information when it is found or not found.”

Utilizing machine learning

St. Denis first realized she would need to utilize machine learning when studying data from the Carlton Complex fire. “I realized that I had some intuition for how I could take the noise off the top to get to the information that I wanted. But the only way that was going to matter is if I could do that in near real time — which would require machine learning,” she says. So she applied for an AWS Machine Learning Research Award and received it in 2019.

She and her team used AWS Lambda and AWS Fargate to query the Twitter API for relevant tweets, and stored the raw data in Amazon S3. St. Denis also used standard machine learning libraries to build her prototype because she wanted everything to be open source. “We're hoping, as we move forward, to move into more sophisticated data collection and AWS tools,” she says.

St. Denis and her team have published two papers on the design of the work done so far, and proved that the prototype they’ve built works equally well across multiple types of hazards. They’ve even used it for work they did examining US-based public response to stay-at-home orders at the onset of the COVID-19 pandemic.

“I have spent over a decade working with some of the most innovative responders in the field, but fundamentally nothing has changed in terms of tools,” she says. “I think that this social media-based tool has a lot of potential, and so it's been really exciting. Now that I have this starter funding, it could go pretty quickly.”

Related content

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). Our lab is a small, talent-dense team with the resources and scale of Amazon. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. 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! Key job responsibilities You will contribute directly to AI agent development in an applied research role, including model training, dataset design, and pre- and post-training optimization. You will be hired as a Member of Technical Staff.
US, WA, Seattle
As part of the AWS Solutions organization, we have a vision to provide business applications, leveraging Amazon’s unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers’ businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon’s real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. The Team Just Walk Out (JWO) is a new kind of store with no lines and no checkout—you just grab and go! Customers simply use the Amazon Go app to enter the store, take what they want from our selection of fresh, delicious meals and grocery essentials, and go! Our checkout-free shopping experience is made possible by our Just Walk Out Technology, which automatically detects when products are taken from or returned to the shelves and keeps track of them in a virtual cart. When you’re done shopping, you can just leave the store. Shortly after, we’ll charge your account and send you a receipt. Check it out at amazon.com/go. Designed and custom-built by Amazonians, our Just Walk Out Technology uses a variety of technologies including computer vision, sensor fusion, and advanced machine learning. Innovation is part of our DNA! Our goal is to be Earths’ most customer centric company and we are just getting started. We need people who want to join an ambitious program that continues to push the state of the art in computer vision, machine learning, distributed systems and hardware design. Key job responsibilities Everyone on the team needs to be entrepreneurial, wear many hats and work in a highly collaborative environment that’s more startup than big company. We’ll need to tackle problems that span a variety of domains: computer vision, image recognition, machine learning, real-time and distributed systems. As an Applied Scientist, you will help solve a variety of technical challenges and mentor other scientists. You will tackle challenging, novel situations every day and given the size of this initiative, you’ll have the opportunity to work with multiple technical teams at Amazon in different locations. You should be comfortable with a degree of ambiguity that’s higher than most projects and relish the idea of solving problems that, frankly, haven’t been solved at scale before - anywhere. Along the way, we guarantee that you’ll learn a ton, have fun and make a positive impact on millions of people. A key focus of this role will be developing and implementing advanced visual reasoning systems that can understand complex spatial relationships and object interactions in real-time. You'll work on designing autonomous AI agents that can make intelligent decisions based on visual inputs, understand customer behavior patterns, and adapt to dynamic retail environments. This includes developing systems that can perform complex scene understanding, reason about object permanence, and predict customer intentions through visual cues. About the team AWS Solutions As part of the AWS solutions organization, we have a vision to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. we blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred 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 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. 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. 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.
US, MA, Boston
We're a new research lab based in San Francisco and Boston focused on developing foundational capabilities for useful AI agents. We're pursuing several key research bets that will enable AI agents to perform real-world actions, learn from human feedback, self-course-correct, and infer human goals. We're particularly excited about combining large language models (LLMs) with reinforcement learning (RL) to solve reasoning and planning, learned world models, and generalizing agents to physical environments. We're a small, talent-dense team with the resources and scale of Amazon. Each team has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. AI agents are the next frontier—the right research bets can reinvent what's possible. Join us and help build this lab from the ground up. Key job responsibilities * Define the product vision and roadmap for our agentic developer platform, translating research into products developers love * Partner deeply with research and engineering to identify which capabilities are ready for productization and shape how they're exposed to customers * Own the developer experience end-to-end from API design and SDK ergonomics to documentation, sample apps, and onboarding flows * Understand our customers deeply by engaging directly with developers and end-users, synthesizing feedback, and using data to drive prioritization * Shape how the world builds AI agents by defining new primitives, patterns, and best practices for agentic applications About the team Our team brings the AGI Lab's agent capabilities to customers. We build accessible, usable products: interfaces, frameworks, and solutions, that turn our platform and model capabilities into AI agents developers can use. We own the Nova Act agent playground, Nova Act IDE extension, Nova Act SDK, Nova Act AWS Console, reference architectures, sample applications, and more.
CA, ON, Toronto
The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art 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 Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond! Key job responsibilities This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: - Contribute to the design and development of GenAI, deep learning, multi-objective optimization and/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and/or bespoke shopping experiences. - Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life. - Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization. - Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond!
US, MA, N.reading
Amazon Industrial Robotics is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of dexterous manipulation system that: - Enables unprecedented generalization across diverse tasks - Enables contact-rich manipulation in different environments - Seamlessly integrates low-level skills and high-level behaviors - Leverage mechanical intelligence, multi-modal sensor feedback and advanced control techniques. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Design and implement methods for dexterous manipulation - Design and implement methods for use of dexterous end effectors with force and tactile sensing - Develop a hierarchical system that combines low-level control with high-level planning - Utilize state-of-the-art manipulation models and optimal control techniques
US, MA, Boston
AI is the most transformational technology of our time, capable of tackling some of humanity’s most challenging problems. That is why Amazon is investing in generative AI (GenAI) and the responsible development and deployment of large language models (LLMs) across all of our businesses. Come build the future of human-technology interaction with us. We are looking for a Research Scientist with strong technical skills which includes coding and natural language processing experience in dataset construction, training and evaluating models, and automatic processing of large datasets. You will play a critical role in driving innovation and advancing the state-of-the-art in natural language processing and machine learning. You will work closely with cross-functional teams, including product managers, language engineers, and other scientists. Key job responsibilities Specifically, the Research Scientist will: • Ensure quality of speech/language/other data throughout all stages of acquisition and processing, including data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc. • Clean, analyze and select speech/language/other data to achieve goals • Build and test models that elevate the customer experience • Collaborate with colleagues from science, engineering and business backgrounds • Present proposals and results in a clear manner backed by data and coupled with actionable conclusions • Work with engineers to develop efficient data querying infrastructure for both offline and online use cases
US, MA, Boston
AI is the most transformational technology of our time, capable of tackling some of humanity’s most challenging problems. That is why Amazon is investing in generative AI (GenAI) and the responsible development and deployment of large language models (LLMs) across all of our businesses. Come build the future of human-technology interaction with us. We are looking for a Research Scientist with strong technical skills which includes coding and natural language processing experience in dataset construction, training and evaluating models, and automatic processing of large datasets. You will play a critical role in driving innovation and advancing the state-of-the-art in natural language processing and machine learning. You will work closely with cross-functional teams, including product managers, language engineers, and other scientists. Key job responsibilities Specifically, the Research Scientist will: • Ensure quality of speech/language/other data throughout all stages of acquisition and processing, including data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc. • Clean, analyze and select speech/language/other data to achieve goals • Build and test models that elevate the customer experience • Collaborate with colleagues from science, engineering and business backgrounds • Present proposals and results in a clear manner backed by data and coupled with actionable conclusions • Work with engineers to develop efficient data querying infrastructure for both offline and online use cases
US, WA, Bellevue
This is currently a 12 month temporary contract opportunity with the possibility to extend to 24 months based on business needs. The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, WA, Bellevue
This is currently a 12 month temporary contract opportunity with the possibility to extend to 24 months based on business needs. The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, WA, Seattle
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. We are seeking a highly skilled and analytical Research Scientist. You will play an integral part in the measurement and optimization of Amazon Music marketing activities. You will have the opportunity to work with a rich marketing dataset together with the marketing managers. This role will focus on developing and implementing causal models and randomized controlled trials to assess marketing effectiveness and inform strategic decision-making. This role is suitable for candidates with strong background in causal inference, statistical analysis, and data-driven problem-solving, with the ability to translate complex data into actionable insights. As a key member of our team, you will work closely with cross-functional partners to optimize marketing strategies and drive business growth. Key job responsibilities Develop Causal Models Design, build, and validate causal models to evaluate the impact of marketing campaigns and initiatives. Leverage advanced statistical methods to identify and quantify causal relationships. Conduct Randomized Controlled Trials Design and implement randomized controlled trials (RCTs) to rigorously test the effectiveness of marketing strategies. Ensure robust experimental design and proper execution to derive credible insights. Statistical Analysis and Inference Perform complex statistical analyses to interpret data from experiments and observational studies. Use statistical software and programming languages to analyze large datasets and extract meaningful patterns. Data-Driven Decision Making Collaborate with marketing teams to provide data-driven recommendations that enhance campaign performance and ROI. Present findings and insights to stakeholders in a clear and actionable manner. Collaborative Problem Solving Work closely with cross-functional teams, including marketing, product, and engineering, to identify key business questions and develop analytical solutions. Foster a culture of data-informed decision-making across the organization. Stay Current with Industry Trends Keep abreast of the latest developments in data science, causal inference, and marketing analytics. Apply new methodologies and technologies to improve the accuracy and efficiency of marketing measurement. Documentation and Reporting Maintain comprehensive documentation of models, experiments, and analytical processes. Prepare reports and presentations that effectively communicate complex analyses to non-technical audiences.